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Patients, Doctors, and Information Technology: Clinical Decision Support at Brigham and Woman’s Hospital and Partners Healthcare. 김석일. Contents I. 5.1 History 5.2 Clinical Decision Support and Inpatient CPOE at BWH 5.2.1 Medication-related Decision Support 5.2.2 Laboratory Interventions

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  1. Patients, Doctors, and Information Technology: Clinical Decision Support at Brigham and Woman’s Hospital and Partners Healthcare 김석일

  2. Contents I • 5.1 History • 5.2 Clinical Decision Support and Inpatient CPOE at BWH • 5.2.1 Medication-related Decision Support • 5.2.2 Laboratory Interventions • 5.2.3 Radiology Interventions • 5.2.4 Signout • 5.2.5 Assessment of Satisfaction with CPOE • 5.2.6 Impact of CPOE on Provider Time

  3. Contents II • 5.3 Decision support Delivered using The Outpatient Electronic Health Record • 5.3.1 Medication-related Decision Support • 5.3.2 Laboratory Interventions • 5.3.3 Radiology Interventions • 5.3.4 Impact on Provider Time • 5.4 Overarching Studies • 5.5 Overarching Lessons • 5.6 Future Directions

  4. Introduction A pioneer in the development of clinical information systems and the implementation of clinical decision support within the US. In the mid-1980s, to develop its own clinical information system(in-house production) The core of the BWH mission and leadership : High-quality, safe, cost-effective care A key tool in achieving that vision : Clinical information systems (Richard Nesson, MD, the BWH CEO )

  5. 5.1 History • The BWH clinical information systems • Warner Slack, MD, and Howard Bleich, MD, • Initial version • Brigham Integrated Computer System (BICS) • MUMPS-based system • Very Physician-friendly and it contained a high proportion of all clinical results. • In 1993 • Computerized Physician Order Entry (CPOE) : John Glaser, PhD, decided to implement • Jonathan Teich, MD, was the primary architect of the application • Designed it to be fast, Adaptable to clinicians’ workflow and Enabled with real-time clinical decision support. • The entire design and implementation team was very clinically oriented (Cynthia apurr, RN)

  6. 5.1 History • In 1989 • Miniamb (outpatient electronic medical record application) : Teich • Problem list with uncoded problems, Medication list, allergy list, visits, and note • free text, Initially, almost no decision support was included • In 1997 • Longitudinal Medical Record (LMR) • Visual Basic platform(with the mumps DB)  Web front end (Web version) • Now, all development is done on the web version (which is being widely deployed across the partners network) • In 1996 • Partners Health System : integrated delivery system ( included both BWH & MGH)

  7. 5.1 History Figure 5-1 Longitudinal Medical Record(LMR) summary page. An important part of clinical decision support is simply making available the key information that the clinician needs. Here, the clinician can see at a glance that the only reminder active for this visit is that the patient should receive a flue shot.

  8. 5.1 History • Very Recently • Signature Initiatives : Dr. James Mongan (the current CEO of partners) • Mongan recognized that for partners to demonstrate leadership and excel in the areas of quality, safety, and efficiency, information systems. • Tremendous variability among the various partners institutions in this area. (Figure 5-2) • The first of six partners initiatives in this effort • Implementing inpatient CPOE at all partners hospitals • Encouraging all partners physicians to begin using an EHR

  9. 5.1 History Figure 5-2 Implementation of clinical systems at Partners Healthcare in 2005. This matrix illustrates the large number of systems in place, and the heterogeneity of systems that are in the Partners network, despite conscious efforts to avoid heterogeneity. This illustrates why efforts to implement standard clinical decision support across a network like Partners is so challenging.

  10. 5.2 Clinical Decision Support and Inpatient CPOE at BWH • Computerized Physician Order Entry (CPOE) • Referential knowledge, anticipated needs, alerts, reminders, order sets, guidelines and feedback • Referential knowledge • Passive • A bank of calculation tools (The Cockcroft-Gault equation) • The Reference Library is available on the network • Anticipated needs • A more productive approach • To anticipate clinician needs • BICS Order Entry • The application suggested starting does for Medications

  11. 5.2 Clinical Decision Support and Inpatient CPOE at BWH • Alerts • Checking Drug allergies • Drug-drug interactions • Reminders • Corollary orders • Order sets • Considerable number of order sets • Time-motion study : submitting groups of orders was five times faster than writing them individually

  12. 5.2.1 Medication-related Decision Support • Improve medication safety • Phase 1. • Adverse Drug Event Prevention Study (observational) • Goal : to describe the epidemiology of Preventable ADEs and Potential ADEs in Hospitalized patients • Finding • 60% of Serious medication errors occurred at the prescription or transcription stages • Serious prescribing errors appeared to be related to access to Knowledge. • Phase 2. • The ADE Prevention (two interventions) • Two interventions • Team-based intervention • Administration and dispense stage (no effect) • CPOE (effective) • reduced the serious medication error rate by 55% (despite minimal decision support) • much more decision support (comprehensive drug-allergy module, comprehensive drug-drug interaction checking and more minor changes)

  13. 5.2.1 Medication-related Decision Support • The cumulative impact of these serial changes was studied – primary outcome • Time series design (one-year intervals) • first year : Computerization of prescribing -> overall Medication error rate (fell 83%) • in the next year : more comprehensive allergy checking added -> rose Slightly (unclear reasons) • final year : two main changes (comprehensive drug-drug interaction checking and elimination of a problem  drop)

  14. 5.2.1 Medication-related Decision Support • Two of the most important additions • Special medication dosing for patents who have renal compromise • The renal dosing application (Nephros) : utilize existing data / Crockcroft-Gault calculation / suggests a drug dose appropriate for the patent’s level of renal function • Controlled trial • the appropriate dose was selected 67%(Intervention G) vs 54%(control G) • Dosing frequency : 59%(Intervention G) vs 35%(control G)

  15. 5.2.1 Medication-related Decision Support Figure 5-3 Renal dosing. The clinician is ordering acetaminophen, for which the clinician should adjust the dose based on the patient’s level of renal function. If the clinician chooses to see how the dosing suggestion was reached, he or she can click on a button and see the Cockcroft-Gault calculation.

  16. 5.2.1 Medication-related Decision Support • Two of the most important additions • Special medication dosing for Geriatric population • The major problem : High initial dosage for Geriatric patients • Initial dosing recommendations for psychoactive medications • result • got the recommended dosage (29% vs 19%) • lower rate of tenfold overdose (2.8% vs 5%) • less likely to fall (2.8% vs 6.4% falls per 1000 patient-days)

  17. 5.2.1 Medication-related Decision Support Figure 5-4 Drug-age or Gerios dosing support. Here, the clinician is ordering ibuprofen, and the default initial dosing frequency has been set to 400mg.

  18. 5.2.1 Medication-related Decision Support • Another approach • to Implement drug-specific guidelines for a number of medications ex) vancomycin

  19. 5.2.2 Laboratory Interventions • To improve the appropriateness of use of the clinical laboratory • Randomized controlled trial evaluating the impact of charge display – “cash register” function • Physicians liked seeing the charges, but no statistically significant impact. • The intervention group showed a beneficial trend of 4.5% (fewer tests performed). • The annual estimated cost reduction benefit in the intervention group was $1.7 million, so the institution elected to continue to display charges.

  20. 5.2.2 Laboratory Interventions • Regarding redundant tests, • Substantial unnecessary utilization (amounted to an estimated $930,000 per year in charges.) • 24% of the time in the intervention group vs 51% of the time in the control group • The saving realized (mere $35,000 vs a prior projection of $436,000) • Only 44% of the redundant tests performed were ordered by computer. • 31% of the reminders were overridden. • Also half of the orders were not screened for technical reasons. • Two take-away messages • first : to design “closed-loop” systems to ensure inclusion of all utilities. • second : to include in the screening those orders that are in order sets.

  21. 5.2.2 Laboratory Interventions • Antiepileptic drug level testing was targeted • Appropriate antiepileptic drug level : only 26 -29 percent (depending on the drug) • intervention group : 19.5% decrease in the use of these levels. (despite a 19.3% increased in overall test volume during the study period.)

  22. 5.2.3 Radiology Interventions • In the inpatient setting, nearly all radiographs have been ordered electronically since the initial implementation of CPOE. • Asked to enter coded historical findings and the clinical question (simply computerizing the process) • In the one trial of decision support for abdominal radiographs. • Suggestions were much more likely to be accepted (about half the time) • Displaying the charges had no impact on the overall level of utilization

  23. 5.2.4 Signout • Inpatients being cross-covered by another physician had nearly a fivefold increase in risk of suffering an adverse event • The development of Signout(application) • Information (medication list, key recent laboratory tests) & code status are abstracted • Providers are asked to add additional data (problem list, a description of the hospital course) • An evaluation of impact this application • The additional risk associated with being cross-covered was eliminated.

  24. 5.2.5 Assessment of Satisfaction with CPOE • Formal study of the impact of CPOE on provider satisfaction • No correlation between satisfaction and provider age • Physician and nurses were quite satisfied with the application overall • Internists were more satisfied than surgeons. • Satisfaction was highly correlated with the perceived impact of CPOE on productivity (ease of use, Speed, Quality of care) • This Suggests that Decision support • must be fast to be tolerated • confirms that users may not perceive the need to improve quality

  25. 5.2.6 Impact of CPOE on Provider Time • In a formal time-motion study • Interns spent their time ordering • Before : 2.1 % • After implementation of CPOE : 9% (although CPOE saved them additional 2 % of time because they spent less time looking for charts, and could write orders from remote locations)(The net difference was 5% of their total time) • However, this counterbalanced by decreased time required by other personnel such as nursing and pharmacy. • The impact on time of any decision-support related intervention must be considered carefully.

  26. 5.3 Decision support Delivered using The Outpatient Electronic Health Record Figure 5-5 Drug-pregnancy alert. A number of drugs should never be used if a woman is pregnant, and many more are relatively contraindicated. For Accutane, the restrictions are especially strict; a negative pregnancy test is required if the woman is of child-bearing age. From the informatics perspective, the most challenging part of delivering the alert appropriately was determining whether a woman is pregnant.

  27. 5.3.1 Medication-related Decision Support • improving the acceptance of medication-related alerts. • Alerts were divided into those • that were clinically important enough to make them interruptive, • with the reminder classified as noninterruptive or informational. • Results • 18,115 drug alerts(71% noninterruptive/29% interruptive) • Of the interruptive alerts(67% were accepted) • Some of keys to this success • highly selective in which alerts to display • iterating to identify alerts with high override rates • using the interruptive approach only for truly important alerts

  28. 5.3.1 Medication-related Decision Support Figure 5-6 Drug-drug interaction. This drug-drug interaction is a Level 1, which means that the clinician is not allowed to bypass it. The clinician’s only option is to discontinue one of the drugs. There are very few such drug-drug interactions.

  29. 5.3.1 Medication-related Decision Support 80% of the drug-allergy alerts were overridden. Only 10 % of alerts were triggered by an exact match between the drug prescribed and allergy listed. A group of recommendations was developed fine-tune the specificity of warnings, thereby increasing the utility of the allergy alerting system(figure 5-7)

  30. 5.3.1 Medication-related Decision Support Figure 5-7 Drug-allergy alert. Here, the patient has a prior rash to sulfa, and trimethoprim/sulfamethoxazole has been ordered. This warning is a Level 2, which means in these systems that the alert is interruptive, and that the clinician must provide a reason for overdosing, but is allowed to do so.

  31. 5.3.2 Laboratory Interventions • Less laboratory-related decision support for ordering has been in the outpatient setting • Since BWH still has not implemented computerized laboratory ordering for ambulatory patient. • However, many reminders to monitor for specific medications. • Another key issue is follow-up of abnormal results, which is often suboptimal. • Third of abnormal test results. (do not receive follow-up) • Result Manager • Dr. Eric Poon led the development of a tool. • Make it for physicians to handle test results. • Use application has grown rapidly, as it has been very popular with clinicians • Tests ordered during a hospitalization for which reports come back after the patient has left the hospital, as a result of which they may not be valuated by physician.

  32. 5.3.2 Laboratory Interventions Figure 5-8 Result Manager. This screen shows what a physician sees when he or she is reviewing a patient’s test results’ the results are prioritized according to how abnormal they are, and the clinician can generate a letter about them with only a few clicks.

  33. 5.3.3 Radiology Interventions • Electronic ordering and mapping of all key historical factors and indications for radiographs are in place. • In one such study, • Patients who underwent abdominal imaging for abnormal liver function tests • All modalities evaluated. (CT scan had the highest yield) • Unexpected new findings that appeared to be clinically important were found in higher proportion of patients than anticipated.

  34. 5.3.4 Impact on Provider Time • Time-motion study by pizziferri • EHR neither significantly decrease nor increase clinic time for primary care physicians. • The results contradict a major barrier to EHR adoption, which is the perception that converting to EHR is slower for clinicians than using the paper-based status quo. • Majority of providers studied believed that EHRs would increase quality of care, access, communication. • Additional studies, however, are needed to examine an EHR’s impact on a provider’s nonclinic activities, such as post-visit documentation.

  35. 5.4 Overarching Studies • The cost-effectiveness of computerized physician order entry (CPOE) • CPOE cost $1.4millon • Software $900,000 • Hardware $500,000 • The benefits in terms of charges (drug savings, AE prevention, more appropiate use of the clinical laboratory) • came to $5 to $ 10million annually.

  36. 5.4 Overarching Studies • The cost-effectiveness of implementing he outpatient electronic health record • The net financial benefit and costs for a primary care provider over a five-year period. • Benefit was $86,400 per provider • Savings related to fewer drug expenditures, more appropriate utilization of radiology tests, better capture of charges, and decrease in the rate of billing errors. • These data suggest that high-yield areas to focus on when selecting decision support for an Electronic health record • Drug cost suggestions, radiology and laboratory recommendations • Especially in settings where more appropriate usage affects physician reimbursement.

  37. 5.5 Overarching Lessons • A summary of the lessons • “Speed is everything.” • “Anticipate needs and deliver in real time.” • “Fit into the user’s workflow.” • “Little things can make a big difference.” • “Physicians resist stopping.” • “Changing direction is fine.” • “Simple interventions work best.” • “Ask for additional information only when you really need it.” • “Monitor impact, set feedback, and respond.” • “Manage and maintain your knowledge-based systems.”

  38. 5.5 Overarching Lessons Table 5-1 Ten commandments for effective clinical decision support.

  39. 5.6 Future Directions • Many additional challenges remain -> How to deliver clinical decision support for complex conditions • Chronic disease in the outpatient setting • RCT in this area are being conducted using “Smart forms”. • Three of the initial conditions being • Acute Respiratory Infections • Coronary Heart Disease (CHD) • Diabetes • Smart forms : documentation tools that incorporate decision support

  40. 5.6 Future Directions • Another key challenge is achieving much higher levels of performance for specific measures. • Clear-cut advantages of using specific piece of decision support, substantial room for improvement remains, for example with renal dosing • Many novel opportunities to deliver decision support in the in patient setting, especially as coded documentation becomes available. • Partners • Rapid and easy access will make it possible • To virtually assess a patient’s stability based on vital signs information • To more accurately assess a patient’s mental status using nursing notes. • Overall, Clinical decision support has the potential to revolutionize clinical care in the coming years, • but many lessons remain to be learned about • What best to deliver. • How to deliver it.

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