# Presentation: 682117

## Influencing Patient Safety in Rural Primary Care Clinics through Computerized Order Entry

- Matthew Samore, MD
- VA Salt Lake City Health Care System
- Professor of Internal Medicine
- Adjunct Professor of Biomedical Informatics
- University of Utah

## Acknowledgments

- Partners: University of Utah, *Healthinsight*, CaduRx
- Funding:
- INFORM study: AHRQ R01 HS15413

- Kim Bateman, MD
- Frank Drews, PhD
- Wu Xu, PhD
- Brian Sauer, PhD
- Shobha Phansalkar, PhD
- Charlene Weir, PhD
- Jonathan Nebeker, MD

- Amyanne Wuthrich
- Jose Benuzillo
- Warren Pettey
- Marjorie Carter
- Marci Fjelstad
- Rui Saito
- Shuying Shen

## Outline

- Background
- Conceptual framework
- Related work
- Our active studies in patient safety
- Patient safety and health information technology in rural settings: the INFORM study
- Methods
- Participating clinics
- Health information technology
- Measurements
- Results to date
- Summary and future directions

## Conceptual framework

- Natural vs. engineered systems
- Natural systems
- Living organisms
- Weather systems
- Engineered systems
- Aircraft
- Computers

## Conceptual framework

- Commonalities
- Defining properties and functional capabilities are causal
- Internal component relationships are deterministic
- Potentially predictable relationship between internal components of system

## Conceptual framework

- Differences
- Adaptability
- How does system deal with change
- Natural systems evolved
- No spontaneous change of defining parameters
- Adaptation as a result of unstable previous states, to meet the changing demands placed upon by environment
- Technical systems
- Intentional design and manufacturing to provide solution to a specific, practical problem
- Suitable to perform a number of specific tasks

## Conceptual framework

- Differences
- Transparency
- Observers ability to observe and understand the algorithms that govern a systems operation
- Natural systems
- Non-transparent
- Algorithms describing component interactions and state variables have to be deduced and are not fully understood
- Models – describe and mimic behavior
- Engineered systems
- Designed for transparency
- Relationships are planned and calculated during design process
- Despite transparency have complex systems sometimes unpredicted and unanticipated emergent behavior
- Transparency affects the ability to cope with changes of system

## Conceptual framework

- Differences
- Linearity
- Mathematical description of the relationship between output of system to its input
- Natural systems
- Often display non-linear behavior
- Engineered systems designed to be linear
- Makes it possible to solve linear problems analytically
- Linear systems output can be predicted from input
- Allows application of reductionistic approach

## Conceptual framework

- Differences
- Predictability
- Allows for anticipation of future state of system
- Natural systems
- More challenging to understand and modify
- Partly due to adaptive and non-linear nature
- Even simple natural systems difficult to predict
- Engineered systems
- Easier to understand and maintain
- Higher complexity increases operator demand
- Maintenance and prediction are possible

## Conceptual framework

- Clinical care delivery
- Complex natural system that varies across myriad settings and care
- Health information technology implementation
- Engineered system meets natural system

## Our active studies in patient safety 

- Medication management
- Real-time detection of prescribing problems
- Medicaid population (Nebeker, Xu, and Sauer)
- VA health care system (Nebeker and Weir)
- Clinical decision support systems in rural settings
- Primary care (Samore and Bateman)
- Nursing homes (Rubin)
- Optimal laboratory monitoring intervals (Sauer)
- Error-producing conditions in intensive care units, including interruptions and task ambiguity
- Medical device problems
- Use of observation (Drews and Samore)

## Methods

- INFORM study
- Clinic-randomized trial to evaluate impact of computerized clinic order entry tool on clinical practice and office efficiency
- Features of computerized clinic order entry tool
- Web-based writing of outpatient orders, including prescriptions, immunizations, laboratory tests, X-ray studies, and work notes.
- Accessible via any type of computer.
- Electronic transmission of orders to pharmacies and other vendors or to local printers
- Common access to records across providers who share patients while maintaining strict levels of confidentiality.

## Methods: participating clinics

- Rural primary care clinics with minimum of two providers were recruited
- None had pre-existing electronic health records
- Eighteen clinics randomly assigned to two groups
- Early implementation (launch in year 1)
- Delayed implementation (launch in year 2)
- Post-launch, 2 clinics closed

## Methods: participating clinics

## Methods: health information technology user interface

- Fumbling for his
- recline button, Ted
- unwittingly instigates
- a disaster.

## Methods: provider home page

- List of all refill requests your staff has submitted for your approval

- Pt: Search for a patient record

## Methods: refill requests

- Select patient name to review medication history

- Select drug to review request and to edit, approve or deny

## Methods: patient home page

- A brief tour:
- 	Status bar
- 	Navigation commands Special functions

- Active drug list
- Filters
- Status icons

## Methods: drug favorites

## Methods: drug interactions

## Methods: mild drug interactions

## Methods: measurements

- Observation of office processes
- Efficiency of refill process
- Survey:
- Theoretical framework of Information Technology Adoption Model (ITAM)
- Focus groups
- “Time trials” using scenarios
- Comparison handwritten and electronic prescription writing
- Time-to-complete task
- Assessment of completeness, legibility, errors
- Electronic prescriptions
- Chart review (Fall, 2007)

## Methods: experimental scenarios

- Instructions for time trials
- “For these scenarios, imagine that you’re with a real patient and work at your normal pace.”
- Example:
- “Next you see a regular patient, Abigail B. Cook.  She is 74-year old with a history of rheumatoid arthritis.  She also complains of pain when she urinates.  The urine culture reveals that the she has a UTI due to *E. coli *that is resistant to fluoroquinolones.  Prescribe an antibiotic to treat her UTI”

## Results: impact on workflow

- Medication refill system in one participating clinic before implementation of computerized clinic order entry

## Post-implementation of computerized clinic order entry

- Results

## Results: survey

- Response to one of the questions pertinent to patient safety:
- “Integrating drug reference and drug interaction look-up with prescription writing is useful”
- 57% (26 of 45) strongly agree
- (answered 6 or 7 on 7 point Likert scale)
- 9% (4 of 45) strongly disagree
- (answered 1 or 2 on 7 point Likert scale)

## Results: focus groups

- Favorable:
- “We see the interactions. That helps quite a bit. So, that has made a difference a couple of times. Essentially changed my mind...”
- “One thing I liked, that there was a medicine that I don’t prescribe very often and didn’t know about the dose...”

## Results: focus groups

- Unfavorable
- “it’s like getting those stupid things from the pharmacy.... It commented on too many menial, insignificant things that just waste your time.... Way too sensitive.”
- “... the drug to drug reaction says “major” and you Look at other... look at ePocrates later and no reaction at all.”

## Results: time trials 

## Results: drug interaction scenario

- Frequency with which trimethoprim/sulfa was prescribed to elderly woman on methotrexate for rheumatoid arthritis
- Handwritten
- 11 of 13 instances
- Computerized clinic order entry tool
- 3 of 12 instances
- Recommendation of the Multum database
- “Generally avoid”

## Results: electronic prescriptions

- Analysis of instances of significant drug-interactions is in progress
- Altogether:
- 430,000 electronic prescriptions
- 52,000 unique patients
- Coumadin interactions
- 7 patients received coumadin and amiodarone concomitantly
- 29 patients received coumadin and NSAID (excluding celecoxib)

## Summary and future directions

- Implementation **is** clinical care system redesign
- Measures of impact
- “Perceived usefulness” versus “average effect”
- “Experiment” versus “experience in the wild”
- “Errors” versus “adverse events”
- Transition to health information technology that enhances patient-centered care