NISTIR 7865 

A Human Factors Guide to 

Enhance EHR Usability of Critical 

User Interactions when Supporting 

Pediatric Patient Care 

Svetlana Z. Lowry 

Matthew T. Quinn 

Mala Ramaiah 

David Brick 

Emily S. Patterson 

Jiajie Zhang 

Patricia Abbott 

Michael C. Gibbons 

http://dx.doi.org/10.6028/NIST.IR.7865 
 

 

NISTIR 7865 

A Human Factors Guide to 

Enhance EHR Usability of Critical 

User Interactions when Supporting 

Pediatric Patient Care 

Svetlana Z. Lowry 

Matthew T. Quinn 

Mala Ramaiah 

National Institute of Standards and Technology 

David Brick 

NYU Langone Medical Center 

Emily S. Patterson 

Ohio State University 

Jiajie Zhang 

University of Texas – Houston 

Patricia Abbott 

Johns Hopkins University 

Michael C. Gibbons 

Johns Hopkins University 

http://dx.doi.org/10.6028/NIST.IR.7865 

June 2012 

U.S. Department of Commerce 

Rebecca Blank, Acting Secretary 

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director 
                      

                

                      

                  

                  

                      

                 

            

          

            

             

           

                    

                

                

                      

           

                     

                  

                      

                         

            

              

                

                    

              

                    

                    

                 

              

                    

                    

  

Acknowledgments 

The authors gratefully acknowledge the intellectual contributions from the following reviewers:

Anne Bobb, R.Ph., Children's Memorial Hospital, Chicago, IL

Joseph Cafazzo, Ph.D., P.Eng., Healthcare Human Factors, University Health Network, Toronto

Willa Drummond, M.D., University of Florida College of Medicine

Daniel Essin, M.A., M.D., USC Keck School of Medicine

Scott Finley, M.D., M.P.H., Westat and VHA, Office of Health Information

Anne B. Francis, M.D., Elmwood Pediatric Group, Rochester, NY

Ayse Gurses, Ph.D., Johns Hopkins University

Marta Hernanz‐Schulman, M.D., Vanderbilt Children's Hospital

Kevin Jones, M.S., Ohio State University

Dean Karavite, M.S., Children’s Hospital of Philadelphia

Ben‐Tzion Karsh, Ph.D., University of Wisconsin

Yiannis L. Katsogridakis, M.D., M.P.H., Children's Memorial Hospital, Chicago, IL

Rainu Kaushal, M.D., M.P.H., Weill Cornell Medical College

George Kim, M.D., Johns Hopkins University Children’s Center

Nancy F. Krebs, M.D., M.S., University of Colorado School of Medicine

David Kreda, Social Research Corporation, PA

Andrew Kroger, M.D., M.P.H., National Center for Immunization and Respiratory Diseases

Herschel R. Lessin M.D., The Children's Medical Group, Poughkeepsie, NY

Eugenia Marcus, M.D., Pediatric Health Care at Newton Wellesley, Newton, MA

Colleen McLaughlin, M.P.H., Ph.D., Patient Safety Center, New York State Dept. of Health

Deepa Menon, M.D., Johns Hopkins University

Mary Patterson, M.D., Med, Akron Children’s Hospital

Sean Petty, R.N., Jacobi Medical Center, Bronx, NY

Debora Simmons, Ph.D., R.N., CCNS, St. Luke’s Episcopal Health System

Dean Sittig, Ph.D., University of Texas Houston

Arthur Smerling, M.D., Columbia University College of Physicians and Surgeons

S. Andrew Spooner, M.D., M.S., Cincinnati Children's Hospital Medical Center

Jennifer Stinson, R.N., Ph.D., Hospital for Sick Children, Toronto

Susan Torrey, M.D., NYU Langone Medical Center

Michael S. Victoroff, M.D., University of Colorado School of Medicine

Roberts Wears, M.D., Ph.D., University of Florida Health Center Jacksonville

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DISCLAIMER 

Certain commercial entities, equipment, or material may be identified in this document in

order to describe a concept adequately. Such identification is not intended to imply

recommendation or endorsement by the National Institute of Standards and Technology,

nor is it intended to imply that these entities, materials, or equipment are necessarily the

best available for the purpose.

2
 
Table of Contents
 

Acknowledgments ..................................................................................................................... 1
 

Table of Contents....................................................................................................................... 3
 

Executive Summary................................................................................................................... 4
 

1 Background: Usability and critical user interactions............................................... 5
 

2 Special considerations for pediatric patients............................................................. 8
 

3 A conceptual model of unique use-related risks of EHR systems for pediatric 

patients ....................................................................................................................................... 12
 

4 Human factors guidance for critical user interactions with the EHR for 

pediatric patients ..................................................................................................................... 14
 

5 Opportunities for innovation ......................................................................................... 35
 

6 Conclusion ......................................................................................................................... 37
 

Appendix A Representative clinical scenarios ........................................................... 38
 

3
 
Executive Summary 

Adoption of electronic health record (EHR) systems in hospitals and physician practices is 

accelerating. Usability of EHRs has been identified as an important factor impacting patient 

safety, and recommendations for improvement have been provided. Pediatric patients have 

unique characteristics that translate into unique EHR usability challenges. It is not surprising, 

then, that the adoption of EHRs by pediatric care providers has lagged behind adoption for adult 

care providers. In this document, we highlight important user interactions that are especially 

salient for pediatric care and hence to the EHR user-centered design process. These 

interactions and associated usability recommendations were identified by consensus during a 

series of teleconferences with experts representing the disciplines of human factors 

engineering, usability, informatics, and pediatrics in ambulatory care and pediatric intensive 

care. In addition, extensive peer review was provided by experts in pediatric informatics, 

emergency medicine, neonatology, pediatrics, human factors engineering, usability engineering, 

and software development and implementation. 

This report details recommendations to enhance EHR usability when supporting pediatric 

patient care and also identifies promising areas for EHR innovation. Finally, we illustrate unique 

pediatric considerations in the context of representative clinical scenarios which may be helpful 

for formative user-centered design approaches and summative usability evaluations. 

4
 
                                                            

1 Background: Usability and critical user interactions 

Adoption of electronic health record (EHR) systems in hospitals and physician practices is 

accelerating.1

 At the same time, however, the lack of usability of EHRs has been identified as an 

important factor impacting patient safety,2

 and national guidelines have been released to 

evaluate, test, validate,3

 and document summative usability testing results.4 In addition, 

recommendations have been made to improve the usefulness,5,6,7,8 interoperability,9

 and ability 

to conduct research10 of EHRs for pediatric patients. 

Pediatric patients have unique characteristics that translate into higher complexity for providing 

care with both paper-based charts and EHRs. As such, EHRs have the potential to reduce 

complexity with advanced decision support features, and thus improve patient safety. Meeting 

this potential will likely require a specialized assessment of the unique challenges in providing 

pediatric care with EHRs, and in particular, unique usability issues associated with critical user 

interactions.11,12,13 It is not surprising, then, that the adoption of EHRs by pediatric providers has 

lagged behind adoption for general population providers.14 In this document, we highlight user 

interactions that are unique to or especially salient for pediatric care. As such, these interactions 

impact EHR usability in particular and user-centered design (UCD) in general. 

1

 Jha, A.K., Desroches, C.M., Campbell, E.G. et al., “Use of electronic health records in U.S. hospitals,” 

The New England Journal of Medicine, 2009;360(16) :1628–1638. 2

 IOM. Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC: The 

National Academies Press; 2012. 

3

 (NISTIR 7804) Technical Evaluation, Testing and Validation of the Usability of Electronic Health 

Records
 4

 (NISTIR 7742) Customized Common Industry Format Template for Electronic Health Record Usability 

Testing.

5

 Spooner, A. and Council on Clinical Information Technology. Special Requirements of Electronic Health 

Record Systems in Pediatrics. Pediatrics. 2007;119(3): 631-637. 6

 Shiffman et al. Information Technology for Children's Health and Health Care: Report on the Information 

Technology in Children's Health Care Expert Meeting, September 21-22, 2000. J Am Med Inform Assoc 

2001;8:546-551. 

7

 Grace, E., Kahn, J., Finley, S. Model Children’s EHR Format. HIMSS 2011 Annual Conference. 

February 23, 2011. 

8

 United States Pharmacopoeia. Error-Avoidance Recommendations for Medications Used in Pediatric 

Populations. Available at: http://www.usp.org/hqi/patientSafety/resources/pedRecommnds2003-01-

22.html (Accessed March 22, 2011).

9

 Hinman, A.R., Davidson, A.J. Linking Children's Health Information Systems: Clinical Care, Public 

Health, Emergency Medical Systems, and Schools. Pediatrics 123 Supplement 2 January 1, 2009; S67 -

S73. 

10 Stiles, P.G., Boothroyd, R.A., Robst, J., Ray, J.V. Ethically Using Administrative Data in Research 

Medicaid Administrators’ Current Practices and Best Practice Recommendations. Administration & 

Society March 2011:43(2):171-192.

11 Scanlon, M. C. Human factors and ergonomics in pediatrics. In: Carayon P, ed. Handbook of Human 

Factors and Ergonomics in Patient Safety: Lawrence Erlaum and Associates; 2006.

12 Scanlon, M.C., Karsh, B., Densmore E. Human Factors and Pediatric Patient Safety. Pediatric Clinics 

of North America. 2006;53:1105-19.

13 Spooner, A. and Council on Clinical Information Technology. Special Requirements of Electronic Health 

Record Systems in Pediatrics. Pediatrics. 2007:119(3):631-637. 14 Nakamura, M.M., Ferris, T.G., DesRoches, C.M., Jha, A.K. Electronic health record adoption by 

children's hospitals in the United States. Arch Pediatr Adolesc Med. 2010 Dec;164(12):1145-51.
 

5
 
                                                            

User-centered design (UCD) is an approach to designing systems; the approach is informed by 

scientific knowledge of how people think, act, and coordinate to accomplish their goals.15 UCD 

design practices employ both formative and summative practices in order to achieve systematic 

discovery of useful functions grounded in an understanding of the work domain. Particularly for 

systems used in high-risk environments, where mistakes can result in fatalities, ensuring system 

usability is an important objective. Usability has traditionally been defined as “The extent to 

which a product can be used by specified users to achieve specified goals with effectiveness, 

efficiency and satisfaction in a specified context of use.”16 In healthcare settings implementing 

EHRs, an emerging consensus is that many of the critical risks for the care of pediatric patients 

associated with the use of the EHR are related not just to the system’s user interfaces, but also 

to the system’s functionality and workflow. Therefore, for the purposes of this document, we use 

a unified framework for defining EHR usability: “how useful, easy to use, and satisfying a system 

is for the intended users to accomplish goals in the work domain by performing certain 

sequences of tasks” (page 1).17 

The focus of this document is not on all aspects of EHR usability, but rather on those that are 

part of critical user interactions. Critical user interactions are interactions between a user, such 

as a physician, nurse, pharmacist, caregiver, or patient, and the EHR, which can potentially lead 

to errors, workarounds, or adverse events that are associated with patient harm. Critical does 

not imply level of clinical care, such as critical care, but rather the highest-priority interactions to 

consider with respect to usability and patient safety in the context of his or her associated 

pediatric care. In other words, these are safety-critical interactions with the EHR. In safetycritical environments (hospitals, emergency departments, etc.), the importance of well-designed, 

usable interfaces is increased precisely because of the potential for catastrophic outcomes. The 

importance is further increased in the presence of time pressure,18 as is the case in much of 

healthcare. Time pressure reduces a user’s opportunity to detect signals in the face of noise 

and may also lead to inadvertent confirmation bias, so appropriate user interface design is all 

the more important in such environments. 

Several usability-related concerns are not addressed in this report and considered outside the 

scope of this document. These include challenges associated with supporting collaborative work 

and shared situation awareness among interdisciplinary team members, transitions across care 

settings, interoperability between systems, integration with bar code point of care and other 

medical devices, quality improvement and research using data pulled from EHRs, integration 

with social media and handheld devices, and software designed exclusively for use by 

caregivers or nontraditional healthcare providers. The user interactions and associated 

recommendations described in this report were identified by consensus during a series of 

teleconferences. Participating experts represented the disciplines of human factors engineering, 

15 Flach, J.M., Dominguez, C.O. Use-centered design: Integrating the user, instrument, and goal. 

Ergonomics in Design: The Quarterly of Human Factors Applications, July 1995;3(3):19-24. 

16 ISO/IEC. 9241-14 Ergonomic requirements for office work with visual display terminals (VDTs) - Part 14 

Menu dialogues, ISO/IEC 9241-14: 1998 (E), 1998. 

17 Zhang, J., Walji, M. TURF: Toward a unified framework of EHR usability. Journal of Biomedical
 

Informatics, 2011: 44 (6):1056-1067. 

18 Hancock, P.A., Szalma, J.L. Operator stress & display design. Ergonomics in Design. 2003;11(2):13-8.
 

6
 
usability, informatics, and pediatrics in ambulatory care and pediatric intensive care. In addition, 

extensive peer review was provided by experts in pediatric informatics, emergency medicine, 

neonatology, pediatrics, human factors engineering, usability engineering, and software 

development and implementation. 

The notion of critical user interactions takes on special importance with pediatric patients. As is 

explained in the next section, pediatric patients are unique. Their uniqueness creates at least 

two important consequences with respect to EHR usability. First, the young and very young 

pediatric patients may be more physiologically vulnerable to even small mistakes or care delays. 

Second, pediatric patients have unique care challenges, which can create additional physical 

and/or mental demands on pediatric clinicians. Both of those observations reveal that user 

interactions with EHRs that might not be deemed critical in other environments become critical 

with pediatric patients. The unique pediatric factors are explained next. 

7
 
 

                                                            

2 Special considerations for pediatric patients 

General Pediatric Considerations 

Pediatric patients have been identified as a high-priority, high-risk population for patient safety 

due to differences in physical characteristics, developmental issues, and issues relating to the 

legal status of minor age children and complicated custody and guardianship situations.19 Even 

within pediatric patients, there is much variability in clinical needs based upon 1) age group 

(prenatal, neonatal/infant, preschool child, school-age child, adolescent, and young adult); 2) 

health issues (health maintenance and preventive care [well child, including newborn care], 

critical and emergency care, chronic disease management, behavioral care); and 3) site and 

process of care (birth, delivery and neonatal/newborn care, inpatient care, primary ambulatory 

care, specialty care). Providing care in general, and medication management in particular, is 

more complex and has higher patient safety risks for pediatric patients for at least three 

reasons: 1) patient physiology, 2) the complex nature of common or routine tasks, and 3) patient 

limited communication ability.20 

First, in terms of physiology, children undergo dramatic developmental changes from birth to 

adulthood. Disease states in children are dynamic and change over a continuum with age. 

Specific disease states, symptoms, exam findings, laboratory findings, and treatments vary with 

gestational age, actual age, weight, length, Body Surface Area, Body Mass Index, and other 

variables. In the first month alone, each organ system in the body (e.g., neurological, cardiac, 

pulmonary, hematologic, renal, hepatic, hematologic, and immune) transitions from fetal life to 

postnatal life; organ system changes happen in hours and days in the Neonatal Intensive Care 

Unit (NICU). The range of physical characteristics in pediatrics is much larger than for adults – 

for example, from a 500 gram premature infant to a 100 kilogram 12-year-old. 

These unique pediatric characteristics influence the clinician’s selection of: (a) factors to 

consider for appropriate care, (b) parameters on which to base decisions, (c) goals to attempt to 

achieve, and (d) tasks to implement that are required to achieve these goals. In turn, these 

characteristics and the clinician’s preferred course of action influence how the user interface of 

an EHR must be designed to accommodate and support the cognitive and decision-making 

requirements of the clinician. This is why the unique aspects of pediatric care make selection 

and arrangement of information displays, definition of “normal” ranges and thresholds for alerts, 

among many other display and user interface considerations, more challenging to design and 

implement. In particular, for example, the norms which define “normal,” “standard,” and “wrong” 

dosages for pediatric patients change rapidly over time and the clinical parameters upon which 

“normal” pediatric doses are defined (age, Body Surface Area, and/or weight) also change with 

time. The user interface must be designed to flexibly and reliably accommodate the realities of 

19 Steering Committee on Quality Improvement and Management and Committee on Hospital Care. 

Principles of Pediatric Patient Safety: Reducing Harm Due to Medical Care. Pediatrics Vol. 127 No. 6 

June 1, 2011 , pp. 1199 -1210.

20 Hughes, RG,Edgerton, EA. (2005). First, Do No Harm: Reducing Pediatric Medication Errors. American
 

Journal of Nursing, 105(5), 79-84.
 

8
 
the rapidly changing pediatric patient and relevant clinical parameters and settings. A one-sizefits-all user interface design will not accommodate the clinical needs of pediatric patients or 

support the cognitive and decision-making requirements of their care providers. 

Ideally, the user interface will provide tools to support the configurability of the clinical 

parameters and setting that permits the clinician to modify those clinical parameters or settings 

over which they should have control (e.g., entering the body weight of the pediatric patient) 

while limiting or restricting access to other clinical parameters or settings (e.g., definition of 

“normal” dosages), which presumably should not be modified by a single care provider without 

careful consideration. 

Second, there is immense complexity even for standard daily tasks such as ordering 

medications and vaccinations and administering breast milk that are not pertinent to adult 

patient care. The complexity stems from added burdens of calculations, individual tailoring, and 

patient identification not typically present in adult care. If designed properly, the EHR will 

provide the functions and features for advanced decision support in these areas. 

Third, young and very young children may not be able to communicate at all or sufficiently to 

direct a clinician to important information, raise questions, correct errors, complain, or articulate 

symptoms. This may seem similar to the situation with very sick adults, but may be different in 

one important way. Very sick adults may at least have family and care providers that are able to 

help fill in gaps of information for current clinicians based on communications with the patient 

prior to them becoming sick. Clinicians caring for the young and especially very young may not 

be able to rely as much on past medical history or even family members, since the young and 

very young can no more communicate with their family than they can with clinicians. This lesser 

ability to communicate, like the aforementioned physical and physiological changes, translates 

into different EHR design needs to support care. If clinicians cannot rely as much on the 

patients, they may need to rely more on the EHR. Data that support clinical decision should be 

available, easily accessible, and customizable by groups of end users. All important information 

should be viewable with one click or “hover over” capability to minimize navigation burdens. 

Seeing more of what one might need is especially important for users who must rely more on 

the display and less on the patient. 

Taken together, the unique physiology, task complexity, and patient communication abilities for 

pediatric patients create unique physical and cognitive burdens on care providers that must be 

accommodated by the design of EHRs. We suggest that flexible designs that accommodate the 

rapidly changing physiologic realities of these unique patients, pediatric-specific decision 

support, and well organized displays are high-level goals to help achieve more usable pediatric 

EHRs. Next we review specific pediatric considerations. 

9
 
                                                            

Specific Pediatric Considerations 

The medication use process, specifically weight-based dosing, is more complex and difficult to 

standardize.21 Additional complexities with medication orders include the use of alternative 

liquid, nasal, or partial-tablet formats, combinations of prescriptions. For example, Amoxicillin 

Clavulanate will typically be used in one or two dose forms for adults, whereas there are 13 

different formulations from which a pediatric provider may choose. With low-weight patients, 

sophisticated rounding strategies and accurate weight measurements are particularly critical to 

avoid over-dosing or under-dosing. 

Caregivers are another special consideration that must be factored. The role of pediatric 

caregivers is larger than with other populations, with the possible exception of the elderly. When 

caring for children, lay caregivers calculate and administer medications (e.g., breathing 

treatments to cystic fibrosis patients) instead of medical personnel in some hospitals, which 

creates the need to support nontraditional EHR users. 

Risks for misidentification are different for pediatric patients, and generally higher. Although 

these risks are not new, many paper-based systems had evolved to have additional protections 

that are not available in most EHRs, such as filing charts of siblings together in the same 

location with handwritten cross-references to sibling chart numbers, which provided an intuitive 

indication for how many siblings were in a family.22 Pediatric siblings are the only cohort of 

patients which share outpatient visits with a primary care provider on a routine basis, and 

usually have the same last name (although not always). In the case of multiple births, patients 

additionally share the same date of birth. In the case of newborns, many patients on the same 

unit will have the same birth date, and which is sometimes the same date as the current date. 

Newborn children are more likely to have identical birthdays in a labor and delivery unit than 

other parts of the hospital. As genetic information becomes more readily available and 

integrated into patient-centered care, it will be important to link updated information across 

patient records for patients who are genetically related, such as parents, children, and siblings. 

21 Caldwell, N, Power B. ( 2012). The pros and cons of electronic prescribing for children. Archives of 

Diseases in Childhood 2012;97 (2):124-128.

22 Ross Koppel. Commentary on EMR Entry Error: Not so Benign. 

http://www.webmm.ahrq.gov/case.aspx?caseID=199.
 

10
 
There are sources of information that are unique to pediatric care. Growth charts are critically 

important and should be depicted in internationally accepted formats. There is a greater need to 

access information across multiple-birth patients, including the infant/child, parents, siblings, 

and other family members, particularly for genetic information and for information related to the 

labor and delivery process. High-risk patients, particularly low-birth-weight neonates, are often 

transferred to institutions that support higher levels of complex care, injecting further difficulty 

into information exchange. While a date of birth may seem quite straightforward in an adult 

EHR, a premature infant can have gestational age, postnatal age, and a date of birth. The 

phrase “days of life” is often used in neonatal ICUs (NICUs) and pediatric ICUs (PICUs) to 

prevent confusion. 

Alerts for crossing normative thresholds are particularly challenging for the pediatric population, 

due to the need to do both age-based and weight-based tailoring with respect to medication 

doses and vaccination schedules. That is, what is perceived as “normal” changes rapidly, and 

so what is an appropriate alert changes too. 

Finally, there is immense complexity even for standard daily tasks such as ordering medications 

and vaccinations and administering breast milk that are not pertinent to adult patient care. If 

designed properly, the EHR will provide the functions and features for advanced decision 

support in these areas. 

11
 
 

                                                            

3 A conceptual model of unique use-related risks of EHR 

systems for pediatric patients 

Many of the critical risks for the care of pediatric patients associated with the use of the EHR are 

related not just to the system’s user interfaces but also to the system’s functionality and 

workflow. The Task, User, Representation, and Function (TURF) framework for EHR usability 

was developed by the National Center for Cognitive Informatics and Decision Making in 

Healthcare.23 The TURF framework integrates user interfaces, functionality, and workflow into a 

coherent structure. A conceptual model of unique use-related risks of EHR systems that can be 

applied to the pediatric population is provided in Figure 1, building upon the TURF framework. 

TURF outlines usability as how useful, usable, and satisfying a system is for the intended users 

to accomplish goals in the work domain by performing certain sequences of tasks. In this case, 

primary users are anticipated to include pediatricians, pediatric trainees (resident physicians 

and fellow physicians in pediatric training programs), physicians’ assistants, registered nurses, 

licensed practical nurses, nurse practitioners, school-based health personnel, home nursing 

personnel, including for hospice care, and community-based pediatric health caregivers. 

Additional users include respiratory care providers, physical therapists, social workers, lactation 

consultants, religious support persons, lay caregivers, play therapists, and subspecialty 

consultants, including neurologists, radiologists, and others. Secondary users are anticipated to 

include parents and nontraditional caregivers, adolescent and young adult patients, 

administrators, quality improvement personnel, and public health officials monitoring outbreaks 

and immunizations. 

In order for an EHR system to be useful, it should have functions that enable providers to meet 

the complex needs of their work domain, providing care to pediatric patients. Usefulness is to 

address the intrinsic complexity of the work domain, and it is determined by the functions of the 

system. “Usable” addresses the extrinsic difficulty of user interactions with the system, and it 

reflects the difficulty when a user uses a specific representation or user interface to perform a 

specific task. Extrinsic difficulty is mainly determined by the formats of representations and the 

workflows of tasks. 

Figure 1 below shows how critical risks associated with pediatric EHR are mapped to the TURF 

model. The unique nature of pediatric care is captured as the intrinsic complexity that should be 

addressed by any pediatric EHR, as shown on the left of Figure 1. To avoid adverse events, the 

list of items under intrinsic complexity needs to be implemented and supported as core functions 

of the EHR. Critical risks associated with the extrinsic difficulty of the EHR need to be 

addressed by careful design of the representations (user interface) and tasks (task steps and 

workflow). When there is a mismatch between the intended workflow with an EHR and the 

actual workflow, risks for pediatric patients are higher. Functions, users, representations, and 

tasks are each linked to a set of root causes that can lead to adverse events. In Figure 1, 

examples of root causes for each category are provided. This list is for demonstration only; it is 

not exhaustive. 

23 Zhang, J., Walji, M. TURF: Toward a unified framework of EHR usability. Journal of Biomedical 

Informatics, 2011; 44 (6):1056-1067. 

12
 
                                                            

 

Figure 1. TURF model of unique use-related risks for pediatric patients 

(based on the TURF framework from Zhang & Walji, 201124) 

24 Ibid. 

13
 
 

                                                            

4 Human factors guidance for critical user interactions 

with the EHR for pediatric patients 

First, we provide high-level usability design goals and specific design considerations relevant to 

pediatric patients (“Detailed guidance for critical user interactions”). Next, Table 1 summarizes 

the human factors guidance provided in this section. Each recommendation is numbered and 

grouped by category of critical user interaction. Finally, we provide general themes across these 

recommendations. 

High-Level Usability Design Goals. As explained in Section 2, we recommend that pediatric 

EHRs be flexible enough to accommodate rapid changes in patient physiology and related 

changes in relevant parameters and provide targeted decision support. EHR displays should 

reduce navigation burdens when complete views of critical graphs, tables, charts, or structured 

text are displayed without needing to scroll. Additional displays that present together data that 

are considered for a particular decision reduce the reliance on human memory when needing to 

access separate tabs or screens.25 As is common when human factors design guidance is 

applied to special populations, when one designs to accommodate users with the greatest need 

for usable systems, generally everyone else benefits too. If a door is designed to accommodate 

the tallest among us, then everyone else can fit through. Similarly, many of our design 

recommendations for pediatric patients likely would benefit all patients. The specific 

recommendations below are consistent with these high-level goals. 

25 Koch, S.H., Weir, C., Haar, M., et al. Journal of the American Medical Informatics Association (2012). 

Intensive care unit nurses' information needs and recommendations for integrated displays to improve 

nurses' situation awareness. 2012 Mar 21

 [Epub ahead of print]. 

14
 
                                                            

Detailed guidance for critical user interactions: 

I. Patient Identification. When assigning and/or verifying patient identification numbers and 

names, the risk profile for pediatric care differs from the adult care population. Human factors 

engineering has a long history of employing systematic methods to conduct risk assessments 

of the potential for human error in a given setting. Recognized methods include: Human 

Reliability Analysis (HRA), Probabilistic Risk Assessment (PRA), Accident Sequence Evaluation 

Program (ASEP), and Standardized Plant Analysis Risk (SPAR).26 Some of these methods 

were used to assess the potential for “wrong patient” errors for adult populations in healthcare 

settings.27 Based upon the unique risks for pediatric patients, we make the following 

recommendations to employ multiple means of identification criteria: 

I-A. Use unique patient identification numbers that are not based upon social security 

numbers. Most EHRs already follow this practice by using either unique patient 

identification (UPI) numbers or Globally Unique Identifiers (GUID). This is a prior 

recommendation for all patient populations due to a number of considerations.28 

Nevertheless, this recommendation is particularly important for newborn patients who 

have not yet received a social security number yet require care and documentation of 

care in the EHR. In addition, newborn infants sometimes have changes to last names in 

the first days of life (e.g., BG Jones for Baby Girl Jones changes to Sara Smith). In 

addition, it is recommended that identification numbers/medical record numbers not be 

sequential in order to reduce the risk of confusing multiple birth patients as well as 

newborns born on the same day in the same hospital. Utilization and maintenance of 

master patient indexes, particularly across multiple hospital organizations, could aid in 

reconciling temporary and permanent identifiers for newborn patients.29 

I-B. Include photographs of newborns with primary caregivers for patient identification. 

Photographs of newborn infants with their primary caregivers in the EHR would reduce 

the risks for wrong patient identification. At a minimum, warnings could be employed 

when patients with the same last name and same date of birth are in the same unit. For 

labor and delivery units, patients cannot be distinguished by birth date. In addition, 

particularly for cultures that have many common last names, unrelated patients can be 

located next to each other and have the same last name and same date of birth. 

I-C. Include age, gender, and weight on constant-identification banner headers on all 

screens. Based upon the unit’s population, the following variables might be included: 

name, gender, weight, age, gestational age, post-conceptual age, and date of birth. For 

pediatric patients, it is common practice for family members with the same last name to 

be cared for by the same providers and/or same organizations during the same 

appointment. In order to prevent “wrong patient” errors, constant-identification banner 

headers should include gender, weight (in kilograms), and age as well as the units for 

26 Lyons M, Adams S, Woloshynowych M, Vincent C. Human reliability analysis in healthcare: A review of 

techniques. International Journal of Risk & Safety in Medicine 16 (2004) 223–237. 

27 DeRosier J, Stalhandske E, Bagian JP, Nudell T. Using Health Care Failure Mode and Effect
 

AnalysisTM: The VA National Center for Patient Safety’s Prospective Risk Analysis System. The Joint 

Commission Journal on Quality Improvement Volume 27 Number 5:248-267, 2002.

28 Hildebrand, Richard, James H. Bigelow, Basit Chaudhry, et al. "Identity Crisis: An Examination of the 

Costs and Benefits of a Unique Patient Identifier for the U.S. Health Care System." 2008. 

www.rand.org/pubs/monographs/MG753.html. 29 www.himss.org/ASP/topics_privacy.asp.
 

15
 
 

age, which can range from “days of life” to “months” to “years” in scale. Note that for 

same age siblings due to multiple births, first name, medical record number, and unique 

medical events, such as birth time in minutes, can be the main distinguishing elements, 

and therefore should be easily accessible if not included on the banner header. Finally, 

there is information that is of little interest for pediatric care that can be distracting to 

users, such as information about smoking, drinking, and vaccinations that are only given 

to elderly patients. 

I-D. Distinguish between newly generated and copied information. When taking care of 

multiple birth patients and patients in the same family, it is often important to have 

multiple charts open simultaneously in order to reduce reliance on human memory, 

reduce navigation burdens, and reduce the risks for missing or duplicate tasks that are 

the same for multiple patients. Nevertheless, having multiple charts open at the same 

time increases the risk for “wrong patient” errors where documentation is copied and 

pasted into the wrong chart. Providing the ability to track where information was copied 

from can aid in detecting these types of errors. For example, subtle background color 

can be used behind any text that is copied and as data is entered, the background 

becomes clear (normal) behind the written text. In addition, the source of the copied 

information can be displayed upon mouse rollover or other interaction to request the 

information. An additional protection against “wrong patient” errors in this context is to be 

able to return to the system configuration including automatically saved information that 

was being worked on when a user was automatically logged out when interrupted to do 

another task. 

16
 
 

                                                            

II Medications. When ordering medications for pediatric patients, there are unique factors 

that increase the risks for mode errors. Mode errors are actions performed in one mode 

that were intended for another mode.30 A mode error example could be ordering a dose that 

would be appropriate for a weight in kilograms, but is incorrect for a weight in pounds, and 

vice versa. Mode errors have been reported due to confusion of prescribing a medication 

as the volume in milliliters (mL) rather than milligrams (mg). This common mode error 

results in tenfold iatrogenic overdoses in hospitalized young children receiving intravenous 

acetaminophen for pain relief.31 Unique pediatric risks for mode errors are created by childspecific formats for calculating medication doses, including mg/kg/dose, mg/kg/day, mg/M2

, 

mg, ml, and dose intervals based on gestational and postnatal age. The last mode, “dose 

intervals,” is particularly complex because it is temporally sensitive. For example, 

vancomycin has a recommended dosing interval of 18 hours for patients with a gestational 

age less than 30 weeks old and a postnatal age less than 15 days, but shifts to every 8 

hours for a patient with gestational age from 37 to 44 weeks with a postnatal age of more 

than 8 days. In order to protect against unique risks with mode errors, the following 

recommendations are made: 

II-A. Protect against mode errors for mg/kg dosing and ml dosing. For infant care, EHRs 

should support ordering expressed by volume of drug and by mass of a drug.32 In the 

best case, EHRs would be designed to recognize inappropriate dosing based on the unit 

of measure it is displaying and the specific patient characteristics. In this way, if a 

clinician accidentally entered a value based on the wrong mode, the EHR could 

recognize it, prevent it, and warn the clinician. A weaker solution would be to highlight 

the distinction between these two modes (e.g., terms never truncated on the menu, 

mode risk items not placed near each other, units highlighted on the order form). 

II-B. Flag that an intended dose is unusual. For example, when a 10 mg/kg dose is 

delivered with an every-18-hour (Q18) schedule, the attention of the user can be brought 

to the unusual dose regimen either through passive graphical or text displays of dose 

ranges or with alerts. A detailed discussion of alert design is beyond the scope of this 

report. Human factors alert design recommendations have been made for adult 

populations that are likely also relevant for pediatric patients.33 

II-C. Support high-precision dosing for low-weight patients. Low-weight patients can 

experience toxicity if medications are rounded to the nearest digit.34 In particular, 

medications with narrow therapeutic indices such as digoxin or insulin have a great 

potential for adverse consequences if dosed improperly. For example, for a 575-gram 

infant, kilogram units need to be accommodated to three decimal points. Therefore, a 

higher resolution for precision during medication dosing needs to be supported and 

encouraged with the interface design fields for patients below a particular weight for a 

particular medication. At a minimum, automated rounding based upon needs for adult 

30 Sarter, N.B., Woods, D.D. How in the world did we ever get into that mode? Mode error and awareness 

in supervisory control. Human Factors, 37(1):5–19. 31 Cooper, W.O., Habel, L.A., Sox, C.M., et al. ADHD drugs and serious cardiovascular events in children 

and young adults. New England Journal of Medicine, 2011; 365:1896. 

32 Spooner, A. and Council on Clinical Information Technology. Special Requirements of Electronic Health 

Record Systems in Pediatrics. Pediatrics. 2007;119(3): 631-637. 33 Handbook of Human Factors in Medical Device Design Boca Raton, FL: CRC Press, 2010. 

34 Johnson, K.B., Lee, C.K., Spooner, S.A., Davison, C.L., Helmke, J.S., Weinberg, S.T. Automated doserounding recommendations for pediatric medications. Pediatrics. 2011 Aug;128(2):e422-8. 

17
 
 

                                                            

patients which cannot be overridden should not be permitted for low-weight patients. 

This is particularly true for neonatal infants (e.g., 0.016 mg digoxin is the accurate dose 

needed). Note that this issue also exists for body weight. The weight of infants who 

weigh less than one kilogram should not be rounded up to one kilogram. 

II-D. Do not permit automated defaults to adult doses. Some EHRs employ defaults for 

standard doses in the event of what appears to be an erroneous dose entry since it is 

much lower than the adult normal dose. This practice is extremely risky for low-weight 

patients, particularly if no warning is provided to the provider of the change. For pediatric 

patients, errors due to “automation surprises”35 where the system makes unexpected, 

automated changes to medication orders can have higher patient safety impacts than for 

adult populations. 

II-E. Support custom formulations for liquid medications. With pediatric patient care, a 

common task is ordering medication in liquid form. Custom formulations, such as 

“mg/ml,” should be available as an option. 

II-F. Support documentation of incomplete medication information. With pediatric patient 

care, it is not uncommon for medications to be prescribed by other providers and for 

caregivers to have incomplete information about the medication. For example, if a parent 

describes that the patient is on “some antibiotic,” that information needs to be captured 

even if the medication is not ordered in the EHR. For example, if a medication is ordered 

to address arrhythmia, a disorder of the heart rate or rhythm, it needs to be verified that 

the ordered medication does not negatively interact with the specific antibiotic. Similarly, 

if a caregiver reports that a patient is allergic to “an over-the-counter fever medicine.” 

there should be a way to document that information without having to select a specific 

one. 

II-G. Reduce displayed options for medication orders. Medication prescribing for children 

is a more complex process than for adult patients, particularly in intensive care.36 With 

most paper-based ordering systems, medications are ordered by physicians without the 

specificity used in pharmacies. When pharmacy-specific information is displayed to 

physicians, there can be 17 choices for a common medication, creating complexity that 

can lead to erroneous selection of medications. For medications for children, 

medications are often given together or with complex dosing regimens, thereby 

increasing the number of potential options for ordering. Fewer and simpler options are 

recommended, which would then require support for nurses or pharmacists to correctly 

translate the order into the level of detail required during the dispensing process. 

II-H. Display where the ordered dose (in mg or mg/kg or mg/kg/day) is with relation to 

the recommended dose range. One protection against mode errors is to display the 

expected dose range for a selected amount, particularly in order to prevent errors on the 

scale of ten times the amount. A stronger protection would be afforded by using 

35 Sarter, N.B., Woods, D.D. Pilot Interaction with Cockpit Automation: 

Operational Experiences with the Flight Management System. International Journal of 

Aviation Psychology, 1992;2(4): 303-321. 

36 van Rosse, F., Maat, B., Rademaker, C.M., van Vught, A.J., Egberts, A.C., Bollen, C.W. The effect of 

computerized physician order entry on medication prescription errors and clinical outcome in pediatric 

and intensive care: a systematic review. Pediatrics. 2009 Apr;123(4):1184-90.
 

18
 
 

“guardrails” technology which alerts when medication orders are outside pre-specified 

ranges. This type of technology would require a low rate of false alarms and pediatricspecific information in order to be usable. 

II-I. Display “input masks” for data entry to clarify type of data. A technique to 

standardize data entry is to control the format that is allowed, including the format and 

number of digits allowed, with input masks (e.g., 0.0 mL for volume-based dosing of 

preschool-age children and 0.00 mL for volume-based dosing of neonates). 

II-J. Avoid truncation of medication names and dosages in menus. Users should be able 

to view the full name of the medication and dose without having to select the item to see 

the full text. The full text should be viewable without additional interactions. When 

display space is too limited, at a minimum, rollover interactions that show the full text 

when the user moves their mouse or other input device over the items are 

recommended rather than requiring an item to be actively selected to view all of the 

information. 

19
 
 

                                                            

III Alerts. For pediatric patients, there is much specialized expertise that is taken into account 

when determining a “normal” range for medications, lab results, and other variables. In theory, 

clinical decision support systems can be extremely useful in aiding especially novice providers 

with an order that falls outside the normal range. On the other hand, even for adult patient 

populations, warnings about potential drug interactions are overridden about 90 percent of the 

time,37 primarily due to very high false alarm rates and associated “alert fatigue.”38 Other types 

of alarms have similar override rates, including in a pediatric intensive care unit.39 

Recommendations to improve effectiveness of alerts, reminders, and warnings are: 

III-A. Support flexibility in unit-based settings for alarms, warnings, and alerts based 

upon weight and age. Specialized units focusing on pediatric care including pediatric 

intensive care units, pediatric emergency departments, labor and delivery, and pediatric 

outpatient clinics need to be able to adapt threshold settings appropriate for their patient 

demographics, particularly with respect to weight and age. A committee is recommended 

to be responsible for determining these settings for groups rather than for individuals in 

collaboration with staff members, including pharmacists, physicians, nurses, and 

administrators, and with periodic updates to thresholds and underlying logic. 

III-B. Ensure that adult-based thresholds do not replace pediatric-specific thresholds 

following a system-wide crash. Pediatric-specific thresholds can be designed to apply 

wherever indicated and maintained locally by units that specialize in pediatric care. In 

the event that local adaptations are used rather than embedded in the system settings 

that are used upon start-up of a system, an important consideration is how to recover 

after a system-wide shutdown, planned or unplanned. Although reasonable for systems 

providing care to adult patients to put in place system-wide generic default settings for 

alarms after a system crash, this practice could be dangerous for pediatric patients, 

particularly if providers are not alerted to the changes in default range settings. For this 

situation, no system defaults are preferable to defaults for adult populations. 

III-C. Do not permit “hard stops” for changes to medication orders. “Hard stop” alerts are 

pop-up alerts which block the ability for clinicians to complete an intended action with 

potentially serious consequences. Instead, alerts that permit users to override a 

recommendation in exceptional situations are recommended. Even when warnings are 

effective in preventing dangerous drug combinations such as the anti-clotting drug 

warfarin and certain antibiotics that can produce hazardous effects in combination, 

somewhat rare but critical situations where exceptions need to be made will be critically 

delayed.40 Therefore, alerts with override features are recommended rather than “hard 

stops.” 

III-D. Cap the dose at the standard adult dose and allow override with justification. When 

an order is entered for a child less than 14 years of age that exceeds the standard adult 

37 Isaac, T., Weissman, J.S., Davis, R.B., Massagli, M., Cyrulik, A., Sands, D.Z., Weingart, S.N. Overrides 

of Medication Alerts in Ambulatory Care. Archives of Internal Medicine. 2009;169(3):305-311. 38 Van der Sijs, H., Aarts, J., Vulto, A., Berg, M. Overriding of drug safety alerts in computerized physician 

order entry. Journal of the American Medical Informatics Association. 2006; 13(2):138-47. 39 Lawless, S.T. Crying Wolf: false alarms in a pediatric intensive care unit, Critical Care Medicine. 

1994:22;981-5. 

40 Strom, B., M.D., M.P.H. ,Schinnar, R, et. al. Unintended Effects of a Computerized Physician Order 

Entry Nearly Hard-Stop Alert to Prevent a Drug Interaction. Archives of Internal Medicine. 

2010;170(17):1578-1583.
 

20
 
 

                                                            

dose, provide a real-time alert that the adult dose has been exceeded. Alerts should not 

be “hard stops” in that they should be allowed to be overridden with a justification, 

particularly for obese adolescent patients. 

III-E. Display normal ranges for medication doses and lab values based upon weight and 

age information. Even in cases when EHRs do not have normal ranges for medications 

based upon weight and age information available, the systems could support ways for 

organizations to incorporate this additional information and display it. 

III-F. Display together parameters that are continuously monitored to facilitate rapid 

interventions. It is particularly important to rapidly intervene for premature infants based 

upon a defined set of pre-identified parameters that are displayed together on a single 

screen. Parameters to support monitoring of respiratory distress, hypoglycemia, 

nutrition, muscle tone, circulation, and perfusion are particularly important.41 Navigating 

to other areas of the EHR for real-time monitoring is inefficient and may lead to delays 

and potential errors. A common example is accessing glucose levels in a labs report 

screen, plus the feeding schedule on a different screen, with additional information about 

timing documented in nurses' notes on yet another screen. 

41 http://clinicaltrials.gov/ct2/show/NCT01478711. 

21
 
                                                            

IV Growth chart. For patients between the ages of 0 and 24 months, the World Health 

Organization (WHO) international growth standard is a critical tool for nearly every pediatric 

diagnosis. The Centers for Disease Control (CDC) growth chart is recommended for patients 

older than 2 years old.42 Training for pediatricians is standardized for these representations for 

quick and effective clinical care. When nonstandard representations are used, there is a risk of 

creating the “representation effect.” This human factors phenomenon refers to the fact that 

expertise when using cognitive artifacts is contextualized such that the specialized knowledge is 

difficult to apply when information is not represented in the way in which professionals were 

trained to use it.43 Therefore, to best support cognitive strategies based upon clinical expertise, 

the following recommendations are made: 

IV-A. Change measurement units (e.g., lb vs. kg) only when initiated by the user. For 

infants, it is somewhat common to use the English pound measurement system for data 

collection and then convert to the metric system when ordering medications. In order to 

reduce mode error risks from working in different measurement systems, displays should 

not automatically default to a different measurement system. In addition, displaying units 

of measure along with data values reduces risks for confusion about the current 

measurement system and scale. 

IV-B. Support accurate conversion from pounds to kilograms. A frequent task is 

converting weights for children from pounds to kilograms, and vice versa for 

communicating with caregivers, and can be supported by the EHR to increase the 

usefulness of the system. 

IV-C. Ensure visibility of chart data and axes. Axes should be clearly labeled and the 

values for plotted data should be easily visible. Plotted data points should not overlap or 

otherwise obscure information about other data points. Vertical (age) axes should be in 

1-month increments (rather than 2-3 month increments for the first year of life). A useful 

feature would be providing additional information when “hovering” over a plotted data 

point, such as a percentile. For some functions, it is also useful to have this information 

for parts of the graph that are not plotted, such as for determining a target body weight 

for a particular length. 

IV-D. Display units accurately in standard notation. Standard “shorthand” notation for 

units such as “kg” for kilogram or “lb” for pounds should be employed. Age information 

needs to be clearly denoted as whether it is the current age or age at the time of the last 

visit. 

IV-E. Support selection of particular weight data value to display. Although an important 

function of growth charts is to see trends, there are situations in which an exact weight 

data are needed, such as when dosing medications. Therefore, retrieving the exact 

number plotted on a graph quickly is an important feature. 

IV-F. Display age-based percentiles for weight, height, head circumference, and BMI 

data. Percentiles are critically important indicators for tracking whether growth 

trajectories are stable or changing. Changes in percentiles in weight and height (length 

or stature) can be a signal of poor health. Extreme ends of percentiles might be 

42 http://www.cdc.gov/growthcharts/clinical_charts.htm. 43 Woods, D. D., Roth, E. M. Cognitive engineering: Human problem solving with tools. Human Factors, 

1988;30(4): 415-430.
 

22
 
 

considered healthy as long as the percentile values remain relatively constant over time. 

Therefore, displaying this information as a temporal trend over hours, days, weeks, or 

years is highly useful; the timeline scale is context-dependent. 

IV-G. Single-click navigation to access growth chart display. Growth charts should be 

easily accessible with little navigational burden to access the chart (e.g., one click on an 

easily recognizable icon or menu item). 

IV-H. Single-click interaction to view complete growth chart. The most frequently used 

growth charts should be easily viewed in their entirety with little navigational burden 

(e.g., no scrolling to view the entire chart). 

IV-I. Display height and weight on the same chart. Pediatricians are trained to visualize 

growth charts that display height and weight on the same chart; the visual representation 

points quickly to various types of growth abnormalities that have diagnostic importance. 

The CDC and the WHO growth standard charts are available online. 

IV-J. Support custom views with custom time ranges. For some diagnostic tasks, it is 

important to look closely at a narrow point in time, such as ages 3 to 6 months. Similarly, 

it is helpful to look at an enlarged segment of a chart when there are many points plotted 

on a chart. Therefore, supporting the ability to focus on specific time periods or 

otherwise expand a portion of the chart is a useful feature. 

IV-K. Support corrections to plotted data. There are a number of reasons why plotted 

data may be inaccurate and need to be corrected in order to aid decision making. One 

common reason is when a premature infant’s chronological age is evaluated based upon 

a younger age group. One technique is to “move back” data points by a time period 

(e.g., two months) in order to assess growth given the premature birth. Data quality 

issues might also arise based upon where measurements were taken, how the data 

were collected, and errors in data entry. 

23
 
                                                            

V Vaccinations. Delivering vaccinations is a scheduling task, which has been a muchresearched task in human factors engineering.44 The scheduling task complexity is greatly 

increased when there is interdependency among tasks, otherwise known as task coupling.

45 

Recommended vaccination schedules from the Centers for Disease Control are available for 

children from 0 to 6 years old and 7 to 18 years old.46 In situations where multiple care providers 

coordinate ordering and administering vaccinations, vaccinations are received at more than one 

organization (e.g., child attending college), vaccinations are missed or children did not follow a 

typical schedule ( such as children adopted from other countries), modifying the schedule is an 

extremely challenging cognitive task. To reduce errors in vaccine tracking and ordering 

processes, the following recommendations are made: 

V-A. Allow ordering vaccination via reminder. Reminders that suggest ordering a vaccine 

can facilitate the process by allowing direct ordering via the reminder’s dialog box, rather 

than requiring navigation to an area dedicated for orders or vaccinations. 

V-B. Allow data entry for vaccinations given at other institutions. In the event that 

systems are not completely integrated across institutions, at a minimum, it should be 

possible to document vaccinations given at other institutions. Displaying the origin of the 

information upon demand would be extremely useful, particularly when there are 

discrepancies. For example, a child attending a university might receive a vaccination at 

the student clinic, and this should be able to be documented by the primary care 

provider at home. The same goes for children who switch providers because of a move. 

This ability would reduce the risk of double vaccinations. Similarly, printouts of 

vaccination records should incorporate data from all institutions where vaccinations were 

given. 

V-C. Support display and tracking of components of combination vaccines. In the event 

that vaccinations are administered in combination format rather than individually, the 

system should support tracking what has been administered and when. Ideally, there 

would be support for evaluating whether vaccination intervals satisfy requirements for 

both regular schedules and catch-up schedules. Vaccination scheduling requirements 

can be partially based on whether a vaccine is live (activated) as opposed to inactivated, 

so EHRs supporting tracking and alerting based on that information would be useful. For 

example, two live vaccinations could be administered the same day. 

V-D. Display the days on which prior vaccinations were given and support alerts for 

recommended minimum/ideal/maximum intervals between vaccinations. Information 

about recommended intervals between vaccinations is publicly available and infrequently 

changes.47 A useful feature would be to support scheduling when to give vaccines when 

the recommended schedules were not previously followed for any number of reasons, 

including waiting until school to receive vaccinations and adoptions from other countries 

with different schedules. 

44 Emmett, J., Lodree, E., Geigerb, C., Jiangc, X. Taxonomy for integrating scheduling theory and human 

factors: Review and research opportunities. International Journal of Industrial Ergonomics, January 

2009;39(1): 39-51.

45 Snoo, C., Wezel, W. Coordination and task interdependence during schedule adaptation. Human 

Factors and Ergonomics in Manufacturing & Service Industries. Online 12 Dec 2011.

46 http://www.cdc.gov/vaccines/recs/schedules/. 47 Gardner, P., Pickering, L.K., Orenstein, W.A., Gershon, A.A., Nichol, K.L. Guidelines for Quality 

Standards for Immunization. CID 2002:35 (1 September 2002): 503-511.
 

24
 
 

V-E. Allow sorting of vaccination data by multiple fields. Information about vaccinations 

is used to support a variety of clinical decisions. Sorting displayed information 

alphabetically, by time, by component, and in a shot record format should all be 

supported. 

25
 
 

                                                            

VI Labs. For pediatric patients, the definition of “normal ranges” is extremely complex and 

based upon data that are often only relevant for pediatric patients, such as body surface area. In 

addition, the definitions of “normal” ranges vary based upon the source of the definition. 

Therefore, to increase the chances of accurately depicting findings against useful “normal” 

ranges and support effective communications among the relevant personnel, the following are 

recommended: 

VI-A. Support communications to change inaccurate normal ranges. It is recommended 

that one contact person be designated to receive requests to change inaccurate normal 

ranges for medications and labs. Requests for change are recommended to be 

facilitated by EHR features, which automatically direct the request for change to the 

designated person. 

VI-B. Enable seeing where normal ranges originated from. For example, normal ranges 

in pediatrics could be based on adult normal, pediatric normal, weight-based normal, 

age-based normal, or body surface area normal, depending on which entity generated 

the ranges. 

VI-C. Enable integrated view of test results from different sources. EHRs provide the 

ability in principle to improve reliability and efficiency of the management of electronic 

test results, such as lab results. In adult patient care, one survey found that 83 percent 

of responding physicians reviewing at least one test result in the previous two months 

reported “they wished that they had known about earlier.”48 For the care of pediatric 

patients, concerns have been raised that electronic results management systems were 

not designed explicitly for use in pediatric purposes and thus might pose a threat to 

patient safety. In particular, there can be delays in diagnosis, missed diagnoses, and 

delays in the receipt of appropriate care.49 In one study, a primary barrier to adoption 

was found to be lack of inclusion of all ordered tests in the system.50 When labs are 

received from multiple locations, there should be a way to track trends over time despite 

originating from different institutions. For example, International Normalized Ratio (INR) 

levels from a blood test need to be tracked carefully over time for patients who have had 

cardiac valves replaced and are thus treated with Coumadin. When integrating 

information, it will be important to identify differences in measurement units (e.g., molar 

vs. concentration values) and normative ranges. 

48 Poon, E.G., Gandhi, T.K., Sequist, T.D., Murff, H.J., Karson, A.S., Bates, D.W. “I wish I had seen this 

test result earlier!”: dissatisfaction with test result management systems in primary care. Archives of 

Internal Medicine. 2004;164(20):2223–2228.

49 Wahls, T.L., Cram, P.M. The frequency of missed test results and associated treatment delays in a 

highly computerized health system. BMC Family Practice. 2007;8:32. 

50 Ferris, T.G., Johnson, S.A., Co, J.P., Backus, M., Perrin, J., Bates, D.W., Poon, E.G. Electronic results 

management in pediatric ambulatory care: qualitative assessment. Pediatrics. 2009 Jan;123 Suppl 2:S85-

91. 

26
 
 

VII Newborn care. Newborn infants require special considerations as a category where many of 

the standard assumptions for adult patients do not apply. In particular, more and unique 

information is needed for much quicker decision-making cycles by highly specialized physician, 

nursing, and other care providers. Particularly for premature infant care, it is appropriate to have 

distinct workflows and information displays since the care needs are sufficiently distinct to 

warrant unique support for high-stakes tasks. It is possible that the recommendations made 

below will not be sufficient to truly meet the unique needs of this vulnerable population, and that 

specialized products might be required for safe and effective systems. In the situation where 

newborn care is supported by existing EHRs, the recommendations are: 

VII-A. Enable efficient creation of newborn records. Newborns should be able to receive 

urgent care immediately upon birth regardless of whether there is an EHR-based 

requirement for services such as providing blood for a transfusion. For example, there 

could be an efficient means for creating a new record for a newborn that automatically 

pulls relevant data from the mother’s record that is accomplished with a single selection 

of a menu item or push of a button. 

VII-B. Support updating information that is initially inaccurate or unknown. Information is 

often not immediately available in the NICU or labor and delivery, such as last names, 

sex, and weight. 

VII-C. Support the use of gestational age and corrected age for patient care (in addition 

to chronologic age). Care decisions are often made based upon nonstandard age 

formats in the NICU and for care of premature infants. 

VII-D. Support efficient processes for administration of breast milk, including labeling and 

matching mother to baby to milk. Inefficient processes for verifying matches between 

mothers, babies, and milk can be frustrating and time-consuming, particularly in the 

NICU where it is a frequent and potentially error-prone activity. 

VII-E. Support connecting prenatal data (e.g., fetal imaging procedure) with post-birth 

data. Prenatal data is often available in the EHR chart for the parent, but it would be 

helpful to explicitly link these data with the child’s EHR chart. Of particular importance is 

information about maternal infections, blood type, and pregnancy complications, 

including substance abuse. 

VIIF. Support efficient documentation of blood type. Newborns can receive more 

appropriate blood transfusions than O negative blood if the information can be efficiently 

entered based upon information obtained during birth. 

VII-G. Support the use of alternative weights for dosing. Newborn weights can vary 

dramatically in the first days of life. In some cases, birth weight or “dry weight” (weight 

before surgery) is used to dose medications rather than current weight, referred to as a 

“dosing weight.” For example, when weight is automatically populated in medication 

ordering, the wrong weight could result in ineffective or toxic amounts. This is 

exacerbated when equipment such as an “arm board,” a board to which the arm is taped 

in order to deliver medications intravenously, is included in the weight measure. 

VII-H. Support conversion from Days of Life (DOL) to Days Old (DO) during care 

transitions. Some healthcare organizations use “Days of Life” and some use “Days Old” 

to denote the age of a newborn infant. During transitions of authority and responsibility 

27
 
 

across organizations, it is critically important to be accurate with this information. Since 

all EHRs can be expected to have accurate birthdate and current date information, it 

should be possible to allow hospitals to use either convention and support this 

conversion from one system to the other. 

VIII. Display weights in grams and ages in days, weeks, or months under thresholds. For 

newborns, the appropriate unit is often different given the young age and low weight. 

Under 3 kilograms, it may be preferable to display weights in grams. During patient 

rounds and other verbal communications between neonatologists, grams are often used, 

and supporting this convention, as well as supporting accurate conversions to kilograms 

for dosing, may reduce conversion errors. Under thirty days, it is preferable to view age 

in days, weeks, and months. In addition to better support decision making and 

communications with caregivers, having an age such as “0.005 years old” could lead to 

erroneous assessments. 

28
 
 

 

VIII Privacy. Ensuring patient privacy and allowing sufficient support for communications to 

provide effective care is challenging for any patient. For pediatric patients, there are special 

considerations due to the unique nature of the transfer of care responsibility based upon age 

and maturity from the parent or nontraditional caregiver to the child for responsibility for care, 

particularly with respect to mental health and sexual information. The following 

recommendations are made: 

VIII-A. Support documenting consent agreements for nontraditional parents. It is 

important to support privacy for nontraditional caregiving arrangements such as children 

in foster or custodial care, adults who are not parents, adoptive parents, and guardians. 

VIII-B. Support “break the glass” privacy law violations for urgent care situations. In 

urgent care scenarios, it might be necessary to access critical health information that is 

available in an EHR yet restricted for privacy or security purposes. In the event this is 

needed, the system should support access as long as documentation is made by the 

user logging the event, who accessed the information, and the reason for getting access. 

VIII-C. Make easily visible what information can be viewed, printed, and transferred with 

different levels of privacy or security. Many levels of confidentiality for different notes can 

make it difficult for users to understand what privileges are provided with each level, 

particularly if the distinctions are not well-defined in the online help documentation. For 

example, systems can have confidential notes, sticky notes, private notes, and internal 

notes, each of which has different definitions regarding access for viewing and 

transferring to other systems. Access issues are particularly complicated for adolescent 

patients based on age, assent status, and nontraditional caregiving arrangements. 

29
 
                                                            

IX Radiology. Radiology is a particularly important specialty in pediatric care. Knowing which 

test to order is an important decision because the risk associated with exposure to radioactivity 

is particularly high for infant patients whose cell division is very active and whose cumulative 

exposure over a lifetime is just beginning. For adult patient populations, consultations between a 

physician and a radiologist about which test is appropriate to order are important 

communications. For pediatric patients, the stakes are much higher. Sedation, intubation, and 

radiation for pediatric patients are much higher-risk activities than for adult patients. Having 

multiple scans due to inaccurate selection of correct procedures from physician-radiologist 

communications conducted only through EHR orders and poor usability of the interface can 

have many negative clinical implications for pediatric patients. Therefore, the following 

recommendations are made: 

IX-A. Support physician-radiologist communications to clarify which scan variation to 

order for high-stakes sedation and intubation procedures. A useful feature would be 

supporting real-time communications between an ordering physician and a radiologist 

about which procedure to order in order to reduce delays in care and redundant 

procedures. In order to ensure that the correct test has been ordered, a narrative 

including a diagnostic, short history and indication for the reason for the test is needed. It 

is also important to include information about who can be contacted in real time in order 

to answer a radiologist’s questions. In addition, this support could meet The Joint 

Commission’s (TJC) recommendation to “Create and implement processes that enable 

radiologists to provide guidance to and dialogue with referring physicians regarding the 

appropriate use of diagnostic imaging using the American College of Radiology’s 

Appropriateness Criteria.”51 For example, this communication could avert an erroneous 

order of “chest CT” when a “chest and abdomen CT” is needed. In the event that realtime communications cannot be supported, support could be provided for quality 

improvement reports to facilitate quality improvement by interdisciplinary teams of 

physicians and radiologists. 

IX-B. Support alerts for contraindicated procedures. There is often information 

electronically available from the chart that, in theory, could inform alerts about 

procedures potentially being contraindicated during the ordering process. In the event 

that the current level of technology cannot support this with an acceptable rate of false 

alarms, reports could be printed to support manual review by assigned personnel prior to 

the date of a procedure. 

IX-C. Monitor cumulative radiation exposure over time. A listing in one location of all 

radiology tests, done at any location, for each patient would help to monitor and reduce 

exposure to ionizing radiation. Over the past two decades, the U.S. population’s total 

exposure to ionizing radiation has nearly doubled.52 For newborn patients, it is possible 

that new sources of radiation will emerge in future decades, further raising the 

cumulative exposure over a lifetime. High cumulative radiation exposures create cancer 

and other undesirable consequences. As such, The Joint Commission has 

recommended that dose information be captured in the patient’s EHR.53 Once the dose 

information is captured, a highly useful feature would be a cumulative plot of radiation 

51 Recommendation 2. http://www.jointcommission.org/assets/1/18/SEA_471.PDF. 52 National Council on Radiation Protection and Measurements: Ionizing radiation exposure of the 

population of the United States (2009). NCRP Report No. 160, Bethesda, Md.:142-146. 

53 Recommendation 19. http://www.jointcommission.org/assets/1/18/SEA_471.PDF.
 

30
 
exposure over time for physicians, nurses, radiologists, and ideally caregivers and 

patients. 

31
 
Summary of Human Factors Themes in Recommendations 

In addition to the detailed recommendations described above, there are several overall 

recommendations that apply across multiple categories to enhance usability in general. These 

are: 

 Facilitate smart rounding of the dose of the medication based on actual formulation of 

drugs. For example, digoxin is 50 mcg/mL, 10 mcg/kg/day. For a baby with a low weight, 

an order of 0.4553 mL is too specific, but the ability to order 0.46 mL and not 0.5 mL is 

important to avoid toxicity. Systems must take into account the correct amount of 

arithmetic precision needed in values. 

	 Avoid truncation of displayed information. This includes medication names for ordering 

medications, units for displayed data, names of vaccinations, and information for points 

plotted so closely together that the information was obscured. 

	 Support notations on data. Much of the numeric information used by pediatric clinicians 

is influenced by data quality issues. Being able to notate “fussy” on a blood pressure 

medication reading, information such as “6-week preemie” on growth chart data, and 

“needs booster shot at next visit” for vaccinations would be extremely helpful. In addition, 

it is very important to know where information originated from, particularly when there 

are unusual data values or discrepancies to resolve. 

	 Support local display options for age and weight. Age and weight do not have the same 

meaning, units, or ranges for pediatric patients as for adult populations. In addition, there 

are multiple variations on these measures. Having the ability to locally select display 

options (e.g., g vs. kg, Days of Life [DOL] vs. Days Old [DO], dosing weight vs. current 

weight) would be useful. 

	 Support the use of customized forms, charts, graphs, and reports. There are many 

specialized tasks that some pediatric providers perform routinely that are supported by 

specialized forms, graphs, charts, and other tools. In general, these tools require 

graphing and analyzing quantitative data where the interpretation is based on other 

dynamic factors. For example, if a patient has a dilated aortic root, their dimensions are 

tracked based on Z-score, which is a measure of standard deviation from the mean 

based on the Body Surface Area. When an adult has an aortic root being tracked, you 

simply follow the absolute value of the dimension (e.g., 4 cm, 4.1 cm, 4.2 cm). For 

pediatric patients, the variables that are tracked change with age, weight, height, and 

other factors. Another example is tracking INR data to see what the dose of an anticoagulation should be. With paper-based records, physicians typically made flowsheets 

and graphs that were pasted into the chart. With electronic records, most EHRs currently 

lack flexibility to provide support for data entry, graphing, or documentation of paper 

graphs. 

	 Support optimizing alerts. Alerts are particularly important in pediatrics given the extreme 

complexity of providing care, particularly for vaccination scheduling and medication 

ordering. On the other hand, designing threshold settings for alerts is particularly 

challenging in pediatrics given high variability and the need for specialized information 

that may change quickly over time. Being able to optimize the alert settings locally, 

particularly for particular units and levels of experience, would be highly useful. In 

general, in pediatrics, the variability is so high that it is strongly recommended that all 

alerts can be overridden at the discretion of the provider. 

32
 
Table 1. Critical user interactions and human factors recommendations 

Critical User 

Interactions 

Human Factors Recommendations 

Patient identification IA. Use unique patient identification numbers that are not based upon social security numbers. 

IB. Include photographs of newborns with primary caregivers for patient identification. 

IC. Include age, gender, and weight on constant-identification banner headers on all screens. ID. Distinguish between newly generated and copied information. 

Medications IIA. Protect against mode errors for mg/kg dosing and ml dosing. 

IIB. Flag that an intended dose is unusual. IIC. Support high-precision dosing for low-weight patients. IID. Do not permit automated defaults to adult doses. IIE. Support custom formulations for liquid medications. IIF. Support documentation of incomplete medication information. IIG. Reduce displayed options for medication orders. IIH. Display the recommended dose range for the selected mg/kg dose. III. Display “input masks” for data entry to clarify type of data. IIJ. Avoid truncation of medication names and dosages in menus. 

Alerts IIIA. Support flexibility in unit-based settings for alerts, reminders, and warnings based upon weight, height, B

Mass Index, and age. IIIB. Do not permit replacing pediatric-specific thresholds with default adult-based thresholds following a systeIIIC. Do not permit “hard stops” for changes to medication orders. IIID. Cap the dose at the standard adult dose and allow override with justification. IIIE. Display normal ranges for medication doses and lab values based upon weight, height, Body Surface Ar

age information. IIIF. Display together parameters that are continuously monitored to rapidly intervene. 

Growth chart IVA. Do not permit changes to measurement systems (e.g., lb vs. kg) unless initiated by the user. 

IVB. Support accurate conversion from pounds to kilograms. IVC. Ensure visibility of chart data and axes. IVD. Display units accurately in standard notation. IVE. Support selection of particular weight data value to display. IVF. Display age-based percentiles for weight, height, head circumference, and BMI data. 

IVG. Provide single-click navigation to access growth chart display (e.g., one click on easily recognizable iconIVH. Provide single-click interaction to view complete growth chart (e.g., no scrolling). IVI. Display height and weight on same chart. IVJ. Support custom views with custom time ranges (e.g., 3 months to 6 months). IVK. Support corrections to plotted data. 

Vaccinations VA. Allow ordering vaccination via reminder. 

VB. Allow data entry for vaccinations given at other institutions. VC. Support display and tracking of components of combination vaccines. VD. Display the days prior vaccinations were given and support alerts for recommended minimum/ideal/maxivaccinations. VE. Allow sorting of vaccination data by multiple fields. 

33
Table 1 (Cont). Critical user interactions and human factors recommendations 

Critical User Interactions 

Human Factors Recommendations 

Labs VIA. Support communications to change inaccurate normal ranges. 

VIB. Enable seeing where normal ranges originated from (adult normal, pediatric normal, weight-based normal, 

surface area normal). VIC. Enable integrated view of lab results from different sources. 

Newborn care VIIA. Enable efficient creation of newborn records. 

VIIB. Support updating information that is initially inaccurate or unknown (e.g., last names, sex, weight). VIIC. Support the use of gestational age and corrected age for patient care (in addition to chronologic age). VIID. Support efficient processes for administration of breast milk, including labeling and matching mother to baVIIE. Support connecting prenatal data (e.g., fetal imaging procedure) with post-birth data. VIIF. Support efficient documentation of blood type. VIIG. Support the use of alternative weights for dosing. VIIH. Support conversion from Days of Life (DOL) to Days Old (DO) during care transitions. VIII. Display weights in grams and ages in days, weeks, or months under thresholds. 

Privacy VIIIA. Support documenting consent agreements for nontraditional parents (children in foster or custodial care, 

parents, adoptive parents, and guardians). VIIIB. Support “break the glass” privacy law violations for urgent care situations. VIIIC. Make easily visible the rules that describe what information can be viewed , printed, and transferred with 

security on notes and all text in the chart. 

Radiology IXA. Support physician-radiologist communications to clarify which scan variation to order for high-stakes seda

procedures. IXB. Support alerts for contraindicated procedures. IXC. Monitor cumulative radiation exposure over time. 

34
5 Opportunities for innovation 

In addition to the recommendations provided in Table 1, potential opportunities for future 

innovation were identified. In particular, developers interested in developing specialized “child 

modules” for pediatric patient care that can be used in conjunction with established EHR 

systems might consider the following areas that we believe would be viewed as useful features: 

 “Normal” dose ranges, lab, and vital sign values for pediatric patients. Complex factors 

are used in each of these calculations. For example, vital signs and blood pressure 

ranges depend on height and age but not weight, whereas dosing is based on weight. 

	 Support seeing all variations on a medication order (e.g., amoxicillin) or procedure (e.g., 

CT scan) without clicking on them, and view which ones have most frequently been 

selected. 

	 Support selecting the necessary billing codes and generating the associated 

documentation to receive the appropriate financial reimbursement from third parties 

(e.g., insurance companies). 

	 Support innovations in “smart alerts,” including expertise-based adjustments and 

escalations of who receives alerts based on values and combinations of variables with 

Boolean logic. 

 Provide smart vaccine support, such as verifying vaccine combinations, catch-up doses, 

and missed doses. 

 Support locally adding charts for specific conditions (e.g., Down syndrome). The growth 

chart is not the only chart that is useful for pediatric patient care. 

	 Support tracking and graphing medical data, where the interpretation is based on other 

dynamic factors. For example, if a patient has a dilated aortic root, their dimensions are 

tracked based on Z-score, which is a measure of standard deviation from the mean 

based on the Body Surface Area. When an adult has an aortic root being tracked, you 

simply follow the absolute value of the dimension (e.g., 4 cm, 4.1 cm, 4.2 cm). For 

children, the variables that are tracked change with age, weight, height, and other 

factors. With paper-based records, physicians typically made flowsheets and graphs that 

were pasted into the chart. With electronic records, most EHRs currently lack flexibility to 

do this strategy. 

	 Support role-based access control for sensitive portions of the note. As responsibility for 

care is transferred from parents to children, there is an increased need to protect 

sensitive portions of the note from parents, such as psychology notes and suicide flags. 

Provide support for communications to add medications to the formulary, reduce false 

alarms for alerts, reminders, and warnings, and address sources of inaccurate data. It is 

recommended that one contact person be designated to receive requests to change 

inaccurate settings or undesirable settings. Requests for change are recommended to 

be facilitated by EHR features, which automatically direct the request for change to the 

designated person. 

	 Provide support for identifying areas for quality improvement by displaying clinical data 

for cohorts of patients against benchmarks. A useful feature, particularly for quality 

improvement or administrative personnel, is to have reports and dashboard displays to 

see how patient care measures relate to national benchmarks. Pediatric subspecialties 

are continuously advancing local, state, and hopefully eventually national standards for 

care and associated measures, few of which are currently captured with existing or 

future requirements for quality improvement measures for adult patients. For example, 

35
 
                                                            

                             

           

all infants with a fever within the last 28 days treated in the hospital might want to be 

reviewed for quality improvement or research purposes. 

	 Drug dictionaries with pediatric-specific dose ranges and alerts that include single-dose, 

daily-dose, and cumulative-dose decision support, including lifetime cumulative dose for 

chemotherapies.54 

	 Support physician-radiologist communications to clarify which scan variation to order for 

high-stakes sedation and intubation procedures. A useful feature would be supporting 

real-time communications between an ordering physician and a radiologist about which 

procedure to order in order to reduce delays in care and redundant procedures. In 

addition, it is important to track the frequency of radiology procedures to assess the 

cumulative patient safety risk. 

	 Support warnings for contraindicated procedures. There is often information 

electronically available from the chart that could inform the system notifying the user that 

a procedure is contraindicated during the ordering process. For pediatric patients, 

procedures often are more challenging than for adult patients due to small sizes and 

resistance to staying still during intubation and other critical activities. On the other hand, 

some procedures occur more often, such as lumbar punctures for young infants since 

they have a higher risk of meningitis and do not reliably show signs of meningeal 

irritation. Patients with increased intracranial pressure (ICUP) are contraindicated for 

receiving a lumbar puncture. 

	 Accommodate critical information exchange for patient facility transfers (particularly for 

premature neonates). Extremely sick premature neonates will frequently be transferred 

to receive care at institutions with the most specialized knowledge to provide care. 

Having an efficient way to get an overview of critical information is critical to meet the 

needs of these patients. In most cases, no social security number will yet be available 

for these patients; therefore, additional support for accurate identification is needed. 

	 Display the origin of medication, lab, and procedure information. Knowledge of where 

information originates is critical to determining what information to consider when there 

are discrepancies. As EHRs begin to incorporate more information originating from other 

systems designed for other purposes, such as insurance companies, billing services, 

and personal health records, this ability will be even more important. 

54 Kim, G.R., Lehmann, C.U., Council on Clinical Information Technology. Pediatric Aspects of Inpatient Health

Information Technology Systems. Pediatrics. December 1, 2008;122(6):e1287 ‐e1296.

36
 
 

6 Conclusion 

Usability of EHR systems has been identified as an important factor in patient safety. The 

adoption of EHRs by providers specializing in pediatric patient care has lagged behind adoption 

for general population providers. Pediatric patient care has unique features and some aspects 

of care are more complex and have higher stakes. In this document, we highlighted unique user 

interactions important for providing pediatric care with the support of an EHR and provided 

guidance from the human factors literature to increase usability. Of particular importance in the 

provided recommendations are: 

 Display information in menu items and on charts/graphs without truncating critical 

information, including doses and measurement units; 

 Support one-click access to the growth chart in the standard display format; 

 Eliminate automated changes to adult doses for medication orders; and 

 Protect against ordering medications in the wrong units, which could result in tenfold or 

higher dosing errors. 

37
 
     

     

     

       

     

     

     

     

     

     

     

   

     

   

       

   

   

     

   

   

Appendix A Representative clinical scenarios 

In the appendix, we illustrate some of the unique considerations for pediatric patients in the 

context of representative, fictional clinical scenarios. Developers may find these scenarios 

helpful for the purpose of assessing unique risks for pediatric patients, such as through 

formative user-centered design approaches or summative usability evaluations. Human factors 

elements are highlighted in call-out boxes on the right. 

Scenario A: Newborn with sepsis treated by Emergency Department 

physician 

A six-day old infant is brought to the Emergency Department (ED) by his 

mother who reports that he has a fever. At triage, he is very irritable, has 

a rectal temperature of 39C, and a bulging anterior fontanel. By ED 

protocol, he is brought to a treatment room immediately to be seen by a 

physician. The infant’s history is significant for being the A twin of a term 

pregnancy delivered by a scheduled C-section. The B twin is reported to 

be well at home with the father. The physician’s assessment is that the 

infant may have sepsis/meningitis and requires a workup. On 

examination, the physician hears crackles and assesses that a chest xray is indicated. 

The mother does not know the medical record number, so the registrar in 

the ED asks for the social security number, but it has not been issued yet. 

The registrar successfully finds the mother’s file that includes a note with 

medical record numbers for both children. The physician successfully 

pulls up the chart. The physician clicks the “sepsis bundle” quick order on 

the interface. The system orders standard adult doses for these 

medications, which are far too large, as well as an inappropriate 

procedure for placing a central line, so he cancels the set of orders. 

The physician then calls up a feature that supports weight-based dosing. 

He estimates that the patient weighs 8 pounds and types 8 in the weight 

box entry and switches the default system from kilograms to pounds by 

clicking the radio button next to the number. The physician does not 

realize that the system records the weight as 8 kg. The alert that the 

weight falls outside the normal values is located on the “face sheet” page, 

not on the page where the dose is entered. The ordered dose is not what 

the nurse expects, and she realizes that the system treated the entry as if 

it were kilograms, not pounds (because the software does not store units 

with the data and assumes kilograms for any weight-based data entry). 

The nurse informs the physician that the weight is incorrect. 

The nurse then informs him that the exact weight is 4.1 kg (9.0 pounds). 

The physician remembers that the appropriate dose for the antibiotic is 10 

mg/kg/dose, and calculates in his head that the appropriate dose is 41 

mg. He types in the order for a brand name antibiotic for that dose. When 

he reviews the medication order, he sees that the system has created an 

additional order for the standard adult dose of 2000 mg as well as in the 

Increased risk for

wrong patient error:

Same parents, last

name, sex, birth date,

age, and gestational

age

Increased risk for

wrong patient error:

Delay in obtaining

social security number

Automation surprise:

Unexpected default to

standard adult dose

Automation surprise:

Unexpected change in

measurement system

for weight results in

inaccurate data

Complexity: Potential

for mode error,

calculation mistakes,

during medication

ordering

38
 
 

 

   

   

       

   

     

         

   

   

   

   

   

       

   

   

     

   

     

   

     

   

     

       

     

generic form rather than the brand name form of the medication. He 

cancels the adult dose and confirms the pediatric dose. 

The doctor needs broad antimicrobial coverage and wants to start a 

second antibiotic. The physician then enters an order for the second 

medication. The dose and frequency of administration for this medication 

are dependent on the gestational age of the patient, the actual age, the 

weight, and the renal function. This medication is administered by IV, so 

the options available for ordering are reported in ml, but the information 

about the concentrations that indicate what the dose is in mg/kg are 

truncated on the display. In some of the EHR systems that the doctor 

uses, this medication is ordered in mg/kg/day, and in some mg/kg/dose, 

so the doctor needs to double check how the information is being 

displayed. He clicks on each of the options, and then cancels the orders 

until he finds the correct concentration. He calculates the amount of 

medication needed in his head and orders it. When he reviews the order, 

the system has automatically rounded the dose amount to the nearest 

regular dose, which is too high and would be potentially harmful to the 

child if administered. He cancels the order and manually enters the 

correct amount. 

The physician then returns to assess the patient, and informs the mother 

that antibiotics have been ordered. He learns that the patient’s twin is at 

home with the father and asks again what the patient’s first name is. 

When he looks at the record, he realizes that he had ordered the 

medications for the wrong sibling. He informs the mother that since this is 

an emergency situation, he will not correct the mistake now, but that he 

will add this information to the progress note so that people in the future 

will be aware that it was the other sibling that had sepsis. He writes in his 

note that there was an error in patient identification and that the sepsis 

treatment was administered to the other child, including the temporary ID 

number and the mother’s name for referencing purposes. 

The physician attends to another patient with critical issues. He then 

orders a CT scan for the infant in the electronic health record. There are 

24 available options for CT scans taking into account the potential 

implications of size-based parameters, sedation techniques, and body 

size. Choosing the appropriate test would be very difficult for a less 

experienced physician without consulting with a radiologist. 

Automation surprise:

Unexpected change

to generic form of

medication

Mode error: High

potential for selecting

the wrong dose due to

complexity, truncation

of critical information,

and inconsistent

conventions

Automation surprise:

Unexpected

consequences of

rounding a dose for

low‐weight patients

Workflow mismatch:

Difficult to change

inaccuracies in

documentation after the

event

Specialized expertise:

Would benefit from

consulting with

radiologists or decision

support for which CT

scan to order

39
 
 

   

     

   

     

       

   

   

   

     

     

     

   

Scenario B: Premature twins cared for by NICU interdisciplinary team 

The unit clerk in the neonatal intensive care unit (NICU) receives a phone 

call asking for transport of two premature (30 weeks gestational age) twin 

girls, both with significant critical care needs. The NICU attending 

physician, Dr. Patel, accepts the patient and is in communication with the 

air transport team and the attending care team at the transferring 

community hospital. While on the phone with the transferring ICU team, Dr. 

Patel is taking handwritten notes. Dr. Patel needs to order critical 

medications/drips and emergency diagnostic imaging in anticipation of the 

arrival of the transport team and the twins. A full set of general admission 

orders is also needed. 

 Because the patients have not been admitted yet, Dr. Patel is unable to 

use the EHR to write orders for the patients, so he notifies the unit clerk of 

the impending admissions and uses paper. Dr. Patel creates the names of 

Smith Baby A and Smith Baby B in keeping with NICU protocol and 

randomly chooses two admitting bed numbers in the NICU for their 

admission. Dr. Patel randomly chooses the bed numbers because he 

knows that the pharmacy will not process the medication orders without a 

bed assignment (Dr Patel’s attempt at a workaround). Dr. Patel submits the 

paper orders to the unit clerk who proceeds to call the pharmacy. 

The two bed numbers that Dr. Patel selected were already occupied with 

other patients, and thus the names associated with the orders did not 

match the names available to the pharmacists about patients in beds (the 

form was called the bed assignment roster). The pharmacist refused to 

verify the medication orders. Similarly, diagnostic imaging refused to 

schedule the procedure, and called the NICU unit clerk with the message 

“call us back when the babies arrive and you have bed assignments.” 

Dr. Patel then was informed by the charge nurse that the standard 

workaround was to use “virtual bed numbers” for urgent care situations 

prior to bed assignments. The charge nurse in the unit called the pharmacy 

and provided the two virtual bed numbers to the pharmacist tech who 

answered the phone and asked the admissions clerk to admit the incoming 

twins to the virtual beds. The pharmacy then began to process the orders 

for Smith Baby A and Smith Baby B. The presence of the virtual bed 

numbers also allowed the admissions clerk to formally “admit” the patients, 

even though they had not physically arrived yet. The admissions clerk typed 

the age in as 30 weeks, since it did not make sense to her to type 0 in as 

the age. Admitting the patients then allowed two new electronic records to 

be created, one for Baby A and one for Baby B. 

Workaround:

Provider

attempts to

select beds with

other patients

already in them

in order to write

time‐critical

orders.

Learnability:

Nonroutine

process of

‘virtual bed

numbers’ is

used to allow

timely care in

exceptional

circumstances,

not intuitive for

first‐time users.

40
 
   

   

   

     

   

     

   

   

     

   

The twins arrived shortly thereafter via airlift from the community hospital. 

The fragments of the patient’s charts that arrived with them as printouts 

listed the babies as Baby 1, Smith and Baby 2, Smith. It was now after 

midnight, so technically this was now the second day in chronological age 

for the twins. 

Smith baby A/Baby 1, Smith and Smith Baby B/Baby 2, Smith were 

delivered via C-section at 30 weeks gestation, and therefore were 

premature (preemies). The mother was a 20-year-old woman in her first 

pregnancy that developed preeclampsia. The transferring community 

hospital (out of state) was not part of the same hospital system as the 

NICU/admitting hospital. Because the babies did not have social security 

numbers yet, the transferring hospital had used a unique medical record 

number (MRN) for each of the babies. The admitting NICU did not 

recognize the transferring MRNs, so two new identification numbers were 

created based on the NICU protocol. Data from the transferring hospital 

was therefore linked to babies with different names and different unique 

identifiers than what had been created in the admitting hospital system. 

Both babies were eventually assigned to open (non-virtual) beds in the 

NICU, requiring a change in the bed assignments in the system. Any paperbased orders and incoming data were manually transcribed into the EHR in 

the NICU system by the bedside nurse using the new bed assignments and 

the new identifiers. The baby names were changed to Smith Baby A and 

Smith Baby B. (Note that John Smith, an unrelated patient, was also in the 

NICU at this time.) 

Smith Baby A weighed 3 lbs. 12 oz. Smith Baby B weighed 3 lbs. 15 oz. 

Smith Baby A presented with gastroschisis, a birth defect in which an 

infant's intestines stick out of the body through a defect on one side of the 

umbilical cord, and was intubated. Smith baby A’s intestines appeared to be 

poorly perfused, indicating concerns about blood flow, and oral secretions 

were positive for meconium, indicating the potential for airway blockages. 

Both babies had Patent ductus arteriosis (PDA), a congenital disorder in the 

heart that causes shortness of breath. Smith Baby A’s PDA was small and 

asymptomatic, Smith Baby B’s was not. Smith Baby B was receiving 

oxygen therapy via a nasal cannula tube. 

Baby A was transported to the Operating Room (OR) within three hours of 

arrival. The system in the OR was not integrated with the NICU’s EHR. The 

medications ordered and filled by the pharmacy were delivered to the 

NICU, although Smith Baby A was now in the OR. Other orders had to be 

temporarily suspended or modified while the patient was in the OR. Smith 

Baby B’s PDA was hemodynamically significant and also was receiving 

supplemental oxygen to correct hypoxia, also known as oxygen depletion. 

An order was written for indomethacin for Smith Baby B. 

Patient

identification:

Challenges with

nonstandardized

identifiers

increases the

potential for

patient

identification

errors based on

mismatch of

babies, beds, and

identifiers.

Data availability:

Poor

interoperability

creates the

potential for

medication

errors, delays in

care, and

redundant

workflow.

41
 
 

   

   

   

     

   

   

Baby A returned from the OR to the NICU. Both Dr. Patel (the original 

admitting physician) and the original charge nurse had handed off 

responsibility for care at the shift change to new providers, Dr. Ware and 

the new charge nurse. The day shift personnel assumed responsibility for 

care and began rounding on all patients, including the two patients. 

Seven personnel (1 attending physician, 2 fellow physicians, 3 resident 

physicians, and a rounding nurse) comprised the rounding team. Four of 

the team members pushed computers on wheels (COWs), two had iPads, 

and one was using a bedside tray table with a laptop on it. The resident 

physicians took turns using one of the COWs to open the next patient’s 

chart since the process of authenticating into the system and opening a 

chart took approximately 4 minutes. This approach made the rounding 

process somewhat more efficient. At the end of the rounds, the attending 

physician, Dr. Ware, opened each chart of the 30 neonates in the unit to 

review and sign off on the plan of care. She used two side-by-side 

computers to alleviate the delay between opening one patient’s chart at a 

time. 

Workarounds:

Lengthy response

time requires

workarounds to

meet urgent care

needs.

Workarounds

tend to

circumvent built‐

in safety

features.

42
 
 

 

   

   

     

   

   

   

       

Scenario C: Teenager treated for knee pain by family physician 

A 16-year-old male patient goes to a family practice physician for knee pain 

after falling during basketball practice as a same-day ill-child urgent outpatient 

appointment. His past medical history is significant for requiring a pacemaker 

for heart block caused by neonatal lupus, which is a rare temporary 

autoimmune disorder due to receiving positive maternal autoantibodies from 

the mother while in utero. 

His mother has signed a release, and he is being seen by the doctor without 

any parent present at the visit. While at the visit, the patient discusses 

concerns about personal matters. The pediatrician assures him that the 

conversation will remain private. The physician selects the option “secure note” 

on the interface in order to protect the information. (Unfortunately, the correct 

option was “confidential note” for this purpose, not the selected option or the 

other available options of sticky note, private note, or internal note, none of 

which are defined in the online help system documentation.) 

After physically examining the knee, there is a concern about a torn anterior 

cruciate ligament (ACL). The pediatrician consults an orthopedic surgeon, who 

recommends a Magnetic Resonance Imaging (MRI) test to make a picture of 

the knee to definitively determine whether the ACL has been torn. The 

physician orders an MRI from available options, without realizing that the order 

will not provide the correct billing (Current Procedural Terminology - CPT) code 

for full reimbursement from the insurance company. The correct test was 

available in the menu options, but looked nearly identical based on the 

description. 

The mother asks for a printout of the findings from the physical examination to 

be taken to the surgeon’s office. The physician selects “print to PDF” with the 

option for a “redacted note with no addendum” for the note on the date of the 

outpatient visit and emails the file through a secure patient portal. The mother 

is surprised to see the private information documented in the note and 

discusses the matter with the patient, who feels a loss of trust in his 

relationship with the physician. 

On the day of the MRI, the test is cancelled because the nurse realizes the 

patient has a pacemaker, which they consider a contraindication for MRI. The 

patient schedules a new appointment three days later with his family physician 

in order to determine an appropriate treatment plan. 

Shared access

to protected

data: Difficult

to see what

information is

accessible to

whom

Alert: No alert

for

contraindication

for MRI with a

pacemaker

43
 
     

     

   

   

   

       

     

     

       

   

   

   

     

   

       

   

   

       

Scenario D: Infant treated for recurrent ear pain by pediatrician 

A 30-month-old female patient has been having recurrent ear infections. 

There is nothing significant in the prior medical history, and she was well 

during her 2-year annual examination. She has had 4 visits in the last 6 

months for recurrent otitis media (OM), which is inflammation of the 

middle ear; sinusitis, which is inflammation of the sinuses; and 

bronchiolitis, which is swelling and mucus buildup in the lungs. However, 

she just started day care, along with her 4-year-old brother, and many of 

the children in the daycare have been ill with similar symptoms. 

 During the fifth same-day ill-child visit for the same problem, the 

pediatrician notices that one of the vaccinations was ordered but then 

canceled, and recommends to the parents that the child receive the 

vaccination immediately, which they consent for, and it is given. (She later 

realizes that the vaccination order was canceled because it was 

previously given.) 

At the 3-year-old annual well-child visit, the mother requests for the 

patient to be seen by a specialist for all of the infections. In carefully 

reviewing the growth chart, the pediatrician realizes that the child has not 

grown well in the last 6 months, and has gone from the 75th percentile to 

the 25th percentile in both height and weight. She apologizes to the 

mother because the team had missed the poor growth for several 

months. It would have been unusual to miss the trend with the previous 

paper-based growth charts, but the doctor suspects the error to be more 

common with the electronic growth embedded in the EHR, because it 

requires 8 mouse clicks to view, and an additional 3 mouse clicks to look 

at height and weight in the internationally standard format promoted by 

the Centers for Disease Control. 

In addition, she realizes that the child received two doses of the same 

vaccine due to confusion about what had already been given when. She 

apologizes for having given a duplicate dose of the vaccine and 

determines an adjusted vaccination schedule for future vaccines which 

are based upon a desired time window after receiving an earlier vaccine. 

She carefully documents this schedule in a note and writes a post-it note 

that the vaccination schedule has been changed and the date of the 

electronic note to read with the new schedule on the paper copy of the 

chart that is kept in the secretarial suite. 

Visibility of critical

information:

Difficult to quickly
 

see what
 

vaccinations were
 

previously given
 

and on what day
 

Navigation: Difficult

to access growth

chart display on

format that has been

trained on

Coupled variables

during scheduling

task: Deviating from a

routine vaccination

schedule is a complex

cognitive task

requiring

remembering changes

to plans months later.

44