Allison R. Wilcox

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record

BACKGROUND Accurate knowledge of a patients medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. OBJECTIVE To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. STUDY DESIGN AND METHODS We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100,000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100,000 records to assess its accuracy. RESULTS Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100,000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. CONCLUSION We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.

The relationship between perceived practice quality and quality improvement activities and physician practice dissatisfaction, professional isolation, and work-life stress.

Background:The importance of physician well-being has been well-documented. However, little is known about how physicians’ self-reported quality improvement (QI) activities and quality of care are related to their practice dissatisfaction, professional isolation, and work-life stress. Methods:We surveyed a random sample of 1884 physicians in Massachusetts by mail and assessed their practices’ participation in QI activities and quality of care, as well as their feelings of professional isolation, work-life stress, and practice dissatisfaction. Results:A total of 1345 physicians responded (71.4% response rate). Most respondents reported QI activities in their practices (85%) and subsequent evaluation of these activities (62%). Approximately one-third (33%) reported quality problems in their practice. In linear regression analyses, the presence of quality problems was independently associated with increased professional isolation, work-life stress, and practice dissatisfaction. In contrast, physicians from practices that were involved in the evaluation of QI activities had significantly less isolation, stress, and dissatisfaction. Participation in QI activities was also independently associated with less dissatisfaction. A substantial fraction of physicians reported moderate to severe problems with isolation (17%), work-life stress (31%), and dissatisfaction (27%). Conclusions:Substantial practice dissatisfaction, professional isolation, and work-life stress are experienced by physicians and they seem to be inversely correlated with QI activities. Physicians who perceive quality problems in their practices are more likely to experience dissatisfaction, isolation, and stress. Efforts to engage physicians in QI and systems change should assess how these programs affect physicians themselves and the care that they deliver.

Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial

Background Accurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date. Objective To determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation. Study Design and Methods Inference rules for 17 conditions were constructed and an electronic health record-based intervention was evaluated to improve problem documentation. A cluster randomized trial was conducted of 11 participating clinics affiliated with a large academic medical center, totaling 28 primary care clinical areas, with 14 receiving the intervention and 14 as controls. The intervention was a clinical alert directed to the provider that suggested adding a problem to the electronic problem list based on inference rules. The primary outcome measure was acceptance of the alert. The number of study problems added in each arm as a pre-specified secondary outcome was also assessed. Data were collected during 6-month pre-intervention (11/2009–5/2010) and intervention (5/2010–11/2010) periods. Results 17 043 alerts were presented, of which 41.1% were accepted. In the intervention arm, providers documented significantly more study problems (adjusted OR=3.4, p<0.001), with an absolute difference of 6277 additional problems. In the intervention group, 70.4% of all study problems were added via the problem list alerts. Significant increases in problem notation were observed for 13 of 17 conditions. Conclusion Problem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement. Trial Registration ClinicalTrials.gov: NCT01105923.

How physicians document outpatient visit notes in an electronic health record

BACKGROUND Clinical documentation, an essential process within electronic health records (EHRs), takes a significant amount of clinician time. How best to optimize documentation methods to deliver effective care remains unclear. OBJECTIVE We evaluated whether EHR visit note documentation method was influenced by physician or practice characteristics, and the association of physician satisfaction with an EHR notes module. MEASUREMENTS We surveyed primary care physicians (PCPs) and specialists, and used EHR and provider data to perform a multinomial logistic regression of visit notes from 2008. We measured physician documentation method use and satisfaction with an EHR notes module and determined the relationship between method and physician and practice characteristics. RESULTS Of 1088 physicians, 85% used a single method to document the majority of their visits. PCPs predominantly documented using templates (60%) compared to 34% of specialists, while 38% of specialists predominantly dictated. Physicians affiliated with academic medical centers (OR 1.96, CI (1.23, 3.12)), based at a hospital (OR 1.57, 95% CI (1.04, 2.36)) and using the EHR for longer (OR 1.13, 95% CI (1.03, 1.25)) were more likely to dictate than use templates. Most physicians of 383 survey responders were satisfied with the EHR notes module, regardless of their preferred documentation method. CONCLUSIONS Physicians predominantly utilized a single method of visit note documentation and were satisfied with their approach, but the approaches they chose varied. Demographic characteristics were associated with preferred documentation method. Further research should focus on why variation exists, and the quality of the documentation resulting from different methods used.

Preventability of adverse drug events involving multiple drugs using publicly available clinical decision support tools

PURPOSE The results of a retrospective evaluation of the frequency and preventability of adverse drug events (ADEs) involving multiple drugs among hospital inpatients are reported. METHODS Data collected in a previous cohort study of 180 actual ADEs and 552 potential ADEs (PADEs) at six community hospitals in Massachusetts were analyzed to determine the frequency and types of multiple-drug ADEs and the extent to which the ADEs might have been prevented using publicly available clinical decision-support (CDS) knowledge bases. None of the hospitals had a computerized prescriber-order-entry system at the time of data collection (January 2005-August 2006). RESULTS A total of 17 ADEs (rate, 1.4 per 100 admissions) and 146 PADEs (rate, 12.2 per 100 admissions) involving multiple drugs were identified. The documented events were related to drug duplication (n = 126), drug-drug interaction (n = 21), additive effects (n = 14), and therapeutic duplication (n = 7) or a combination of those factors. The majority of actual ADEs were due to drug-drug interactions, most commonly involving opioids, benzodiazepines, or cardiac medications; about 75% of the PADEs involved excessive drug doses resulting from order duplication or the prescribing of combination drugs with overlapping ingredients, usually products containing acetaminophen and an opioid. It was determined that 5 (29.4%) of the ADEs and 131 (89.7%) of the PADEs could have been detected through the use of the evaluated CDS tools. CONCLUSION A substantial number of actual ADEs and PADEs in the community hospital setting may be preventable through the use of publicly available CDS knowledge bases.

A novel clinician interface to improve clinician access to up-to-date genetic results

OBJECTIVES To understand the impact of GeneInsight Clinic (GIC), a web-based tool designed to manage genetic information and facilitate communication of test results and variant updates from the laboratory to the clinics, we measured the use of GIC and the time it took for new genetic knowledge to be available to clinicians. METHODS Usage data were collected across four study sites for the GIC launch and post-GIC implementation time periods. The primary outcome measures were the time (average number of days) between variant change approval and notification of clinic staff, and the time between notification and viewing the patient record. RESULTS Post-GIC, time between a variant change approval and provider notification was shorter than at launch (average days at launch 503.8, compared to 4.1 days post-GIC). After e-mail alerts were sent at launch, providers clicked into the patient record associated with 91% of these alerts. In the post period, clinic providers clicked into the patient record associated with 95% of the alerts, on average 12 days after the e-mail was sent. DISCUSSION We found that GIC greatly increased the likelihood that a provider would receive updated variant information as well as reduced the time associated with distributing that variant information, thus providing a more efficient process for incorporating new genetic knowledge into clinical care. CONCLUSIONS Our study results demonstrate that health information technology systems have the potential effectively to assist providers in utilizing genetic information in patient care.

A Picture is Worth 1,000 Words

OBJECTIVE To understand how clinicians utilize image uploading tools in a home grown electronic health records (EHR) system. METHODS A content analysis of patient notes containing non-radiological images from the EHR was conducted. Images from 4,000 random notes from July 1, 2009 -June 30, 2010 were reviewed and manually coded. Codes were assigned to four properties of the image: (1) image type, (2) role of image uploader (e.g. MD, NP, PA, RN), (3) practice type (e.g. internal medicine, dermatology, ophthalmology), and (4) image subject. RESULTS 3,815 images from image-containing notes stored in the EHR were reviewed and manually coded. Of those images, 32.8% were clinical and 66.2% were non-clinical. The most common types of the clinical images were photographs (38.0%), diagrams (19.1%), and scanned documents (14.4%). MDs uploaded 67.9% of clinical images, followed by RNs with 10.2%, and genetic counselors with 6.8%. Dermatology (34.9%), ophthalmology (16.1%), and general surgery (10.8%) uploaded the most clinical images. The content of clinical images referencing body parts varied, with 49.8% of those images focusing on the head and neck region, 15.3% focusing on the thorax, and 13.8% focusing on the lower extremities. CONCLUSION The diversity of image types, content, and uploaders within a home grown EHR system reflected the versatility and importance of the image uploading tool. Understanding how users utilize image uploading tools in a clinical setting highlights important considerations for designing better EHR tools and the importance of interoperability between EHR systems and other health technology. CITATION AC Ai, FL Maloney, T-T Hickman, AR Wilcox, H Ramelson, A Wright. A picture is worth 1,000 words: The use of clinical images in electronic medical records. Appl Clin Inform 2017; 8: 710-718 https://doi.org/10.4338/ACI-2016-10-RA-0180.