scholarly journals Electronic Health Record–Based Strategy to Promote Medication Adherence Among Patients With Diabetes: Longitudinal Observational Study

10.2196/13499 ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. e13499 ◽  
Author(s):  
Stacy Cooper Bailey ◽  
Amisha Wallia ◽  
Sarah Wright ◽  
Guisselle A Wismer ◽  
Alexandra C Infanzon ◽  
...  
Keyword(s):  
Medication Adherence ◽  
Electronic Health Record ◽  
Low Cost ◽  
Clinical Decision ◽  
Health Record ◽  
Low Literacy ◽  
Minimal Impact ◽  
Patients With Diabetes ◽  
Electronic Health

Background Poor medication adherence is common; however, few mechanisms exist in clinical practice to monitor how patients take medications in outpatient settings. Objective This study aimed to pilot test the Electronic Medication Complete Communication (EMC2) strategy, a low-cost, sustainable approach that uses functionalities within the electronic health record to promote outpatient medication adherence and safety. Methods The EMC2 strategy was implemented in 2 academic practices for 14 higher-risk diabetes medications. The strategy included: (1) clinical decision support alerts to prompt provider counseling on medication risks, (2) low-literacy medication summaries for patients, (3) a portal-based questionnaire to monitor outpatient medication use, and (4) clinical outreach for identified concerns. We recruited adult patients with diabetes who were prescribed a higher-risk diabetes medication. Participants completed baseline and 2-week interviews to assess receipt of, and satisfaction with, intervention components. Results A total of 100 patients were enrolled; 90 completed the 2-week interview. Patients were racially diverse, 30.0% (30/100) had a high school education or less, and 40.0% (40/100) had limited literacy skills. About a quarter (28/100) did not have a portal account; socioeconomic disparities were noted in account ownership by income and education. Among patients with a portal account, 58% (42/72) completed the questionnaire; 21 of the 42 patients reported concerns warranting clinical follow-up. Of these, 17 were contacted by the clinic or had their issue resolved within 24 hours. Most patients (33/38, 89%) who completed the portal questionnaire and follow-up interview reported high levels of satisfaction (score of 8 or greater on a scale of 1-10). Conclusions Findings suggest that the EMC2 strategy can be reliably implemented and delivered to patients, with high levels of satisfaction. Disparities in portal use may restrict intervention reach. Although the EMC2 strategy can be implemented with minimal impact on clinic workflow, future trials are needed to evaluate its effectiveness to promote adherence and safety.

scholarly journals Electronic Health Record–Based Strategy to Promote Medication Adherence Among Patients With Diabetes: Longitudinal Observational Study (Preprint)

2019 ◽  
Author(s):  
Stacy Cooper Bailey ◽  
Amisha Wallia ◽  
Sarah Wright ◽  
Guisselle A Wismer ◽  
Alexandra C Infanzon ◽  
...  
Keyword(s):  
Medication Adherence ◽  
Electronic Health Record ◽  
Low Cost ◽  
Clinical Decision ◽  
Health Record ◽  
Low Literacy ◽  
Minimal Impact ◽  
Patients With Diabetes ◽  
Electronic Health

BACKGROUND Poor medication adherence is common; however, few mechanisms exist in clinical practice to monitor how patients take medications in outpatient settings. OBJECTIVE This study aimed to pilot test the Electronic Medication Complete Communication (EMC<sup>2</sup>) strategy, a low-cost, sustainable approach that uses functionalities within the electronic health record to promote outpatient medication adherence and safety. METHODS The EMC<sup>2</sup> strategy was implemented in 2 academic practices for 14 higher-risk diabetes medications. The strategy included: (1) clinical decision support alerts to prompt provider counseling on medication risks, (2) low-literacy medication summaries for patients, (3) a portal-based questionnaire to monitor outpatient medication use, and (4) clinical outreach for identified concerns. We recruited adult patients with diabetes who were prescribed a higher-risk diabetes medication. Participants completed baseline and 2-week interviews to assess receipt of, and satisfaction with, intervention components. RESULTS A total of 100 patients were enrolled; 90 completed the 2-week interview. Patients were racially diverse, 30.0% (30/100) had a high school education or less, and 40.0% (40/100) had limited literacy skills. About a quarter (28/100) did not have a portal account; socioeconomic disparities were noted in account ownership by income and education. Among patients with a portal account, 58% (42/72) completed the questionnaire; 21 of the 42 patients reported concerns warranting clinical follow-up. Of these, 17 were contacted by the clinic or had their issue resolved within 24 hours. Most patients (33/38, 89%) who completed the portal questionnaire and follow-up interview reported high levels of satisfaction (score of 8 or greater on a scale of 1-10). CONCLUSIONS Findings suggest that the EMC<sup>2</sup> strategy can be reliably implemented and delivered to patients, with high levels of satisfaction. Disparities in portal use may restrict intervention reach. Although the EMC<sup>2</sup> strategy can be implemented with minimal impact on clinic workflow, future trials are needed to evaluate its effectiveness to promote adherence and safety.

scholarly journals Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study (Preprint)

2018 ◽  
Author(s):  
Azraa Amroze ◽  
Terry S Field ◽  
Hassan Fouayzi ◽  
Devi Sundaresan ◽  
Laura Burns ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Primary Care Physicians ◽  
Controlled Trial ◽  
Patient Characteristics ◽  
Clinical Decision ◽  
Descriptive Study ◽  
Health Record ◽  
Log Data ◽  
Electronic Health

BACKGROUND Electronic health record (EHR) access and audit logs record behaviors of providers as they navigate the EHR. These data can be used to better understand provider responses to EHR–based clinical decision support (CDS), shedding light on whether and why CDS is effective. OBJECTIVE This study aimed to determine the feasibility of using EHR access and audit logs to track primary care physicians’ (PCPs’) opening of and response to noninterruptive alerts delivered to EHR InBaskets. METHODS We conducted a descriptive study to assess the use of EHR log data to track provider behavior. We analyzed data recorded following opening of 799 noninterruptive alerts sent to 75 PCPs’ InBaskets through a prior randomized controlled trial. Three types of alerts highlighted new medication concerns for older patients’ posthospital discharge: information only (n=593), medication recommendations (n=37), and test recommendations (n=169). We sought log data to identify the person opening the alert and the timing and type of PCPs’ follow-up EHR actions (immediate vs by the end of the following day). We performed multivariate analyses examining associations between alert type, patient characteristics, provider characteristics, and contextual factors and likelihood of immediate or subsequent PCP action (general, medication-specific, or laboratory-specific actions). We describe challenges and strategies for log data use. RESULTS We successfully identified the required data in EHR access and audit logs. More than three-quarters of alerts (78.5%, 627/799) were opened by the PCP to whom they were directed, allowing us to assess immediate PCP action; of these, 208 alerts were followed by immediate action. Expanding on our analyses to include alerts opened by staff or covering physicians, we found that an additional 330 of the 799 alerts demonstrated PCP action by the end of the following day. The remaining 261 alerts showed no PCP action. Compared to information-only alerts, the odds ratio (OR) of immediate action was 4.03 (95% CI 1.67-9.72) for medication-recommendation and 2.14 (95% CI 1.38-3.32) for test-recommendation alerts. Compared to information-only alerts, ORs of medication-specific action by end of the following day were significantly greater for medication recommendations (5.59; 95% CI 2.42-12.94) and test recommendations (1.71; 95% CI 1.09-2.68). We found a similar pattern for OR of laboratory-specific action. We encountered 2 main challenges: (1) Capturing a historical snapshot of EHR status (number of InBasket messages at time of alert delivery) required incorporation of data generated many months prior with longitudinal follow-up. (2) Accurately interpreting data elements required iterative work by a physician/data manager team taking action within the EHR and then examining audit logs to identify corresponding documentation. CONCLUSIONS EHR log data could inform future efforts and provide valuable information during development and refinement of CDS interventions. To address challenges, use of these data should be planned before implementing an EHR–based study.

scholarly journals Dissemination of child abuse clinical decision support: Moving beyond a single electronic health record

2021 ◽  
Vol 147 ◽  
pp. 104349
Author(s):  
Thomas McGinn ◽  
David A. Feldstein ◽  
Isabel Barata ◽  
Emily Heineman ◽  
Joshua Ross ◽  
...  
Keyword(s):  
Child Abuse ◽  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Electronic Health

scholarly journals Developing Clinical Decision Support within a Commercial Electronic Health Record System to Improve Antimicrobial Prescribing in the Neonatal ICU

2014 ◽  
Vol 05 (02) ◽  
pp. 368-387 ◽  
Author(s):  
K. Cato ◽  
B. Sheehan ◽  
S. Patel ◽  
J. Duchon ◽  
P. DeLaMora ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Electronic Health Record System ◽  
Health Record ◽  
Record System ◽  
Antimicrobial Prescribing ◽  
Neonatal Icu ◽  
Electronic Health

SummaryObjective: To develop and implement a clinical decision support (CDS) tool to improve antibiotic prescribing in neonatal intensive care units (NICUs) and to evaluate user acceptance of the CDS tool.Methods: Following sociotechnical analysis of NICU prescribing processes, a CDS tool for empiric and targeted antimicrobial therapy for healthcare-associated infections (HAIs) was developed and incorporated into a commercial electronic health record (EHR) in two NICUs. User logs were reviewed and NICU prescribers were surveyed for their perceptions of the CDS tool.Results: The CDS tool aggregated selected laboratory results, including culture results, to make treatment recommendations for common clinical scenarios. From July 2010 to May 2012, 1,303 CDS activations for 452 patients occurred representing 22% of patients prescribed antibiotics during this period. While NICU clinicians viewed two culture results per tool activation, prescribing recommendations were viewed during only 15% of activations. Most (63%) survey respondents were aware of the CDS tool, but fewer (37%) used it during their most recent NICU rotation. Respondents considered the most useful features to be summarized culture results (43%) and antibiotic recommendations (48%).Discussion: During the study period, the CDS tool functionality was hindered by EHR upgrades, implementation of a new laboratory information system, and changes to antimicrobial testing methodologies. Loss of functionality may have reduced viewing antibiotic recommendations. In contrast, viewing culture results was frequently performed, likely because this feature was perceived as useful and functionality was preserved.Conclusion: To improve CDS tool visibility and usefulness, we recommend early user and information technology team involvement which would facilitate use and mitigate implementation challenges.Citation: Hum RS, Cato K, Sheehan B, Patel S, Duchon J, DeLaMora P, Ferng YH, Graham P, Vawdrey DK, Perlman J, Larson E, Saiman L. Developing clinical decision support within a commercial electronic health record system to improve antimicrobial prescribing in the neonatal ICU. Appl Clin Inf 2014; 5: 368–387 http://dx.doi.org/10.4338/ACI-2013-09-RA-0069

scholarly journals Integrating pharmacogenetic information and clinical decision support into the electronic health record

2014 ◽  
Vol 21 (3) ◽  
pp. 522-528 ◽  
Author(s):  
Barry R Goldspiel ◽  
Willy A Flegel ◽  
Gary DiPatrizio ◽  
Tristan Sissung ◽  
Sharon D Adams ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Electronic Health

scholarly journals Clinical decision support alert malfunctions: analysis and empirically derived taxonomy

2017 ◽  
Vol 25 (5) ◽  
pp. 496-506 ◽  
Author(s):  
Adam Wright ◽  
Angela Ai ◽  
Joan Ash ◽  
Jane F Wiesen ◽  
Thu-Trang T Hickman ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Quantitative Methods ◽  
Clinical Decision ◽  
The United States ◽  
Health Record ◽  
Qualitative And Quantitative Methods ◽  
Electronic Health

Abstract Objective To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. Materials and Methods We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. Results We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Discussion Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. Conclusion CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

scholarly journals A dynamic reaction picklist for improving allergy reaction documentation in the electronic health record

2020 ◽  
Vol 27 (6) ◽  
pp. 917-923
Author(s):  
Liqin Wang ◽  
Suzanne V Blackley ◽  
Kimberly G Blumenthal ◽  
Sharmitha Yerneni ◽  
Foster R Goss ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Language Processing ◽  
Clinical Decision ◽  
Free Text ◽  
Health Record ◽  
Dynamic Reaction ◽  
Value Set ◽  
Expert Review ◽  
Electronic Health ◽  
Development And Validation

Abstract Objective Incomplete and static reaction picklists in the allergy module led to free-text and missing entries that inhibit the clinical decision support intended to prevent adverse drug reactions. We developed a novel, data-driven, “dynamic” reaction picklist to improve allergy documentation in the electronic health record (EHR). Materials and Methods We split 3 decades of allergy entries in the EHR of a large Massachusetts healthcare system into development and validation datasets. We consolidated duplicate allergens and those with the same ingredients or allergen groups. We created a reaction value set via expert review of a previously developed value set and then applied natural language processing to reconcile reactions from structured and free-text entries. Three association rule-mining measures were used to develop a comprehensive reaction picklist dynamically ranked by allergen. The dynamic picklist was assessed using recall at top k suggested reactions, comparing performance to the static picklist. Results The modified reaction value set contained 490 reaction concepts. Among 4 234 327 allergy entries collected, 7463 unique consolidated allergens and 469 unique reactions were identified. Of the 3 dynamic reaction picklists developed, the 1 with the optimal ranking achieved recalls of 0.632, 0.763, and 0.822 at the top 5, 10, and 15, respectively, significantly outperforming the static reaction picklist ranked by reaction frequency. Conclusion The dynamic reaction picklist developed using EHR data and a statistical measure was superior to the static picklist and suggested proper reactions for allergy documentation. Further studies might evaluate the usability and impact on allergy documentation in the EHR.

scholarly journals Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation

2019 ◽  
Vol 28 (9) ◽  
pp. 762-768 ◽  
Author(s):  
Norman Lance Downing ◽  
Joshua Rolnick ◽  
Sarah F Poole ◽  
Evan Hall ◽  
Alexander J Wessels ◽  
...  
Keyword(s):  
Severe Sepsis ◽  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Tertiary Care ◽  
Clinical Decision ◽  
Health Record ◽  
Hospitalised Patients ◽  
Significant Difference ◽  
Electronic Health

BackgroundSepsis remains the top cause of morbidity and mortality of hospitalised patients despite concerted efforts. Clinical decision support for sepsis has shown mixed results reflecting heterogeneous populations, methodologies and interventions.ObjectivesTo determine whether the addition of a real-time electronic health record (EHR)-based clinical decision support alert improves adherence to treatment guidelines and clinical outcomes in hospitalised patients with suspected severe sepsis.DesignPatient-level randomisation, single blinded.SettingMedical and surgical inpatient units of an academic, tertiary care medical centre.Patients1123 adults over the age of 18 admitted to inpatient wards (intensive care units (ICU) excluded) at an academic teaching hospital between November 2014 and March 2015.InterventionsPatients were randomised to either usual care or the addition of an EHR-generated alert in response to a set of modified severe sepsis criteria that included vital signs, laboratory values and physician orders.Measurements and main resultsThere was no significant difference between the intervention and control groups in primary outcome of the percentage of patients with new antibiotic orders at 3 hours after the alert (35% vs 37%, p=0.53). There was no difference in secondary outcomes of in-hospital mortality at 30 days, length of stay greater than 72 hours, rate of transfer to ICU within 48 hours of alert, or proportion of patients receiving at least 30 mL/kg of intravenous fluids.ConclusionsAn EHR-based severe sepsis alert did not result in a statistically significant improvement in several sepsis treatment performance measures.

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