Information Management in EMR

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1 Information Management in EMR Data Standards, Data Cleaning & Data Discipline CEM Rounds, April 15, 2008 Karim Keshavjee MD, MBA, CCFP InfoClin Inc All Rights Reserved.

2 Learning Objectives What are the challenges for setting data standards in EMRs? How should dirty data be cleaned in an EMR? How does one maintain data discipline in an EMR? Implications for the next generation design for EMRs

3 Outline Background Problem Data issues Cleaning data Maintaining data discipline EMR deficiencies EMR future design

4 Background Increasing need for managing patient populations Increasing burden of chronic disease Increasing use of screening programs (cervical cancer, breast cancer, colon cancer) Increasing importance of vaccination Government pay-for-performance programs are a key driver of change Family physicians can increase revenues by $30-90K per annum with current incentives

5 EMRs are ineffective Linder et al, Archives of Internal Medicine Retrospective study of 1.8 billion ambulatory visits 18% were in EMR Measured care across 17 quality indicators Antibiotic rx, preventive counselling, screening, avoid inappropriate meds in elderly 14 measures showed no difference 2 areas were better, 1 was worse Overall assessment, EMRs are ineffective in improving patient care

6 The EMR Struggle 25% Efficient Users 40% Inefficient Users 100% Adopted EMR N=112 35% Back to Paper

7 Problem EMRs are optimized for individual patient care Documentation within a patient is quite good Aggregates data from a variety of sources and puts it into one place EMRs are not designed for managing a deluge of paper EMRs are not designed for population-based care Data capture is not standardized Standard terminology is poorly enforced Meta-data is poorly captured (can put data in wrong place ) Data inconsistency is rampant Many patients with HbA1c > 7 are not labelled as diabetic Data inaccuracies abound Many patients with diagnosis code 250 are not diabetic Query tools cannot handle complex queries required for population management

8 Examples of Data Issues #1 Text based EMRs require careful data collection Smoking status is a great example Lots of ways of saying patient doesn t smoke: Non-smoker, ex-smoker, never smoked, quit smoking, smoking = 0, smoking: no, smoking: quit All of these are have the text smok in them, yet all imply non-smoker It is not possible to have queries that capture the heterogeneity and provide clean results i.e., separate the smokers from the non-smokers Certain words should not be used: e.g., quit means a patient attempted to stop at some point, not that they are an ex-smoker Queries that have many exclusions are prone to error There is a need to clean data to be confident in quality

9 Examples of Data Issues #2 Some diseases are prone to inconsistencies in data collection Diabetes is a good example Many patients have a HbA1c >7% or a prescription of insulin and yet are not labelled as being diabetic Many patients have a billing code of 250 but are not diabetic Many patients have a text diagnosis of dm or diabetes that is not coded Queries that attempt to create lists on the basis of inconsistent data or un-coded data are prone to error E.g., patients on metformin cannot be reliably identified as diabetic, patients with HbA1c >7% could have gestational diabetes There is a need to clean data

10 Setting Data Standards Setting data standards is difficult Need to balance feasibility vs utility Can t ask people to go back and recode all their dm patients to diabetes type 2 But they do have to code all text diagnoses Cannot clean data without agreeing on what you will code to

11 Cleaning Data Cleaning data is tedious and laborious But necessary for clinical decision support and for population management Cleaning data for different diseases requires different strategies E.g., For diabetes, need to code all patients labelled with the diagnosis and then find all unlabelled patients and code them as well E.g., For smoking, need to clearly label smokers and non-smokers. Some people like to say never smoked vs ex- E.g., For INR, need to clearly have active vs inactive anticoagulation prescription

12 Maintaining Data Discipline Once cleaned, the database starts to get dirty again People forget to use their coding system People forget to label a patient explicitly People forget to inactivate a medication People forget the underlying logic of information storage

13 Interpretation Humans are chronically inconsistent Will continue to prescribe glyburide and forget to label the patient as diabetic Humans continue to deviate from accepted terms Humans forget to change the status of information Forget to make a warfarin prescription inactive Medical knowledge and terminology keeps evolving Juvenile vs. adult onset diabetes IDDM vs. NIDDM Type 1 vs. Type 2 diabetes There is a need for better information management tools

14 Querying EMRs EMRs lack sophisticated query tools EMRs cannot return lists of patients unless data is properly cleaned and maintained EMRs cannot generally provide statistics on patients E.g., % of patients with diabetes or % of diabetic patients with uncontrolled hypertension To do this, you would need to run multiple queries and collate the data manually EMRs do not generally allow for complex patient list management

15 Implications for EMR design If EMRs want to be better at information management, they need the following: Tools that can help prevent data inconsistencies or detect, present and resolve data inconsistencies E.g., EMR asks you if you want to add diabetes to the problem list when prescribing insulin EMR enforces appropriate words to be used in the right places e.g., second hand smoke EMR asks if you want to discontinue an active medication after a few months of non-prescribing

16 New Complementing Technologies Many of the functions that people expect from EMRs are actually provided in a type of software called disease registries Disease registries provide true database functionality the ability to query and return statistics The role of this new technology is still being debated Will it work side-by-side with EMRs and provide new functionality to EMRs? Will it replace EMRs as the new wonder tool and recommended clinical technology? Will it have its own market niche, separate from EMRs? Stick around, we ll have an update soon!

17 Discussion & Conclusion EMRs are still in their infancy There are many information management issues that still need to be resolved We need tools that can help us ensure that we enter high quality data We need query tools to do more sophisticated queries for population management

18 Dr. Karim Keshavjee Dr. Karim Keshavjee is a Family Physician with a part-time practice in Mississauga. He spent five years in the pharmaceutical industry managing clinical trials and managing an electronic drug utilization project. He is currently an Associate Member of the Centre for Evaluation of Medicines, an independent academic research institute affiliated with McMaster University in Hamilton, Ontario. At the Centre for Evaluation of Medicines he is the Clinician-Project Manager for the COMPETE (Computerization of Medical Practices for the Enhancement of Therapeutic Effectiveness) series of research studies. The COMPETE research program studies the impact of e-health technologies on the management of patients with diabetes and vascular disease. You can find out more about COMPETE at Karim was also the physician consultant to Canada Health Infoway for the pan-canadian electronic prescribing project (CeRx) and the inter-operable electronic health record (iehr) project. He is also a mentor on a CIHR-funded, pan-canadian health informatics research training program for post-graduate students. Karim completed his MBA at the Rotman School of Business in 2004 in technology commercialization. He now specializes in helping academic researchers disseminate their evidence-based research findings and inventions to primary care physicians who could benefit from them. He has recently licensed EMR implementation methodology from McMaster University to assist primary care physicians be more successful with EMR implementations. He has also licensed the P-PROMPT TM (www.pprompt.com) service to help primary care physicians be more successful with managing preventive services and chronic diseases within their patient populations. You can find out more about InfoClin at You can contact Karim at

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