Automated Detection of Systematic Off-label Drug Use in Free Text of Electronic Medical Records.

Off-label use of a drug occurs when it is used in a manner that deviates from its FDA label. Studies estimate that 21% of prescriptions are off-label, with only 27% of those uses supported by evidence of safety and efficacy. We have developed methods to detect population level off-label usage using computationally efficient annotation of free text from clinical notes to generate features encoding empirical information about drug-disease mentions. By including additional features encoding prior knowledge about drugs, diseases, and known usage, we trained a highly accurate predictive model that was used to detect novel candidate off-label usages in a very large clinical corpus. We show that the candidate uses are plausible and can be prioritized for further analysis in terms of safety and efficacy

Lessons Learned from Implementing Service-Oriented Clinical Decision Support at Four Sites: A Qualitative Study

Objective To identify challenges, lessons learned and best practices for service-oriented clinical decision support, based on the results of the Clinical Decision Support Consortium, a multi-site study which developed, implemented and evaluated clinical decision support services in a diverse range of electronic health records. Methods Ethnographic investigation using the rapid assessment process, a procedure for agile qualitative data collection and analysis, including clinical observation, system demonstrations and analysis and 91 interviews. Results We identified challenges and lessons learned in eight dimensions: (1) hardware and software computing infrastructure, (2) clinical content, (3) human-computer interface, (4) people, (5) workflow and communication, (6) internal organizational policies, procedures, environment and culture, (7) external rules, regulations, and pressures and (8) system measurement and monitoring. Key challenges included performance issues (particularly related to data retrieval), differences in terminologies used across sites, workflow variability and the need for a legal framework. Discussion Based on the challenges and lessons learned, we identified eight best practices for developers and implementers of service-oriented clinical decision support: (1) optimize performance, or make asynchronous calls, (2) be liberal in what you accept (particularly for terminology), (3) foster clinical transparency, (4) develop a legal framework, (5) support a flexible front-end, (6) dedicate human resources, (7) support peer-to-peer communication, (8) improve standards. Conclusion The Clinical Decision Support Consortium successfully developed a clinical decision support service and implemented it in four different electronic health records and four diverse clinical sites; however, the process was arduous. The lessons identified by the Consortium may be useful for other developers and implementers of clinical decision support services

Rising drug allergy alert overrides in electronic health records: an observational retrospective study of a decade of experience

Objective There have been growing concerns about the impact of drug allergy alerts on patient safety and provider alert fatigue. The authors aimed to explore the common drug allergy alerts over the last 10 years and the reasons why providers tend to override these alerts. Design: Retrospective observational cross-sectional study (2004–2013). Materials and Methods Drug allergy alert data (n = 611,192) were collected from two large academic hospitals in Boston, MA (USA). Results Overall, the authors found an increase in the rate of drug allergy alert overrides, from 83.3% in 2004 to 87.6% in 2013 (P < .001). Alarmingly, alerts for immune mediated and life threatening reactions with definite allergen and prescribed medication matches were overridden 72.8% and 74.1% of the time, respectively. However, providers were less likely to override these alerts compared to possible (cross-sensitivity) or probable (allergen group) matches (P < .001). The most common drug allergy alerts were triggered by allergies to narcotics (48%) and other analgesics (6%), antibiotics (10%), and statins (2%). Only slightly more than one-third of the reactions (34.2%) were potentially immune mediated. Finally, more than half of the overrides reasons pointed to irrelevant alerts (i.e., patient has tolerated the medication before, 50.9%) and providers were significantly more likely to override repeated alerts (89.7%) rather than first time alerts (77.4%, P < .001). Discussion and Conclusions These findings underline the urgent need for more efforts to provide more accurate and relevant drug allergy alerts to help reduce alert override rates and improve alert fatigue

Experience with decision support system and comfort with topic predict clinicians’ responses to alerts and reminders

Objective Clinicians at our institution typically respond to about half of the prompts they are given by the clinic’s computer decision support system (CDSS). We sought to examine factors associated with clinician response to CDSS prompts as part of a larger, ongoing quality improvement effort to optimize CDSS use. Methods We examined patient, prompt, and clinician characteristics associated with clinician response to decision support prompts from the Child Health Improvement through Computer Automation (CHICA) system. We asked pediatricians who were nonusers of CHICA to rate decision support topics as “easy” or “not easy” to discuss with patients and their guardians. We analyzed these ratings and data, from July 1, 2009 to January 29, 2013, utilizing a hierarchical regression model, to determine whether factors such as comfort with the prompt topic and the length of the user’s experience with CHICA contribute to user response rates. Results We examined 414 653 prompts from 22 260 patients. The length of time a clinician had been using CHICA was associated with an increase in their prompt response rate. Clinicians were more likely to respond to topics rated as “easy” to discuss. The position of the prompt on the page, clinician gender, and the patient’s age, race/ethnicity, and preferred language were also predictive of prompt response rate. Conclusion This study highlights several factors associated with clinician prompt response rates that could be generalized to other health information technology applications, including the clinician’s length of exposure to the CDSS, the prompt’s position on the page, and the clinician’s comfort with the prompt topic. Incorporating continuous quality improvement efforts when designing and implementing health information technology may ensure that its use is optimized

Pediatricians’ Responses to Printed Clinical Reminders: Does Highlighting Prompts Improve Responsiveness?

Objective Physicians typically respond to roughly half of the clinical decision support prompts they receive. This study was designed to test the hypothesis that selectively highlighting prompts in yellow would improve physicians' responsiveness. Methods We conducted a randomized controlled trial using the Child Health Improvement Through Computer Automation clinical decision support system in 4 urban primary care pediatric clinics. Half of a set of electronic prompts of interest was highlighted in yellow when presented to physicians in 2 clinics. The other half of the prompts was highlighted when presented to physicians in the other 2 clinics. Analyses compared physician responsiveness to the 2 randomized sets of prompts: highlighted versus not highlighted. Additionally, several prompts deemed high priority were highlighted during the entire study period in all clinics. Physician response rates to the high-priority highlighted prompts were compared to response rates for those prompts from the year before the study period, when they were not highlighted. Results Physicians did not respond to prompts that were highlighted at higher rates than prompts that were not highlighted (62% and 61%, respectively; odds ratio 1.056, P = .259, NS). Similarly, physicians were no more likely to respond to high-priority prompts that were highlighted compared to the year before, when the prompts were not highlighted (59% and 59%, respectively, χ2 = 0.067, P = .796, NS). Conclusions Highlighting reminder prompts did not increase physicians' responsiveness. We provide possible explanations why highlighting did not improve responsiveness and offer alternative strategies to increasing physician responsiveness to prompts

Association between Electronic Prescribing among Ambulatory Care Providers and Adverse Drug Event Hospitalizations in Older Adults

Purpose. This dissertation research sought to determine whether the proportion of physicians using electronic prescribing (e-prescribing) was associated with the hospitalization rate for adverse drug events (ADEs) among patients aged 65 and older in 2011. Additionally, we sought to determine whether increases in the proportion of e-prescribing physicians in a county were associated with decreases in the hospitalization rate for ADE among older adults. Methods. Two study designs were used, a cross-sectional study using 2011 data and a pre-post- study using 2008 and 2011 data. Data from the 2008 and 2011 State Inpatient Databases, the Office of the National Coordinator Health IT Dashboard, and the Area Health Resource File were gathered for six states: Arizona, Florida, Maryland, Michigan, New Jersey, and Washington. ADE hospitalization rates were calculated for adults 65 years and older. The independent variable, the rate of e-prescribing, was an ecological measure for both analyses. Multivariable linear regression examined county rates of ADE hospitalization in 2011, multivariable logistic regression examined the odds that a discharge would have been ADE associated versus other causes in 2011, and negative binomial regression was used to model the ADE hospitalization rate among older adults in 2011 based on the ADE hospitalization rate in 2008, the change in e-prescribing rates, and county characteristics. Results. Results indicated that county e-prescribing rates were not significantly associated with county ADE hospitalization rates among older adults (p=0.4705). Further, after adjusting for patient, provider, health infrastructure, and community factors, the county e-prescribing rate was not a significant factor in determining the odds of an ADE hospitalization. Change in e-prescribing rates was not significantly associated with the change in ADE hospitalization rates; no other county characteristics were found to be significant factors. Conclusion. Though the adoption of e-prescribing has continued to increase throughout the U.S., our findings indicate that population-level benefits, such as decreased ADE hospitalization among older adults, have yet to be seen. It may be too early to detect population-level changes due to low levels of implementation of health information technologies, such as e-prescribing. Researchers and policy makers must continue to monitor the population impact that the implementation of HITs is having on the health of the nation