A rules based algorithm to generate problem lists using emergency department medication reconciliation

OBJECTIVES To evaluate the sensitivity and specificity of a problem list automatically generated from the emergency department (ED) medication reconciliation. METHODS We performed a retrospective cohort study of patients admitted via the ED who also had a prior inpatient admission within the past year of an academic tertiary hospital. Our algorithm used the First Databank ontology to group medications into therapeutic classes, and applied a set of clinically derived rules to them to predict obstructive lung disease, hypertension, diabetes, congestive heart failure (CHF), and thromboembolism (TE) risk. This prediction was compared to problem lists in the last discharge summary in the electronic health record (EHR) as well as the emergency attending note. RESULTS A total of 603 patients were enrolled from 03/29/2013-04/30/2013. The algorithm had superior sensitivity for all five conditions versus the attending problem list at the 99% confidence level (Obstructive Lung Disease 0.93 vs 0.47, Hypertension 0.93 vs 0.56, Diabetes 0.97 vs 0.73, TE Risk 0.82 vs 0.36, CHF 0.85 vs 0.38), while the attending problem list had superior specificity for both hypertension (0.76 vs 0.94) and CHF (0.87 vs 0.98). The algorithm had superior sensitivity for all conditions versus the EHR problem list (Obstructive Lung Disease 0.93 vs 0.34, Hypertension 0.93 vs 0.30, Diabetes 0.97 vs 0.67, TE Risk 0.82 vs 0.23, CHF 0.85 vs 0.32), while the EHR problem list also had superior specificity for detecting hypertension (0.76 vs 0.95) and CHF (0.87 vs 0.99). CONCLUSION The algorithm was more sensitive than clinicians for all conditions, but less specific for conditions that are not treated with a specific class of medications. This suggests similar algorithms may help identify critical conditions, and facilitate thorough documentation, but further investigation, potentially adding alternate sources of information, may be needed to reliably detect more complex conditions.

[1]  Peter J. Haug,et al.  Randomized controlled trial of an automated problem list with improved sensitivity , 2008, Int. J. Medical Informatics.

[2]  Mary K Goldstein,et al.  Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic. , 2002, The American journal of managed care.

[3]  Adam Wright,et al.  Clinician attitudes toward and use of electronic problem lists: a thematic analysis , 2011, BMC Medical Informatics Decis. Mak..

[4]  William K. Thompson,et al.  Automatically detecting problem list omissions of type 2 diabetes cases using electronic medical records. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[5]  I. Kohane,et al.  Public standards and patients' control: how to keep electronic medical records accessible but private. , 2001, BMJ : British Medical Journal.

[6]  David W. Bates,et al.  Automated coded ambulatory problem lists: evaluation of a vocabulary and a data entry tool , 2003, Int. J. Medical Informatics.

[7]  Kenneth Mandl,et al.  Electronic Patient-Physician Communication: Problems and Promise , 1998, Annals of Internal Medicine.

[8]  J Zelingher,et al.  Categorization of free-text problem lists: an effective method of capturing clinical data. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[9]  David O. Meltzer,et al.  A trial of indication based prescribing of antihypertensive medications during computerized order entry to improve problem list documentation , 2013, Int. J. Medical Informatics.

[10]  R. Kessels,et al.  Patients’ Memory for Medical Information , 2003, Journal of the Royal Society of Medicine.

[11]  Mark S. Ackerman,et al.  Cooperative documentation: the patient problem list as a nexus in electronic health records , 2012, CSCW.

[12]  Sameer Malhotra,et al.  Problem list completeness in electronic health records: A multi-site study and assessment of success factors , 2015, Int. J. Medical Informatics.

[13]  B. Naughton,et al.  Delirium and other cognitive impairment in older adults in an emergency department. , 1995, Annals of emergency medicine.

[14]  Daniel M. Hartung,et al.  Clinical implications of an accurate problem list on heart failure treatment , 2005, Journal of General Internal Medicine.

[15]  J. Morley,et al.  Unrecognized delirium in ED geriatric patients. , 1995, The American journal of emergency medicine.

[16]  David Sarne,et al.  Computerized physician order entry of medications and clinical decision support can improve problem list documentation compliance , 2010, Int. J. Medical Informatics.

[17]  R A Miller,et al.  Empirical Derivation of an Electronic Clinically Useful Problem Statement System , 1999, Annals of Internal Medicine.

[18]  Linas Simonaitis,et al.  Medication and Indication Linkage: A Practical Therapy for the Problem List? , 2008, AMIA.

[19]  S. Lange,et al.  Adjusting for multiple testing--when and how? , 2001, Journal of clinical epidemiology.

[20]  M. McHugh.  Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[21]  Tarone Re.  A modified Bonferroni method for discrete data. , 1990 .

[22]  L. Weed.  Medical records that guide and teach. , 1968, The New England journal of medicine.