Allison B. McCoy

A Computerized Provider Order Entry Intervention for Medication Safety During Acute Kidney Injury: A Quality Improvement Report

BACKGROUND Frequently, prescribers fail to account for changing kidney function when prescribing medications. We evaluated the use of a computerized provider order entry intervention to improve medication management during acute kidney injury. STUDY DESIGN Quality improvement report with time series analyses. SETTING & PARTICIPANTS 1,598 adult inpatients with a minimum 0.5-mg/dL increase in serum creatinine level over 48 hours after an order for at least one of 122 nephrotoxic or renally cleared medications. QUALITY IMPROVEMENT PLAN Passive noninteractive warnings about increasing serum creatinine level appeared within the computerized provider order entry interface and on printed rounding reports. For contraindicated or high-toxicity medications that should be avoided or adjusted, an interruptive alert within the system asked providers to modify or discontinue the targeted orders, mark the current dosing as correct and to remain unchanged, or defer the alert to reappear in the next session. OUTCOMES & MEASUREMENTS Intervention effect on drug modification or discontinuation, time to modification or discontinuation, and provider interactions with alerts. RESULTS The modification or discontinuation rate per 100 events for medications included in the interruptive alert within 24 hours of increasing creatinine level improved from 35.2 preintervention to 52.6 postintervention (P < 0.001); orders were modified or discontinued more quickly (P < 0.001). During the postintervention period, providers initially deferred 78.1% of interruptive alerts, although 54% of these eventually were modified or discontinued before patient death, discharge, or transfer. The response to passive alerts about medications requiring review did not significantly change compared with baseline. LIMITATIONS Single tertiary-care academic medical center; provider actions were not independently adjudicated for appropriateness. CONCLUSIONS A computerized provider order entry-based alerting system to support medication management after acute kidney injury significantly increased the rate and timeliness of modification or discontinuation of targeted medications.

Early Results of the Meaningful Use Program for Electronic Health Records

The HITECH Act created incentives to encourage adoption of electronic health records. As of May 2012, only 12.2% of 62,226 eligible professionals had attested to meaningful use, including 9.8% of specialists and 17.8% of primary care providers.

A framework for evaluating the appropriateness of clinical decision support alerts and responses

OBJECTIVE Alerting systems, a type of clinical decision support, are increasingly prevalent in healthcare, yet few studies have concurrently measured the appropriateness of alerts with provider responses to alerts. Recent reports of suboptimal alert system design and implementation highlight the need for better evaluation to inform future designs. The authors present a comprehensive framework for evaluating the clinical appropriateness of synchronous, interruptive medication safety alerts. METHODS Through literature review and iterative testing, metrics were developed that describe successes, justifiable overrides, provider non-adherence, and unintended adverse consequences of clinical decision support alerts. The framework was validated by applying it to a medication alerting system for patients with acute kidney injury (AKI). RESULTS Through expert review, the framework assesses each alert episode for appropriateness of the alert display and the necessity and urgency of a clinical response. Primary outcomes of the framework include the false positive alert rate, alert override rate, provider non-adherence rate, and rate of provider response appropriateness. Application of the framework to evaluate an existing AKI medication alerting system provided a more complete understanding of the process outcomes measured in the AKI medication alerting system. The authors confirmed that previous alerts and provider responses were most often appropriate. CONCLUSION The new evaluation model offers a potentially effective method for assessing the clinical appropriateness of synchronous interruptive medication alerts prior to evaluating patient outcomes in a comparative trial. More work can determine the generalizability of the framework for use in other settings and other alert types.

Matching identifiers in electronic health records: implications for duplicate records and patient safety

Objective To quantify the percentage of records with matching identifiers as an indicator for duplicate or potentially duplicate patient records in electronic health records in five different healthcare organisations, describe the patient safety issues that may arise, and present solutions for managing duplicate records or records with matching identifiers. Methods For each institution, we retrieved deidentified counts of records with an exact match of patient first and last names and dates of birth and determined the number of patient records existing for the top 250 most frequently occurring first and last name pairs. We also identified methods for managing duplicate records or records with matching identifiers, reporting the adoption rate of each across institutions. Results The occurrence of matching first and last name in two or more individuals ranged from 16.49% to 40.66% of records; inclusion of date of birth reduced the rates to range from 0.16% to 15.47%. The number of records existing for the most frequently occurring name at each site ranged from 41 to 2552. Institutions varied widely in the methods they implemented for preventing, detecting and removing duplicate records, and mitigating resulting errors. Conclusions The percentage of records having matching patient identifiers is high in several organisations, indicating that the rate of duplicate records or records may also be high. Further efforts are necessary to improve management of duplicate records or records with matching identifiers and minimise the risk for patient harm.

The Medicare Electronic Health Record Incentive Program: Provider Performance on Core and Menu Measures

OBJECTIVE To measure performance by eligible health care providers on CMSs meaningful use measures. DATA SOURCE Medicare Electronic Health Record Incentive Program Eligible Professionals Public Use File (PUF), which contains data on meaningful use attestations by 237,267 eligible providers through May 31, 2013. STUDY DESIGN Cross-sectional analysis of the 15 core and 10 menu measures pertaining to use of EHR functions reported in the PUF. PRINCIPAL FINDINGS Providers in the dataset performed strongly on all core measures, with the most frequent response for each of the 15 measures being 90-100 percent compliance, even when the threshold for a particular measure was lower (e.g., 30 percent). PCPs had higher scores than specialists for computerized order entry, maintaining an active medication list, and documenting vital signs, while specialists had higher scores for maintaining a problem list, recording patient demographics and smoking status, and for providing patients with an after-visit summary. In fact, 90.2 percent of eligible providers claimed at least one exclusion, and half claimed two or more. CONCLUSIONS Providers are successfully attesting to CMSs requirements, and often exceeding the thresholds required by CMS; however, some troubling patterns in exclusions are present. CMS should raise program requirements in future years.

The use of sequential pattern mining to predict next prescribed medications

BACKGROUND Therapy for certain medical conditions occurs in a stepwise fashion, where one medication is recommended as initial therapy and other medications follow. Sequential pattern mining is a data mining technique used to identify patterns of ordered events. OBJECTIVE To determine whether sequential pattern mining is effective for identifying temporal relationships between medications and accurately predicting the next medication likely to be prescribed for a patient. DESIGN We obtained claims data from Blue Cross Blue Shield of Texas for patients prescribed at least one diabetes medication between 2008 and 2011, and divided these into a training set (90% of patients) and test set (10% of patients). We applied the CSPADE algorithm to mine sequential patterns of diabetes medication prescriptions both at the drug class and generic drug level and ranked them by the support statistic. We then evaluated the accuracy of predictions made for which diabetes medication a patient was likely to be prescribed next. RESULTS We identified 161,497 patients who had been prescribed at least one diabetes medication. We were able to mine stepwise patterns of pharmacological therapy that were consistent with guidelines. Within three attempts, we were able to predict the medication prescribed for 90.0% of patients when making predictions by drug class, and for 64.1% when making predictions at the generic drug level. These results were stable under 10-fold cross validation, ranging from 89.1%-90.5% at the drug class level and 63.5-64.9% at the generic drug level. Using 1 or 2 items in the patients medication history led to more accurate predictions than not using any history, but using the entire history was sometimes worse. CONCLUSION Sequential pattern mining is an effective technique to identify temporal relationships between medications and can be used to predict next steps in a patients medication regimen. Accurate predictions can be made without using the patients entire medication history.

Adopting Real-Time Surveillance Dashboards as a Component of an Enterprisewide Medication Safety Strategy

BACKGROUND High-alert medications are frequently responsible for adverse drug events and present significant hazards to inpatients, despite technical improvements in the way they are ordered, dispensed, and administered. METHODS A real-time surveillance application was designed and implemented to enable pharmacy review of high-alert medication orders to complement existing computerized provider order entry and integrated clinical decision support systems in a tertiary care hospital. The surveillance tool integrated real-time data from multiple clinical systems and applied logical criteria to highlight potentially high-risk scenarios. Use of the surveillance system for adult inpatients was analyzed for warfarin, heparin and enoxaparin, and aminoglycoside antibiotics. RESULTS Among 28,929 hospitalizations during the study period, patients eligible to appear on a dashboard included 2224 exposed to warfarin, 8383 to heparin or enoxaparin, and 893 to aminoglycosides. Clinical pharmacists reviewed the warfarin and aminoglycoside dashboards during 100% of the days in the study period-and the heparinlenoxaparin dashboard during 71% of the days. Displayed alert conditions ranged from common events, such as 55% of patients receiving aminoglycosides were missing a baseline creatinine, to rare events, such as 0.1% of patients exposed to heparin were given a bolus greater than 10,000 units. On the basis of interpharmacist communication and electronic medical record notes recorded within the dashboards, interventions to prevent further patient harm were frequent. CONCLUSIONS Even in an environment with sophisticated computerized provider order entry and clinical decision support systems, real-time pharmacy surveillance of high-alert medications provides an important platform for intercepting medication errors and optimizing therapy.

Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications

OBJECTIVE We describe a novel, crowdsourcing method for generating a knowledge base of problem-medication pairs that takes advantage of manually asserted links between medications and problems. METHODS Through iterative review, we developed metrics to estimate the appropriateness of manually entered problem-medication links for inclusion in a knowledge base that can be used to infer previously unasserted links between problems and medications. RESULTS Clinicians manually linked 231,223 medications (55.30% of prescribed medications) to problems within the electronic health record, generating 41,203 distinct problem-medication pairs, although not all were accurate. We developed methods to evaluate the accuracy of the pairs, and after limiting the pairs to those meeting an estimated 95% appropriateness threshold, 11,166 pairs remained. The pairs in the knowledge base accounted for 183,127 total links asserted (76.47% of all links). Retrospective application of the knowledge base linked 68,316 medications not previously linked by a clinician to an indicated problem (36.53% of unlinked medications). Expert review of the combined knowledge base, including inferred and manually linked problem-medication pairs, found a sensitivity of 65.8% and a specificity of 97.9%. CONCLUSION Crowdsourcing is an effective, inexpensive method for generating a knowledge base of problem-medication pairs that is automatically mapped to local terminologies, up-to-date, and reflective of local prescribing practices and trends.

Adverse Drug Events during AKI and Its Recovery

BACKGROUND AND OBJECTIVES The impact of AKI on adverse drug events and therapeutic failures and the medication errors leading to these events have not been well described. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS A single-center observational study of 396 hospitalized patients with a minimum 0.5 mg/dl change in serum creatinine who were prescribed a nephrotoxic or renally eliminated medication was conducted. The population was stratified into two groups by the direction of their initial serum creatinine change: AKI and AKI recovery. Adverse drug events, potential adverse drug events, therapeutic failures, and potential therapeutic failures for 148 drugs and 46 outcomes were retrospectively measured. Events were classified for preventability and severity by expert adjudication. Multivariable analysis identified medication classes predisposing AKI patients to adverse drug events. RESULTS Forty-three percent of patients experienced a potential adverse drug event, adverse drug event, therapeutic failure, or potential therapeutic failure; 66% of study events were preventable. Failure to adjust for kidney function (63%) and use of nephrotoxic medications during AKI (28%) were the most common potential adverse drug events. Worsening AKI and hypotension were the most common preventable adverse drug events. Most adverse drug events were considered serious (63%) or life-threatening (31%), with one fatal adverse drug event. Among AKI patients, administration of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, antibiotics, and antithrombotics was most strongly associated with the development of an adverse drug event or potential adverse drug event. CONCLUSIONS Adverse drug events and potential therapeutic failures are common and frequently severe in patients with AKI exposed to nephrotoxic or renally eliminated medications.

Teaching evidence-based medicine: Impact on students’ literature use and inpatient clinical documentation

Background: Effective teaching of evidence-based medicine (EBM) to medical students is important for lifelong self-directed learning. Aims: We implemented a brief workshop designed to teach literature searching skills to third-year medical students. We assessed its impact on students’ utilization of EBM resources during their clinical rotation and the quality of EBM integration in inpatient notes. Methods: We developed a physician-led, hands-on workshop to introduce EBM resources to all internal medicine clerks. Pre- and post-workshop measures included students attitudes to EBM, citations of EBM resources in their clinical notes, and quality of the EBM component of the discussion in the note. Computer log analysis recorded students’ online search attempts. Results: After the workshop, students reported improved comfort using EBM and increased utilization of EBM resources. EBM integration into the discussion component of the notes also showed significant improvement. Computer log analysis of students’ searches demonstrated increased utilization of EBM resources following the workshop. Conclusions: We describe the successful implementation of a workshop designed to teach third-year medical students how to perform an efficient EBM literature search. We demonstrated improvements in students’ confidence regarding EBM, increased utilization of EBM resources, and improved integration of EBM into inpatient notes.