Data Management. Shanna M. Morgan, MD Department of Laboratory Medicine and Pathology University of Minnesota
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1 Data Management Shanna M. Morgan, MD Department of Laboratory Medicine and Pathology University of Minnesota
2 None Disclosures
3 Objectives History of data management in medicine Review of data management use in medicine UMMC Apheresis database What works What doesn t work Future
4 Introduction Clinical registries will have an increasing role in health-care as information technology is more accessible and there are higher demands for quality improvement Registries can be used for quality assessment, research, review productivity, regulatory purposes and more
5 History of Data Management Empirical observations have been around for some time Acute diseases (short periods of time between onset, intervention, and outcome) become case reports, series, etc. Chronic diseases benefit the most from data management Impact of the computer Link observations made by many practitioners across long periods of time Narrow as a specific disease or device, broad as the whole medical record Exact date of first data entry unknown 1887, NIH with multiple divisions thereafter 1969, Duke databank for Cardiovascular Disease
6 History of Data Management Apheresis registries are more recent 1994, The Italian Registry 2005, The Italian Pediatric Registry 2003, World Apheresis Registry (3 centers, 2 European countries) 1993, Swedish Apheresis study group 2000, Turkish Registry (single center)
7 Review: Examples of Successful Data Bases The Medical Record (TMR) The most important and thorough document within the delivery of healthcare Tracks course of patients care Communication tool Current, past, future medical history Reviewing and reimbursement of insurance claims Review utilization and quality of care Origin: replacing paper chart with computerized record, satisfying needs of practicing physicians
8 TMR Contents Face sheets, encounters for each visit Vital Signs Physician's orders History and Physical, Progress, and Discharge List of Medical Problems Medication Lists Authorization Forms Diagnostic and Laboratory Testing Reports, Operative and Pathology
9 TMR Contents Documentation (specifics on what to include) Legibility (scanned documents) Verbiage (NOT doing well, appeared etc., too ambiguous) Claims reimbursement (medical necessity) Correcting errors Telephone calls Counter-signatures Abbreviations (only standard abbreviations should be used) Records: retention, release, ownership facility owns record, patient owns information within the record, rights differ between states Legal
10 Review: Examples of Successful Data Bases Examples of medical record information systems COSTAR, Regenstrief Medical Record System, HELP, STOR, PROMIS Specific data base applications: The Duke Databank for Cardiovascular Disease The Boston Collaborative Drug Surveillance Program (BCDSP) Mayo Clinic Data Base National Heart, Lung, and Blood Institute of percutaneous transluminal coronary angioplasty (NHLBI-PTCA)
11 UMMC Apheresis Data Base
12 UMMC Apheresis Data Base 2005: informatics student needed a project Took our log book Productivity Records for inspectors Statistical purposes Good for recording Bad for query (inefficient and error prone) Microsoft Access (readily available)
13 UMMC Apheresis Data Base All charting went into data base (2005 through March, 2011) Volumes, access (line v. peripheral) Medications, anticoagulants, blood products (primes, plasma) Reactions (classification) Allergies Data entry was time consuming March 2011, Epic went live (Epic is not queryable), now using a scaled back version of the data base
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22 Creating a Query
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26 What Works Characteristics of most clinical databases Information Collected Entered Stored Recalled/Queried (for individuals or groups)
27 What Works: Data Collection Physician and IT work together Which variables collected, how collected, how long they will be collected Decide what types of information you would query to determine what fields you are interested in Make sure you can capture what it is you want Danger with too much or too little data (maximum gain/minimum pain) Remember: the busier you are, the more data to enter, database can get behind
28 What Works: Data Collection Data bases functions can change Two ways to collect clinical information Part of patient care process Broad focus Advantages are prospective, existing quality control, reduced cost Separate data base Narrow focus Focus is not part of the patient care process
29 What Works General demographics MR#, DOB, name, Adult vs. pediatric, sex, diagnosis, requesting physician, requesting service, TM physician initiator, start/stop date, # of procedures, schedule (daily, QOD), comorbidities Course of Therapy (COT) Date, procedure, emergent or not, IP or OP, stability, start time and duration, access, instrument, anticoagulant, frequency (sometimes different), blood products
30 What Doesn t Work Putting everything in (people get too excited at first) Avoid free text Difficult to query Takes longer to enter the data
31 What would be nice Adverse events Could rate them 1-5 depending on a scale or nonsevere, severe, life-threatening, death, notdetermined Imputablity (definite ruled out, not-determined) # of rxns and X% managed by a nurse per SOP Pertinent labs (TTP) Could say platelet count rises by day X Fibrinogen decreases by day X
32 Future Registry data vs. randomized clinical trials (RCT) Registry data is gold standard for observational data, but second best to RCT data Despite advances in statistical methods, a registry is not a replacement for a well-designed RCT RCT Advantages: Confounding variables accounted for by randomization Limitations: Expense and resource requirements to run them Narrow patient cohorts may not reflect general target population Some diseases too rare, prevalence unknown, or change in diagnosis
33 Registry Data vs. Randomized Registry data Advantages: Clinical Trials (RCT) Reflect common practice and the general target population Can be used to validate or augment RCTs Can be superior for monitoring long-term outcomes that exceed study window of a RCT Can accelerate research for rare diseases Facilitate collaboration and sharing of data Disadvantages: Limitations in collection fields means unmeasured confounders and potential selection bias
34 Future: Record Linkage Registry data linked to other data sources Administrative databases Official census records (e.g. Office for National Statistics) National medical council databases (e.g. General Medical Council) More research possibilities, but also new legal and ethical challenges (mismatched data and patient ID potential)
35 Future: Commercial Value Registries require resources, infrastructure, and sustained funding for long-term benefits Funding: Government budgets Professional societies Local health commissioners Hospital
36 Conclusion Success of a registry: Database completeness Accessibility of information Proven usefulness Benefits: Advances in research Improved patient decision making Most effective registries combine clinical input and data management expertise
37 References 1. Hickey GL et al. Clinical registries: governance, management, analysis and applications. European Journal of Cardio-Thoracic Surgery 2013; Gilkes CE et al. Clinical databases and data protection: are they compatible? British Journal of Neurosurgery, October 2003; 17(5): Pryor DB et al. Clinical Data Bases: Accomplishments and Unrealized Potential. Medical Care. May 1985; Vol. 23, No Boyd P. The requirements of the Data Protection Act 1998 for the processing of medical data. J Med Ethics 2003; 29: Black N. High-quality clinical databases: breaking down barriers. The Lancet 1999; Vol 353, April 10.
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