# Presentation: 884132

## IMPROVING PATIENT SAFETYBY REDUCING MEDICATION ERRORS

**Brian L. Strom, M.D., M.P.H.**
**Professor of Biostatistics and  Epidemiology**
**Center for Clinical Epidemiology and Biostatistics**
**University of Pennsylvania School of Medicine**

## IMPROVING PATIENT SAFETY BYREDUCING MEDICATION ERRORS: THEME

**AHRQ: Center of Excellence for Patient Safety Research and Practice**
**Theme: Improving Patient Safety Through Reduction of Errors in the Medication Use Process**
**PRIME: Program for Reduction In Medication Errors**

## OVERALL APPROACH

**Focus - the occurrence of errors anywhere along the entire pathway of medication use  **
**Drugs with ubiquitous use, capacity to lead to errors, and severity of consequences**
**Different settings and populations**
**Both human psychosocial factors and technical system factors**
**Evaluations in sites prepared to rapidly implement the studies’ findings, which could then be evaluated in future studies**

## IMPROVING PATIENT SAFETYBY REDUCING MEDICATION ERRORS:OVERALL ORGANIZATION

**Four projects**
**Four cores:**
**Administrative Core**
**Data Collection Core**
**Biostatistics and Data Management Core**
**Dissemination Core**

## Project One: Medication Errors Leading to Hospitalization Among the Elderly (Joshua Metlay, MD, PhD--PI)

**Identify predisposing factors for hospitalization due to medication errors among the elderly**
**Develop a prediction rule to identify high risk elderly patients**
**Estimate costs associated with hospitalizations due to errors**

## DESIGN

**Prospective cohort**
**3 Drug Groups**
**Chronic vs. New Users**
**5 Study Cohorts (insufficient numbers for new phenytoin)**
**One year enrollment**
**Two year follow-up**

## HYPOTHESES

**Key risk factors include:  uncoordinated medical and pharmaceutical care, inadequate delivery of new medication instructions, visual and cognitive impairment, depression**
**Risk factors differ across types of drugs and new and old users**

## DRUG TARGETS

**Focus on warfarin, phenytoin, digoxin**
**High risk drugs, frequently implicated in ADEs leading to hospitalization**
**Narrow therapeutic windows lead to drug level monitoring**

## Medication Grouping Enrolled in Study (surveyed) N= 5569

| DRUG | COUNT | % |
| --- | --- | --- |
| Chronic Digoxin | 1659 | 29.8% |
| Chronic Phenytoin | 558 | 10.0% |
| Chronic Warfarin | 1923 | 34.5% |
| New Digoxin | 626 | 11.2% |
| New Phenytoin | 76 | 1.4% |
| New Warfarin | 727 | 13.1% |

## Cohort CharacteristicsN=5569

| Mean Age | 78 Yrs |
| --- | --- |
| Female | 78 % |
| Caucasian | 93 % |
| Widowed | 66 % |
| Lives in home
Lives alone
Education < HS
Self rated health fair/poor | 64 %
59 %
42 %
38 % |

## Source of Medication Instructions(N=5569)

| Received med instructions (3671) | 66 % |
| --- | --- |
| Source of instructions |  |
|      MD (34% of 3671) | 20% |
|      Pharmacist (44% of 3671) | 34% |
|      Both | 31% |

## Content of Medication Discussions with MD  (N=5569)

| 
MD asks you to bring in medications | 
24 % |
| --- | --- |
| 
MD asks about OTCs, herbs, vitamins | 
37 % |

## Medication Dispensing Patterns in the Home  (N=5569)

| Use multiple compartment pill box
 | 48% |
| --- | --- |
| Cut or split medication 

   Split pill with knife
   Split pill with hands

Set pills out to take each day

Someone else gives pills | 30%

40%
30%

61%

10% |

## Project Two: Predictors for PoorAdherence to Warfarin Therapy(Stephen Kimmel, MD, MSCE--PI)

**To determine the clinical, demographic, organizational, behavioral, and psychosocial predictors of poor adherence **
**To develop a predictive index that can identify patients at high risk for medication errors before starting therapy**

## PROJECT TWOSTUDY DESIGN

**Prospective cohort design, enrolling adult patients requiring warfarin who are treated at a outpatient pharmacist-managed Anticoagulation Clinic (AC)**
**Patients presenting to the AC clinic will be identified at the start of therapy and followed throughout their course**
**An addition to a funded NIH study designed to examine the effects of genetic polymorphisms and adherence on clinical outcomes (INR levels, bleeding, and thromboembolism)**

## To evaluate antibiotic monitoring practices that may predispose to ARF including:
The failure to use pharmacokinetic monitoring
Delays in initiating pharmacokinetic monitoring
Failure to implement recommendations from the pharmacokinetic monitoring service
Pharmacokinetic monitoring service characteristics/procedures systems

**The failure to use pharmacokinetic monitoring**
**Delays in initiating pharmacokinetic monitoring**
**Failure to implement recommendations from the pharmacokinetic monitoring service**
**Pharmacokinetic monitoring service characteristics/procedures systems**

**Project Three: Inpatient Medication ****Errors Leading to Acute Renal Failure****(Harold Feldman, MD, MSCE—PI)**

## PROJECT THREE STUDY DESIGN

**Hospital-based case-control study nested within a cohort of hospitalized patients receiving aminoglycosides **
**Cases of ARF (defined by elevations in serum creatinine) occurring among patients receiving aminoglycoside antibiotics to be compared to random sample of controls not experiencing ARF **
**Data collection: structured review of medical records and evaluation of their interaction with the pharmacokinetic monitoring service prior to the occurrence of ARF for the cases, or during an analogous exposure time for controls**

## Project Four: Medication ErrorsRelated to Workplace Stressors(Ross Koppel, PhD--PI)

**To determine if, and to what extent, the organization of work within a hospital, (e.g., schedules, shifts, workloads) affects houseofficers’ risk of medication errors **
**To determine if houseofficers’ experience of workplace stress affects the risk of medication errors**
**To determine how stressors interact with psychological profiles to influence the risk of medication errors**

## PROJECT FOURSTUDY DESIGN

**A  series of cross sectional studies **
**Data collection: **
**1) analysis of house-officers’ workloads, shifts, schedules; 2) surveys of houseofficers—at several points in their training—about workplace stressors and strain; 3) one-on-one interviews with housestaff, pharmacists, & IT staff; 4) focus groups; 5) observations on hospital floors, in pharmacy and meetings 6) psychometric personality inventory **

## PROJECT FOUR OUTCOMES 

**The “near misses” for medication errors detected by experienced pharmacists–in relation to houseofficers’ workloads, fatigue, schedules, rotations, shifts, experience, etc**
**Self-reported strains and errors in relation to workplace stressors **
**Analysis of the physician computer ordering system in preventing and in, perhaps, facilitating error**

## PROJECT FOUR QUALITATIVE SUMMARY OUTCOMES

**An emerging theme focused on the errors created by technological solutions designed to reduce errors**
**Several examples illustrate the unintended harms caused by the ****commercial CPOE system (TDS)  **

## AHRQ: Center of Excellence for Patient Safety Research and Practice

**Now that we have developed hypotheses for possible interventions, who will evaluate them????**

## Center for Excellence in Patient Medication Safety:Investigative Teams

**Brian L. Strom, MD, MPH****	****PI; Director, Admin Core; **
**	****	****Dir, Data Collection Core**
**Joshua Metlay, MD, PhD****	****Co-PI; Proj Leader, Proj 1; **
**	****	****Co-Dir, Admin Core **
**David Asch, MD, MBA ****	****Dir, Dissemination Core **
**Lily Cheung, PharmD****	****Co-investigator, Project 3**
**Abigail Cohen, PhD, MA****	****Senior Project Manager**
**Dean Cruess, PhD****	****Co-inv, Project 2 **
**John Farrar, MD, MSCE, PhD****	****Co-inv, Data Collection Core**
**Harold Feldman, MD, MSCE****	****Project Leader, Project 3 **

## Center for Excellence in Patient Medication Safety:Investigative Team

**Sean Hennessy, PharmD, ****	****Co-inv, Project 1, Project 3, **
**	**** ****MSCE, PhD ****	****	****Data Collection Core**
**Stephen Kimmel, MD, MSCE****	****Project Leader, Project 2 **
**Robert Gross, MD, MSCE****	****Co-inv, Project 2 **
**Ross Koppel, PhD****	****Project Leader, Project 4**
**Russell Localio, JD, MS****	****Dir, Biostats & Data **
**	****	****	****	****Management Core**
**Sandra Norman, PhD****	****Co-Dir, Data Collection Core**
**Daniel Polsky, PhD****	****Co-inv, Project 1**
** **

## METHODS OF PROVIDING MEDICATION INSTRUCTIONS

## Adherence to Warfarin Sodium Using Electronic Pill-Cap Monitoring and the Millon Behavioral Medicine Diagnostic Inventory

- Adherence assessed with multiple techniques, electronic monitoring and psychosocial instrument that predict adherence.
- _Electronic monitoring device _(MEMS caps):
- Makes use of a microprocessor in the cap of the pill bottle to record exact dates/times the bottle is opened
- A standard cutoff of > 25% of days on which the prescribed dose was not taken was used to designate non-compliant participants
- _A 140-item T/F questionnaire (MBMD)_ that produces a number of psychosocial indices, including Problematic Compliance
- Non-compliance was designated for participants with scores _>_ 75 on    the Problematic Compliance Index

## PROJECT TWOStudy Design & Participants

- 44 participants, (28 men, 16 women, mean age 51.5_+_14.7 years) beginning warfarin therapy at an anticoagulation clinic
- Indications for warfarin included thrombosis, atrial fibrillation/flutter embolism, and myocardial infarction.
- Pill cap adherence was monitored and averaged over the time of pill-cap  use (minimum of 7 days)
- Participants completed the MBMD inventory, along with other psychosocial measures towards the start of pill-cap monitoring

## PROJECT TWO DATA COLLECTION

**Data collection:  1) demographics, 2) clinical characteristics, 3) health-care structure characteristics, 4) pill taking practices, 5) psychosocial variables, 6) study outcomes**
**The primary outcome is adherence, to be measured using an electronic data monitoring system**

## PROJECT TWOResults

- Table 1. Adherence results

| Measure | Mean (SD) | Adherent* | Non-Adherent* |
| --- | --- | --- | --- |
| Pill-Cap Monitoring |  6.5+26.4%  |   56.8% |     43.2% |
| MBMD Problematic Compliance index |  63.11+28.5  |   61.4% |     38.6% |

- *  Adherent = _>_ 75% of days with correct dose taken for pill cap measurement  and MBMD scores of < 75 on the Problematic Compliance Index

- Table 2. Agreement of adherence results

|  | Adherent | Non-Adherent |
| --- | --- | --- |
| Adherent |          O=18
         E=11 |            O=9
           E=11 |
| Non-Adherent |          O=11
         E=11 |            O=6
           E=11 |

- (k=.062)

## PROJECT TWO Discussion

- While the pill-cap monitoring and MBMD problematic compliance index produced similar percentages of adherent and non-adherent participants, a closer examination of the data found that the two measures did not have adequate statistical agreement.
- It appears that the MBMD inventory assesses general medical compliance, while pill-cap monitoring limits compliance to pharmacological regimens.

## PROJECT THREE Schematic Protocol

**PROJECT THREE**** ****Schematic Protocol**

## PROJECT THREEAbstraction Form

## First Wave SampleDemographics (n=261)

**Female****	****	****	****44.8%**
**White****	****	****	****	****66.3%**
**Mean Age****	****	****	****29.6 yrs**
**PGY1****	****	****	****	****32.6%**
**PGY2****	****	****	****	****32.2%**
**PGY3+****	****	****	****	****35.2%**

## First Wave Specialty (n=261)

**Medicine****	****	****	****	****	****47.1%**
**Neurology****	****	****	****	****10.7%**
**General Surgery****	****	****	****10.0%**
**Emergency Medicine****	****	****09.6%**
**Ob/Gyn****	****	****	****	****	****06.9%**
**Otorhyno****	****	****	****	****06.1%**
**Family Medicine****	****	****	****05.7%**
**Urology****	****	****	****	****	****02.3%**
**Neurosurgery****	****	****	****	****01.5%**

## Computerized Order Entry Survey ResultsWave One (n=261)

|  | Never
% | <Once/wk % | >Once/wk % |
| --- | --- | --- | --- |
| Used COE determine low dose | 27.3 | 34.6 | 38.1 |
| Use COE range dose | 18.5 | 40.4 | 41.2 |
| Difficult to know/see all meds | 28.5 | 25.4 | 46.1 |
| Due to COE not DC’d meds | 48.6 | 29.0 | 22.4 |
| Due to COE delay antibiotic | 16.9 | 43.5 | 39.6 |

## Computerized Order Entry Survey ResultsWave One cont. (n=261)

|  | Never% | <Once/wk % | >Once/wk % |
| --- | --- | --- | --- |
| Due to COE difficulty telling who order for | 45.4 | 32.3 | 22.3 |
| COE down, delay in ordering | 8.5 | 45.0 | 46.5 |
| No available COE terminal, delay in order | 11.5 | 30.5 | 58.5 |
| COE system inflexible | 8.5 | 37.1 | 54.4 |

## CPOE vs. Paper-Based Systems:The Benefits of CPOE (part 1)

**1) free of handwriting identification problems**
**2) faster to reach the pharmacy**
**3) less subject to error associated with similar drug names**
**4) more easily integrated into medical records and decision support systems**
**5) less subject to errors caused by use of apothecary measures**
**6) easily linked to drug-drug interaction warnings**
**7) more likely to identify the prescribing physician**
**8) able to link to ADE reporting systems**

## CPOE vs. Paper-Based Systems:The Benefits of CPOE, Part II

**9) able to avoid specification errors, such as trailing zeros**
**10) available and appropriate for training and education**
**11) available for immediate data analysis, including post marketing reporting. **
***Also, with on-line prompts CPOE systems can:***** **
**12) link to algorithms to emphasize cost-effective medications; **
**13) reduce under-prescribing and over-prescribing; and **
**14) reduce the incorrect choice of drugs.**

## Increasing Interest in CPOE

**CPOE adoption has, perhaps, gathered such strong support because its promise is so great, the effects of medication error so distressing, the circumstances of medication error so preventable, and the studies of CPOE so reassuring, albeit preliminary.**

## Our Study’s Genesis

**Project objective: The role of hospital workplace stressors (e.g., shifts, sleeplessness, new rotations) on housestaff medication prescribing errors.**
**The CPOE system emerged as a study focus when housestaff repeatedly told us it caused stress and error **

## Our CPOE Study Methods:

**Face-to-face intensive interviews with physicians **
**5 focus groups with physicians**
**Shadowing docs as they entered orders and at handoffs**
**Executive interviews with leaders of: Nurses, Medicine/Surgery, Pharmacy, & IT**
**Face-to-face interviews with nurses and nurse-managers**
**Shadowed nurses and pharmacists processing orders, interacting with CPOE system**
**72 item questionnaire to 90% sample of housestaff (9 questions about CPOE)**

## Measuring Success

**CPOE systems are currently found in only 5% to 9% of hospitals**
**CPOE systems’ efficacy (17 to 81% error reduction) usually focus on their advantages and are generally limited to single outcome studies, potential error reduction, or physician satisfaction**

## PROJECT FOUR QUALITATIVE SUMMARY OUTCOMES -1

**Sometimes serious delays were caused because of the re-approval process for antibiotics done by infectious disease fellows**
**While a sticker is placed on chart the day before a renewal is needed, most rely ****on the computer system (TDS) which may not be updated due to the difficulty of working with the system to re-enter data**

## PROJECT FOUR QUALITATIVE SUMMARY OUTCOMES-2

**Doctors sometimes use computer displays to judge lowest dose & range of doses **
**TDS is not designed to illustrate dose or range information for clinical decisions**
**The dosages displayed reflect purchasing and warehousing considerations by the pharmacy**
**Computer gives a false sense of accuracy**** **

## PROJECT FOUR QUALITATIVE SUMMARY OUTCOMES-3

**Doctor must go though 15 or more computer screens to discontinue a medication causing problems with missed information**
**If houseofficer is interrupted or in hurry, process maybe postponed and forgotten  **

## PROJECT FOUR QUALITATIVE SUMMARY OUTCOMES-4

**Nurses often don't record the administration of medications in the TDS system due to cumbersome and time consuming nature of interface**
**Doctors must assume medications given when ordered or find nurse and ask **
**System is updated only at the end of a shift to reconcile records**
**Accuracy questionable**

## PROJECT FOUR QUALITATIVE SUMMARY OUTCOMES-4

**When a patient enters OR all medications stopped **
** ****Surgeons must enter continuing and new medications into TDS for use after post-op **
**When patient released from the post anesthesia care unit, the nurse “pre-activates” meds**
**But doctor must re-re-activate meds   **
**This last step is too often forgotten**

## PROJECT FOURPROGRESS AND PRELIMINARY FINDINGS

**Creation and administration of house officer stressor inventory**
**Findings on differing causes of stress vs. causes of medication error**
**Findings on the role of CPOE systems as facilitator of error (in addition to its preventive role)**
**Findings of greater stress and error among residents compared to interns**

## Findings: 22 medication error risks facilitated by CPOE  

**I) *****Information Errors***** generated by fragmentation of data and failure to integrate the hospital’s several computer and information systems**
**II.  *****Human-Machine Interface Flaws*****:  Reflecting machine rules that do not correspond to work organization or usual behaviors**

## Ex: Assumed Minimum Dose &Assumed Dose Range Information

**73% of housestaff use CPOE displays to determine *****low doses***
**82% used CPOE displays to determine *****range of doses***
**40% used CPOE to determine dosages at least a few times weekly; 10 to 14% daily**

## Ex. Patient Selection--Screen Design;& Inconsistent Color/Font Coding 

**55% of housestaff: Difficulty “identifying the patient they were ordering for” because of fragmented CPOE displays **
**23% say this happened a few times weekly or more frequently**

## Our RECOMMENDATIONS concentrateon organizational factors:

**Focus primarily on the organization of work; not on technology.  CPOE must only determine clinical actions if they improve care.**
**Aggressively examine the technology *****in use*****. Problems obscured by workarounds, medical problem solving ethos, and low housestaff status.    **
**Substitution of technology for people is a misunderstanding of both. Aggressively fix technology when shown to be counter-productive. Failure to do so engenders alienation and dangerous workarounds.**

## Our RECOMMENDATIONS concentrateon organizational factors:

**Episodic and incomplete error reporting are standard. Management belief in these reports obfuscates and compounds problems. Pursue errors’ “second stories.”**
**Plan for continuous revisions and quality improvement; recognizing that all changes generate new error risks.**

## CPOE: Tremendous Promise

**But do not bend hospitals and clinicians around the CPOE system; make CPOE work with other systems and with clinicians.   **
**Our future research: New CPOE systems may be better, but face the same and new challenges of integration with workflow, humans, and organizations.**