Data Standards, Data Cleaning and Data Discipline. Insight November 24, 2008

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2 Agenda Why is data quality important? Why data quality is poor in the EMR The Principles of Data Discipline Design Implications and Recommendations

3 The Problem If you can t tell which of your patients has had a pap smear, how can you do proper cervical cancer screening? If you can t tell which of your patients has bowel disease, how can you do proper colon cancer screening? If you can t reliably identify patients with chronic disease in your EMR, how can you have a chronic disease management program? If you don t know which patients are on warfarin, how can you make sure that you are prescribing safely?

4 Current EMRs are not Effective Linder JA, Ma J, Bates DW, Middleton B, Stafford RS. Electronic health record use and the quality of ambulatory care in the United States. Arch Intern Med Jul 9;167(13): Retrospective study of ambulatory visits across the US 18% of visits were recorded in an 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 and are probably no better than paper A damning indictment EMRs will only be more effective if they can manage information better they have the promise, but they need much more to deliver on the promise

5 Why Data in EMRs is Poor 1 EMRs are optimized for individual patient care Documentation within a patient is quite good Allows you to view data from a variety of sources in one place Current EMRs are not designed for population-based care Data capture is not standardized Standard terminology is poorly enforced in most EMRs Meta-data is poorly captured (i.e., can put data in the wrong place ) Data inconsistency is rampant Many patients with HBA1c > 7 or on Insulin are not labelled as diabetic in the EMR Data inaccuracies abound Many patients with diagnosis code of 250 in the billing system are not diabetic There are no standardized data feeds into the EMR Laboratory, medications, consult notes, hospital discharges and diagnostic imaging do not come in a consistent and standard way Data good enough for individual care are too complex for population care To manage a colon cancer screening program, you need to enter information in 4 different places Lab data: Stool Occult Blood Test result if lab doesn t send it, add it manually! Procedures: Colonoscopy Past medical history: Colon cancer or inflammatory bowel disease Problem list: current cancer or inflammatory bowel disease Any error in where you put the data, will put the patient in the wrong category Diabetes patient management is even more complex requires data in 7 places, not all of which are structured in most EMRs

6 Why Data in EMRs is Poor 2 As humans, we Are chronically inconsistent We continue to prescribe glyburide and forget to label the patient as being diabetic Deviate from standard terms CAD, Atherosclerosis, CHD, ASHD all mean the same thing but computers don t know that! Forget to change the status of information in the EMR We tell the patient to stop taking a medication, but don t actively stop it in the EMR Use terms that denote a class, when we really mean an instance We say ACE inhibitors or statins, when we really prescribe ramipril and atorvastatin but computers only know instances, not classes! Medical Knowledge and Terminology evolves over time Juvenile vs. Adult onset IDDM vs. NIDDM Type 1 vs. Type 2 Some data is too tedious to structure for non-specialists Foot ulcers, retinopathy, diet, exercise, etc. Current EMRs don t make up for the foibles of humans or the vagaries of human progress

7 Principles of Data Discipline Data Standardization Coding Diagnoses, Medications, Labs, Vitals & Physical Exam Data Cleaning Coverage all patients are in the system Consistency all data tells the same story Completeness all data is in the system Correctness right patients in, wrong patients out Coded all relevant data is coded or in a single format Data Discipline Systems thinking Templates, reminders and searches work together Environmental cues System supports humans Provides clues that data is incomplete or inconsistent or not coded

8 Principles of Data Discipline Data discipline should be maintained using systems Using a template for a particular aspect of care should automatically provide the data to turn a reminder off E.g., smoking cessation counselling, any form of in-office procedure that is not lab related Using lab results to turn off a reminder E.g., pap smear, HbA1c, LDL, FOBT etc Using a scanned report to turn off a reminder E.g., mammography Importance of Reminders They are a good signal of dirty data

9 Managing Colorectal Cancer Screening The Task Quickly generate a list of patients who need to be screened Generate statistics on screening Wrinkles FOBT results that come in on paper need to be managed carefully Patients who have had a colonoscopy need to be excluded for 5 years Patients who have had cancer need to be excluded Patients with GI bleeding disorders need to be excluded Data Issues Electronic FOBT results sometimes not consistent Colonoscopy needs to be recorded properly and consistently GI bleeding disorders and cancers need to be recorded properly, consistently and in the right place so they can be excluded

10 Managing Diabetes Care The Task Be able to find all patients with diabetes reliably Wrinkles There are different types of diabetes that can confuse the issue gestational diabetes, diabetes insipidus Patients may not have a diagnosis listed, but may have other signs of diabetes: on insulin, high blood sugar Data Issues Different terms are used for diabetes: DM and diabetes mellitus are the most prevalent The diagnosis could be in 2 different places History of Past Health and Problem List Some signs of diabetes are also a sign of other things: metformin (a drug for diabetes) is also a drug for another unrelated disease (polycystic ovarian syndrome)

11 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 Tools that automatically add important metadata to the record or recommend appropriate metadata EMR automatically adds metadata for you e.g., when referring a patient to Dr. Jones, the system also captures the fact that Dr. Jones is a cardiologist and that she works at Best General Hospital. EMR suggests putting a diagnosis in the Problem List if it is in the Past Medical History or in an Encounter Note.

12 Implications for EMR Design Tools that allow you to manage data at the population level Assign a group of patients as having received a particular intervention e.g., flu shot or pap smear Assign a group of patients to a particular registry (group) or treatment Update status of data without having to enter each chart separately Features that clean up dirty data, inconsistent data and missing data Some labs send HbA1c as a % (7%) and some as a decimal (0.07) Both are correct, but they can t coexist in the same database Lots of patients have missing dates of birth, addresses and other clinical information Features that allow you to download a computerized guideline from a respected group Currently, you have to do your own programming with complex guidelines Doctors are not programmers and get it wrong or spend hours trying to get it right Sophisticated statistical analysis tools Physicians expect their EMR to provide them with the information they need run time charts, uncontrolled patients, etc. They don t want to export data to another application and generate reports they don t have the time, the staff nor the capabilities

13 New Competing Technologies Many of the functions that people expect from EMRs are actually provided in a type of software called disease management systems Disease management systems provide true database functionality The ability to maintain good quality data The ability to make changes at the population level The ability to have built-in guidelines 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?

14 Summary Medical practice is changing rapidly Chronic disease management has reached maturity Team-based care is rapidly gaining ground as the clinical results are outstanding The new way of practicing medicine requires new tools EMRs will have to catch up to the new ways of practicing or lose ground to Disease Registry and Chronic Disease Management software

15 Acknowledgements Hamilton Family Health Team for supporting this work over the last 2 years

16 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 Director 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), the inter-operable electronic health record (iehr) project and the consumer health architecture project (PAQC). He is also a mentor on a CIHR-funded, pan-canadian health informatics research training program for postgraduate 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. You can find out more about InfoClin at You can contact Karim at karim@infoclin.ca.