The effect of medication cost transparency alerts on prescriber behavior.
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ABSTRACT:
SUBMITTER: Monsen CB
PROVIDER: S-EPMC7647180 | biostudies-literature | 2019 Oct
REPOSITORIES: biostudies-literature
Publications
The effect of medication cost transparency alerts on prescriber behavior.
Monsen Craig B CB Liao Joshua M JM Gaster Barak B Flynn Kevin J KJ Payne Thomas H TH
Journal of the American Medical Informatics Association : JAMIA 20191001 10
<h4>Objective</h4>The purpose of this study was to determine if medication cost transparency alerts provided at time of prescribing led ambulatory prescribers to reduce their use of low-value medications.<h4>Materials and methods</h4>Provider-level alerts were deployed to ambulatory practices of a single health system from February 2018 through April 2018. Practice sites included 58 primary care and 152 specialty care clinics totaling 1896 attending physicians, residents, and advanced practice n ...[more]
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Project description:INTRODUCTION:Drug-drug interaction (DDI) alerts in hospital electronic medication management (EMM) systems are generated at the point of prescribing to warn doctors about potential interactions in their patients' medication orders. This project aims to determine the impact of DDI alerts on DDI rates and on patient harm in the inpatient setting. It also aims to identify barriers and facilitators to optimal use of alerts, quantify the alert burden posed to prescribers with implementation of DDI alerts and to develop algorithms to improve the specificity of DDI alerting systems. METHODS AND ANALYSIS:A controlled pre-post design will be used. Study sites include six major referral hospitals in two Australian states, New South Wales and Queensland. Three hospitals will act as control sites and will implement an EMM system without DDI alerts, and three as intervention sites with DDI alerts. The medical records of 280 patients admitted in the 6?months prior to and 6?months following implementation of the EMM system at each site (total 3360 patients) will be retrospectively reviewed by study pharmacists to identify potential DDIs, clinically relevant DDIs and associated patient harm. To identify barriers and facilitators to optimal use of alerts, 10-15 doctors working at each intervention hospital will take part in observations and interviews. Non-identifiable DDI alert data will be extracted from EMM systems 6-12 months after system implementation in order to quantify alert burden on prescribers. Finally, data collected from chart review and EMM systems will be linked with clinically relevant DDIs to inform the development of algorithms to trigger only clinically relevant DDI alerts in EMM systems. ETHICS AND DISSEMINATION:This research was approved by the Hunter New England Human Research Ethics Committee (18/02/21/4.07). Study results will be published in peer-reviewed journals and presented at local and international conferences and workshops.
| S-EPMC6701635 | biostudies-literature
Project description:IntroductionPhysicians are a critical clinical resource for patient care. Yet physician recruitment has been considerably understudied, particularly in substance use disorder (SUD) settings. This study proposes a conceptual model called the "Physician Recruitment Descriptive Factors Framework" to investigate the role of environmental, organizational, and individual factors in the use of physician recruitment strategies.MethodsThe study setting was 75 sites that provided outpatient SUD treatment services in Florida, Ohio, and Wisconsin from 2016 to 2019. Central to the analysis is the use of five targeted physician recruitment strategies. The study investigated whether financial conditions, location (urban v. non-urban), external implementation coaching, and recruiters' roles influenced use of the targeted physician recruitment strategies.ResultsDuring the study period, a formal plan to recruit physicians was the most common strategy used (n = 67.6 %). The director or chief executive officer (CEO) was most likely to conduct physician recruitment (n = 58.7 %). During the study, use of four of the five recruitment strategies significantly declined (at p ≤ 0.01), while the perceived need for new prescribing capacity significantly declined (p ≤ 0.01), and prescribers per site increased from 1.54 to 3.21. Sixty-four percent of this increase in prescribers was due to more physician prescribers, while 36 % was due to the onset of the ability of advanced nurse practitioners and physician assistants to prescribe buprenorphine. In year 3 of the study, the strategies most closely aligned with the current number of prescribers were conducting weekly outreach to prescriber candidates (p = .018), having a dedicated prescriber recruiter (p = .011), and having a dedicated budget for prescriber recruiting (p = .002).ConclusionsThe study describes which physician recruitment strategies SUD treatment sites used and how the need to recruit physicians for specialty treatment SUD clinics declined as prescriber capacity increased. The proposed multi-level framework provides the scaffolding for future physician recruitment research and practice.
| S-EPMC10833474 | biostudies-literature
Project description:BackgroundComputerized order entry systems have the potential to prevent medication errors and decrease adverse drug events with the use of clinical-decision support systems presenting alerts to providers. Despite the large volume of medications prescribed in the outpatient setting, few studies have assessed the impact of automated alerts on medication errors related to drug-laboratory interactions in an outpatient primary-care setting.Methods and findingsA primary-care clinic in an integrated safety net institution was the setting for the study. In collaboration with commercial information technology vendors, rules were developed to address a set of drug-laboratory interactions. All patients seen in the clinic during the study period were eligible for the intervention. As providers ordered medications on a computer, an alert was displayed if a relevant drug-laboratory interaction existed. Comparisons were made between baseline and postintervention time periods. Provider ordering behavior was monitored focusing on the number of medication orders not completed and the number of rule-associated laboratory test orders initiated after alert display. Adverse drug events were assessed by doing a random sample of chart reviews using the Naranjo scoring scale. The rule processed 16,291 times during the study period on all possible medication orders: 7,017 during the pre-intervention period and 9,274 during the postintervention period. During the postintervention period, an alert was displayed for 11.8% (1,093 out of 9,274) of the times the rule processed, with 5.6% for only "missing laboratory values," 6.0% for only "abnormal laboratory values," and 0.2% for both types of alerts. Focusing on 18 high-volume and high-risk medications revealed a significant increase in the percentage of time the provider stopped the ordering process and did not complete the medication order when an alert for an abnormal rule-associated laboratory result was displayed (5.6% vs. 10.9%, p = 0.03, Generalized Estimating Equations test). The provider also increased ordering of the rule-associated laboratory test when an alert was displayed (39% at baseline vs. 51% during post intervention, p < 0.001). There was a non-statistically significant difference towards less "definite" or "probable" adverse drug events defined by Naranjo scoring (10.3% at baseline vs. 4.3% during postintervention, p = 0.23).ConclusionProviders will adhere to alerts and will use this information to improve patient care. Specifically, in response to drug-laboratory interaction alerts, providers will significantly increase the ordering of appropriate laboratory tests. There may be a concomitant change in adverse drug events that would require a larger study to confirm. Implementation of rules technology to prevent medication errors could be an effective tool for reducing medication errors in an outpatient setting.
| S-EPMC1198038 | biostudies-literature
Project description:OBJECTIVE:Clinicians using clinical decision support (CDS) to prescribe medications have an obligation to ensure that prescriptions are safe. One option is to verify the safety of prescriptions if there is uncertainty, for example, by using drug references. Supervisory control experiments in aviation and process control have associated errors, with reduced verification arising from overreliance on decision support. However, it is unknown whether this relationship extends to clinical decision-making. Therefore, we examine whether there is a relationship between verification behaviors and prescribing errors, with and without CDS medication alerts, and whether task complexity mediates this. METHODS:A total of 120 students in the final 2 years of a medical degree prescribed medicines for patient scenarios using a simulated electronic prescribing system. CDS (correct, incorrect, and no CDS) and task complexity (low and high) were varied. Outcomes were omission (missed prescribing errors) and commission errors (accepted false-positive alerts). Verification measures were access of drug references and view time percentage of task time. RESULTS:Failure to access references for medicines with prescribing errors increased omission errors with no CDS (high-complexity: χ 2(1) = 12.716; p < 0.001) and incorrect CDS (Fisher's exact; low-complexity: p = 0.002; high-complexity: p = 0.001). Failure to access references for false-positive alerts increased commission errors (low-complexity: χ 2(1) = 16.673, p < 0.001; high-complexity: χ 2(1) = 18.690, p < 0.001). Fewer participants accessed relevant references with incorrect CDS compared with no CDS (McNemar; low-complexity: p < 0.001; high-complexity: p < 0.001). Lower view time percentages increased omission (F(3, 361.914) = 4.498; p = 0.035) and commission errors (F(1, 346.223) = 2.712; p = 0.045). View time percentages were lower in CDS-assisted conditions compared with unassisted conditions (F(2, 335.743) = 10.443; p < 0.001). DISCUSSION:The presence of CDS reduced verification of prescription safety. When CDS was incorrect, reduced verification was associated with increased prescribing errors. CONCLUSION:CDS can be incorrect, and verification provides one mechanism to detect errors. System designers need to facilitate verification without increasing workload or eliminating the benefits of correct CDS.
| S-EPMC6353646 | biostudies-literature
Project description:Acute kidney injury is common among hospitalized individuals, particularly those exposed to certain medications, and is associated with substantial morbidity and mortality. In a pragmatic, open-label, National Institutes of Health-funded, parallel group randomized controlled trial (clinicaltrials.gov NCT02771977), we investigate whether an automated clinical decision support system affects discontinuation rates of potentially nephrotoxic medications and improves outcomes in patients with AKI. Participants included 5060 hospitalized adults with AKI and an active order for any of three classes of medications of interest: non-steroidal anti-inflammatory drugs, renin-angiotensin-aldosterone system inhibitors, or proton pump inhibitors. Within 24 hours of randomization, a medication of interest was discontinued in 61.1% of the alert group versus 55.9% of the usual care group (relative risk 1.08, 1.04 - 1.14, p = 0.0003). The primary outcome - a composite of progression of acute kidney injury, dialysis, or death within 14 days - occurred in 585 (23.1%) of individuals in the alert group and 639 (25.3%) of patients in the usual care group (RR 0.92, 0.83 - 1.01, p = 0.09). Trial Registration Clinicaltrials.gov NCT02771977.
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Project description:Electronic prescribing is increasingly used, in part because of government incentives for its use. Many of its benefits come from clinical decision support (CDS), but often too many alerts are displayed, resulting in alert fatigue.To characterize the override rates for medication-related CDS alerts in the outpatient setting, the reasons cited for overrides at the time of prescribing, and the appropriateness of overrides.We measured CDS alert override rates and the coded reasons for overrides cited by providers at the time of prescribing. Our primary outcome was the rate of CDS alert overrides; our secondary outcomes were the rate of overrides by alert type, reasons cited for overrides at the time of prescribing, and override appropriateness for a subset of 600 alert overrides. Through detailed chart reviews of alert override cases, and selective literature review, we developed appropriateness criteria for each alert type, which were modified iteratively as necessary until consensus was reached on all criteria.We reviewed 157,483 CDS alerts (7.9% alert rate) on 2,004,069 medication orders during the study period. 82,889 (52.6%) of alerts were overridden. The most common alerts were duplicate drug (33.1%), patient allergy (16.8%), and drug-drug interactions (15.8%). The most likely alerts to be overridden were formulary substitutions (85.0%), age-based recommendations (79.0%), renal recommendations (78.0%), and patient allergies (77.4%). An average of 53% of overrides were classified as appropriate, and rates of appropriateness varied by alert type (p<0.0001) from 12% for renal recommendations to 92% for patient allergies.About half of CDS alerts were overridden by providers and about half of the overrides were classified as appropriate, but the likelihood of overriding an alert varied widely by alert type. Refinement of these alerts has the potential to improve the relevance of alerts and reduce alert fatigue.
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Project description:BACKGROUND:Poor medication adherence is common and limits the effectiveness of treatment. OBJECTIVE:To investigate how social supports, automated alerts, and their combination improve medication adherence. DESIGN:Four-arm, randomized clinical trial with a 6-month intervention. PARTICIPANTS:A total of 179 CVS health employees or adult dependents with CVS Caremark prescription coverage, a current daily statin prescription, a medication possession ratio less than 80%, and Internet access. INTERVENTIONS:Participants were randomly assigned to control, social support (partner), automated adherence alert messages (alert), or both social support and alerts (partner?+?alert). Participants in the social support arms were asked to name a medication adherence partner (MAP) to help them take their medication. Participants in the alert arms were sent emails, text messages, or automated phone calls if they had failed to adhere on the previous day and on one or both of the 2 days before that. In partner?+?alert, both participants and fully enrolled MAPs received alerts. MAIN MEASURES:Adherence measured by wireless pill bottle opening. KEY RESULTS:Compared to 36.0% adherence in control, adherence was significantly greater in the alert arm (52.9%, difference vs. control of 17.0%, 95% CI for difference 6.3 to 27.6%, P?=?0.002) and the partner?+?alert arm (54.5%, difference vs. control of 18.6%, 95% CI for difference 6.6 to 30.5%, P?=?0.003). Adherence in the partner arm was not statistically significantly greater than control (43.2%, difference vs. control of 7.2%, 95% CI of difference -?5.2% to 19.5%, P?=?0.25). There were no statistically significant differences among the three treatment arms. Fewer participants invited a MAP in the partner?+?alert arm than the partner arm (P?=?0.02). CONCLUSIONS:Automated alerts were effective at improving medication adherence. Assigning a medication adherence partner did not statistically significantly affect adherence rates. TRIAL REGISTRATION:ClinicalTrials.gov Number NCT01890018 [ https://clinicaltrials.gov /].
| S-EPMC6109000 | biostudies-literature
Project description:The objective of this case report is to evaluate the use of a clinical data warehouse coupled with a clinical information system to test and refine alerts for medication orders control before they were fully implemented. A clinical decision rule refinement process was used to assess alerts. The criteria assessed were the frequencies of alerts for initial prescriptions of 10 medications whose dosage levels depend on renal function thresholds. In the first iteration of the process, the frequency of the 'exceeds maximum daily dose' alerts was 7.10% (617/8692), while that of the 'under dose' alerts was 3.14% (273/8692). Indicators were presented to the experts. During the different iterations of the process, 45 (16.07%) decision rules were removed, 105 (37.5%) were changed and 136 new rules were introduced. Extensive retrospective analysis of physicians' medication orders stored in a clinical data warehouse facilitates alert optimization toward the goal of maximizing the safety of the patient and minimizing overridden alerts.
| S-EPMC3422842 | biostudies-other
Project description:ObjectiveTo examine medication errors potentially related to computerized prescriber order entry (CPOE) and refine a previously published taxonomy to classify them.Materials and methodsWe reviewed all patient safety medication reports that occurred in the medication ordering phase from 6 sites participating in a United States Food and Drug Administration-sponsored project examining CPOE safety. Two pharmacists independently reviewed each report to confirm whether the error occurred in the ordering/prescribing phase and was related to CPOE. For those related to CPOE, we assessed whether CPOE facilitated (actively contributed to) the error or failed to prevent the error (did not directly cause it, but optimal systems could have potentially prevented it). A previously developed taxonomy was iteratively refined to classify the reports.ResultsOf 2522 medication error reports, 1308 (51.9%) were related to CPOE. Of these, CPOE facilitated the error in 171 (13.1%) and potentially could have prevented the error in 1137 (86.9%). The most frequent categories of "what happened to the patient" were delays in medication reaching the patient, potentially receiving duplicate drugs, or receiving a higher dose than indicated. The most frequent categories for "what happened in CPOE" included orders not routed to or received at the intended location, wrong dose ordered, and duplicate orders. Variations were seen in the format, categorization, and quality of reports, resulting in error causation being assignable in only 403 instances (31%).Discussion and conclusionErrors related to CPOE commonly involved transmission errors, erroneous dosing, and duplicate orders. More standardized safety reporting using a common taxonomy could help health care systems and vendors learn and implement prevention strategies.
| S-EPMC7651942 | biostudies-literature
Project description:IntroductionMedication organizations across the USA have adopted electronic health records, and one of the most anticipated benefits of these was improved medication safety, but alert fatigue has been a major issue.ObjectiveWe compared the appropriateness of medication-related clinical decision support alerts triggered by two commercial applications: EPIC and Seegnal's platform.MethodsThis was a retrospective comparison of two commercial applications. We provided Seegnal with deidentified inpatient, outpatient, and inpatient genetic electronic medical record (EMR)-extracted datasets for 657, 2731, and 413 patients, respectively. Seegnal then provided the alerts that would have triggered, which we compared with those triggered by EPIC in clinical care. A random sample of the alerts triggered were reviewed for appropriateness, and the positive predictive value (PPV) and negative predictive value (NPV) were calculated. We also reviewed all the inpatient and outpatient charts for patients within our cohort who were receiving ten or more concomitant medications with alerts we found to be appropriate to assess whether any adverse events had occurred and whether Seegnal's platform could have prevented them.ResultsResults from EPIC and the Seegnal platform were compared based on alert load, PPV, NPV, and potential adverse events. Overall, compared with EPIC, the Seegnal platform triggered fewer alerts in the inpatient (1697 vs. 27,540), outpatient (2341 vs. 35,134), and inpatient genetic (1493 vs. 20,975) cohorts. The Seegnal platform had higher specificity in the inpatient (99 vs. 0.3%; p < 0.0001), outpatient (99 vs. 0.3%; p < 0.0001), and inpatient genetic (97.9 vs. 1.2%; p < 0.0001) groups and higher sensitivity in the inpatient (100 vs. 68.8%; p < 0.0001) and outpatient (88.6 vs.78.3%; p < 0.0001) groups but not in the inpatient genetic cohort (81 vs. 78.5%; p = 0.11). We identified 16 adverse events that occurred in the inpatient setting, 11 (69%) of which potentially could have been prevented with the Seegnal platform.ConclusionsOverall, the Seegnal platform triggered 94% fewer alerts than EPIC in the inpatient setting and 93% fewer in the outpatient setting, with much higher sensitivity and specificity. This application could substantially reduce alert fatigue and improve medication safety at the same time.
| S-EPMC8184526 | biostudies-literature