Preliminary validation of an automated health problem list Lise Poissanta, Robyn Tamblyna,b, Allen Huangc a
Clinical and Health Informatics Research Group, McGill University, Montreal, Qc, Canada Department of Epidemiology and Biostatistics, Faculty of Medicine, McGill University, Montreal, Qc, Canada c Division of Geriatric Medicine, Faculty of Medicine, McGill University Health Center, Montreal, Qc, Canada b
Abstract The expanding functionalities of EHR require valid, medical problem list. This study describes the preliminary validation of an automated problem list. A total of 5482 health problems were documented in the system for 1535 patients. Sixtysix percent of all health problems were automated by the system through available information on administrative databases and only 3.6% of those were rejected by physicians. Further validation of the system’s automated problem list is being conducted.
prompted to confirm or reject the presence of the problem or have it remain as a potential problem. Health problems can also be manually entered by physicians directly into the problem list or at time of e-prescribing to document the diagnosis associated with the prescribed drug. Study Population Data for this study were obtained from 28 primary care physicians participating in the McGill E-Rx project and their 1866 patients who consented between May 25th 2004 and February 15th 2005.
Results
Introduction Medical problem lists are valuable to health care professionals to document and communicate key clinical information in a concise way. The need for valid, medical problem lists has gained importance with the expanded use of electronic health records and associated functionalities such as decisionsupport tools and drug-disease alert systems. Despite the recognized added value of problem lists, most are created through manual data entry, placing an added burden on physicians in the form of documentation time.
Of all consenting patients, 331 patients (17.7%) did not have any health problems documented in the system. A total of 5482 health problems (280 unique problems) were identified for the remaining 1535 patients, either through the automated system (n=3705) or manual entry (n=1777). Problems most often confirmed as positive were: hypertension (13.2%), hyperlipidemia (7.4%) and hypothyroidism (4.5%).
This study describes the preliminary validation of an innovative automated health problem list in an electronic drug/disease management system (McGill-E-Rx). Specifically, this study aims to determine the number and types of health problems generated by the system and to examine the accuracy of the system in generating problems that are subsequently confirmed by physicians as being present (positive).
Hypertension and hyperlipidemia were also the problems most frequently presented to physicians as potential ones through the automated retrieval of information from the medical services claims and drug insurance databases, respectively. Among all automated health problems, less than 3.6% were rejected by physicians. The most frequently rejected problems were ventricular arrhythmia (7.6%) due to inaccuracies in the medical services claims data, and psychosis (6.1%) and inflammation/pruritus (6.1%) due to problems with the drug insurance data.
Methods
Conclusion/Recommendations
The McGill E-Rx System The McGill E-Rx system uses several sources of information to generate in an automated manner, patient specific health problems. ICD-9 coded diagnoses are retrieved from the medical services claims and transformed into text-based health problems. The provincial drug and health insurance database is utilized to identify health problems associated with one of 326 single-indication drugs (e.g. insulin diabetes) once the drug is dispensed to the patient regardless of the prescribing physician. Automated problems are presented to physicians as potential problems and physicians are
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This study successfully demonstrated the capacity of the McGill E-Rx system in generating a valid, automated list of health problems. Further work is required to address the inaccuracies associated with the drug insurance data. Further validation steps will be conducted to assess the proportion and types of health problems that are not captured by the system. Address for correspondance Lise Poissant, McGill University, 1140 Pine Ave, W. Montreal (Qc), Canada, H3A 1A3.
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