6/14/2010. Clinical Decision Support: Applied Decision Aids in the Electronic Medical Record. Addressing high risk practices

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1 Clinical Decision Making in Emergency Medicine Ponte Vedra 2010 Evidence based decision support Clinical Decision Support: Applied Decision Aids in the Electronic Medical Record The ED as a high risk settings Reducing gaps between evidence and practice Improving medical decisions and reducing risk Types of decision support The Ten Commandments of decision support Michael VanRooyen, MD, MPH Brigham and Women s Hospital Department of Emergency Medicine Harvard Medical School Harvard School of Public Health Addressing high risk practices Why is the ED a high risk Environment? Many medical errors originate from process breakdown and variability in decision making. Decision analysts have used the airline industry as a model for improving decision making, but the work flow process flaws in the ED are different, accentuated by: High acuity High volume High rate of interruptions Variability in decisions to be made High risk settings and practices Transition of care Data retrieval (laboratory, radiology) Consultant communication Provider variability Intellectual anchoring Provider Variability Non-use of practice standards and clinical guidelines Lack of awareness of guidelines Available guidelines are: Non-standard formatting Not readily available Not always evidence based No monitoring of provider compliance 1

2 Intellectual Anchoring Practitioner investment into a thought pattern Difficulty breaking intellectual pattern, despite available data and information Often takes an external stimulus to break this cycle Prompts and cues Charting formatting Intelligent questioning Intellectual Anchoring: Example Dr. Johnson sees a 70 year old male with chest pain. The pain is atypical, radiating to the neck and back. The patient is a smoker, hypertensive and has a prior HX of CAD. The pain responded partially to nitrates, but the EKG is normal, and the CXR reveals slight bilateral pleural effusions but no acute CHF. He discusses the case with the admitting cardiologist, treats the patient with morphine, aspirin, heparin bolus and infusion and beta blockers, and admits the patient to a monitored bed for probable ACS. Then The patient arrives on the floor, moves over to the bed, and develops severe, tearing chest pain, diaphoresis and near syncope. The nurse gives a fluid bolus, and calls the cardiologist. Before the cardiologist arrives, the patient has a syncopal event, has a cardiac arrest. EKG reveals asystole, and CPR is unsuccessful. The patient expires 35 minutes after arriving to the floor. Post mortem reveals an acute thoracic aortic dissection. So what s the problem Is this an atypical presentation of a rare disease? NO Is diagnostic testing unavailable in the ED? NO Is the physician unaware of the symptoms of dissection? NO Is there lack of available data or info about aortic dissection? NO Improving medical decisions and reducing legal risk Did the MD miss it? YES Why? Anchoring bias 2

3 Improving medical decisions: The EMR as a decision support tool The electronic medical record can assist in improving typical high risk areas by: Template driven documentation Links to order sets Lab and radiology reporting mechanisms Quality assurance feedback Practice lags behind evidence base An average of five years between published guidelines and widespread adoption Many guidelines, even broadly accepted ones, are not routines followed 50% of eligible patients do not receive beta blockers in AMI) 28% of labs are ordered too soon after a prior test to be useful But what about real time decision making? Lomas J, Sisk JE, Stocking B. From evidence to practice in the United States, United Kingdom and Canada. Millbank 1993; 71: The utility (and peril) of pre-formatted charting Chief complaint driven charting can: Improve documentation and billing Guide the clinician in essential documentation Link to order sets But template driven documentation can also further lead to intellectual anchoring by leading the user down a specific path chosen based on incomplete data. How clinical decision support can help Decision making resources for clinicians in the patient care setting come in a variety of flavors and includes real-time availability of medical references, tools, decision tools and patient safety checks. Three tiers of clinical decision support First Tier: Long reach Second Tier: Short reach Third Tier: Push 3

4 First Tier MICROMEDEX InfoButton Access Examples of 1 st tier information include: Textbooks and journals Palm-based information resources Online access to resources Micromedex, MD Consult, e-medicine Dxplain: differential diagnosis generator This case was from the New England Journal of Medicine, June 2, 2005 Clinical Problem Solving exercise. For all cases presented to DXplain, you should provide the patient's age and gender and a rough estimate of the duration of the disease. Scoring tools Scoring systems 4

5 Problems with first tier information Data is still in long format, not summarized Not tailored to ED patient and clinical decisions Too much data for real time use Second Tier Examples of 2 nd tier information include: Charting prompts (ie: PE risk factors in SOB pts) Information at your fingertips Risk scores, clinical reminders Chief complaint linked tips Third Tier Examples of 3rd tier information include: Real time synthesis of information and flags (example: allergy checks, duplicate med orders) Automated risk scoring on the fly Critical value prompts at discharge Problems with decision support aids External to system Non-topically integrated Require non-work related data entry Data is in long format, not summarized Not tailored to ED clinical decisions Impractical for real-time clinical use (particularly in urgent or emergent care) How can the EMR be used to improve decision making? 1. Speed is Everything Decision support may contain the right information, but unless it is instantly available. EMR users ranked speed and accessibility as more important than formatting and actual content. Physician adoption (or rebellion) of Ed order entry is directly related to the time it takes to navigate the system, measured in new screens and field clicks. The Ten Commandments of Clinical Decision Support 5

6 2. Anticipate needs and deliver in real time 3. Fit into the users work flow Applied decision tools must be in front of a clinician at the right point in the decision making process. Decision aids must be linked carefully to the specific point in decision making to alter behavior, such as: Just before ordering an lab or radiology study During the documentation of the HPI Medical decision making portion of the chart Alerts, guidelines, scores and algorithms must be very strategically placed in the cognitive path of the provider. Stand alone guidelines are used rarely if the clinician has to reach for it. Clinical workflow must be very well understood. MDM typically too late, unless using summary and rescue knowledge Latent needs: those not consciously realized by the provider 4. Little things can make a big difference Minor alternations in screen appearance, language, color schemes and balance between free text and click charting can make a huge difference in user satisfaction and adoption. 5. Physicians will strongly resist stopping Physicians will rebel against interruptions in workflow and attempt a work around if possible. As it turns out, interruptions are bad for decision aids as well, and prevent either their effectiveness or adoption. 6. Changing direction is easier than stopping Getting physicians to change direction with out interrupting workflow is the key to ease of adoption and effective behavior change. This can be done by changing defaults in lab ordering and order sets to suggest first line med choices and doses. 7. Simple interventions work best Avoid requesting several new entries or additional pieces of input in order to evoke a decision rule. Making simple suggestions in abbreviated language can be far more effective that invoking evidence and probabilities real time. 6

7 8. As for additional information only if needed 9. Monitor impact, get feedback As with number 7, decision aids often fail when there is a need for new info or specifically formatted data that should be available elsewhere. TIMI scores, PORT scores and other scores fail when clinicians are asked all over a gain to fill out a score. It is much more effective (although technically challenging) to extract data from the record real time and request only those fields necessary to complete the scoring. Tracking clinician usage will provide some surprising results, and getting feedback from users, however demoralizing, can lead to more creative and useful solutions in decision support formatting. Impact of decision aid in the EMR can be a success or failure because of some unpredictable issues. 10. Manage and maintain knowledge based systems: Essential to have material up to date and reflecting current medical decision making. This applies to all disciplines, and many require frequent adaptation of content. This is a big problem for slower adapters in the EMR 7