IMPROVING PATIENT SAFETY BY REDUCING MEDICATION ERRORS

1. IMPROVING PATIENT SAFETYBY 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

2. IMPROVING PATIENT SAFETY BYREDUCING 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

3. OVERALLAPPROACH • 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

4. IMPROVING PATIENT SAFETYBY REDUCING MEDICATION ERRORS:OVERALL ORGANIZATION • Four projects • Four cores: • Administrative Core • Data Collection Core • Biostatistics and Data Management Core • Dissemination Core

5. 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

6. 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

7. Project Two:Predictors forPoorAdherence 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

8. PROJECT TWOSTUDY 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)

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

10. 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

11. Project Four: Medication ErrorsRelated 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

12. PROJECT FOURSTUDYDESIGN • 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

13. 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

14. 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)

15. Findings: 22 medication error risksfacilitated 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

16. 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

17. 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

18. Our RECOMMENDATIONS concentrateon organizationalfactors: 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.

19. Our RECOMMENDATIONS concentrateon organizationalfactors: 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.

20. 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.

21. 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

22. 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

23. 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

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

25. Medication Grouping Enrolled in Study (surveyed) N= 5569

26. Cohort CharacteristicsN=5569

27. METHODS OF PROVIDING MEDICATION INSTRUCTIONS

28. Source of Medication Instructions(N=5569)

29. Content of Medication Discussions with MD (N=5569)

30. Medication Dispensing Patterns in the Home (N=5569)

31. 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

32. PROJECT TWOStudy 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

33. 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

34. PROJECT TWOResults Table 1. Adherence results * Adherent = > 75% of days with correct dose taken for pill cap measurement and MBMD scores of < 75 on the Problematic ComplianceIndex Table 2. Agreement of adherence results (k=.062)

35. PROJECT TWODiscussion • 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.

36. Clinical courseprior to risk period T0 T24 T48 • Risk Period • Continues through earlier of • - ARF • - 3 days after discontinuation of AG • - death • - discharge Minimum AG drug exposure PROJECT THREESchematic Protocol

37. PROJECT THREEAbstraction Form

38. First Wave SampleDemographics (n=261) • Female 44.8% • White 66.3% • Mean Age 29.6 yrs • PGY1 32.6% • PGY2 32.2% • PGY3+ 35.2%

39. 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%

40. Computerized Order Entry Survey ResultsWave One (n=261)

41. Computerized Order Entry Survey ResultsWave One cont. (n=261)

42. 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

43. 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.

44. 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.

45. 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

46. 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)

47. 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

48. 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

49. 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

50. 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