Oncology pharmacist-led medication reconciliation among cancer patients initiating chemotherapy

2019 ◽  
Vol 26 (5) ◽  
pp. 1156-1163
Author(s):  
Danielle S Chun ◽  
Aimee Faso ◽  
Hyman B Muss ◽  
Hanna K Sanoff ◽  
John Valgus ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Cancer Patients ◽  
Medication Reconciliation ◽  
Patient Characteristics ◽  
Median Number ◽  
Health Record ◽  
Binomial Regression ◽  
Electronic Health ◽  
The University ◽  
Medication Changes

Background Pharmacist-led medication reconciliation (PMR) ensures adequate recording and use of medications by patients. PMR may be important for cancer patients initiating new therapies, as they have a high burden of medication use and are more susceptible to inadvertent medication discrepancies. To describe medication changes (additions, discontinuations, and modifications) made to the electronic health record during a PMR among cancer patients initiating chemotherapy. Methods From October 2011 to March 2012, 397 cancer patients initiating chemotherapy underwent a PMR at the University of North Carolina Cancer Hospital. Self-reported medications and those in the patients’ electronic health record were reviewed. Log-binomial regression models were used to estimate adjusted prevalence ratios and 95% confidence intervals for the associations between patient characteristics and medication changes made to the electronic health record. Results Mean age at time of the PMR was 58. Median number of medications taken prior to the PMR was 10 and median time to PMR completion was 11 min. Vitamins and herbal supplements accounted for the largest proportion of medication additions (38%) and modifications (20%). Antimicrobials accounted for the largest share of discontinuations (15%). After adjustment for all other covariates, patients aged 60–69 years were more likely to have additions than those aged 50 and under (aPR = 1.47, 95%CI: 1.10–1.97). Patients 70 years and over were more likely to have modifications (aPR = 1.74, 95%CI: 1.07–2.82). Conclusion Our results show that most cancer patients had a medication change in the electronic health record. A brief oncology PMR can accurately capture and improve medication safety by preventing prescribing and administration errors.

scholarly journals The Development and Piloting of the Ambulatory Electronic Health Record Evaluation Tool: Lessons Learned

2021 ◽  
Vol 12 (01) ◽  
pp. 153-163
Author(s):  
Zoe Co ◽  
A. Jay Holmgren ◽  
David C. Classen ◽  
Lisa P. Newmark ◽  
Diane L. Seger ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Medication Safety ◽  
Medication Reconciliation ◽  
The United States ◽  
Health Record ◽  
Evaluation Tool ◽  
Safety Test ◽  
Electronic Health ◽  
The Impact

Abstract Background Substantial research has been performed about the impact of computerized physician order entry on medication safety in the inpatient setting; however, relatively little has been done in ambulatory care, where most medications are prescribed. Objective To outline the development and piloting process of the Ambulatory Electronic Health Record (EHR) Evaluation Tool and to report the quantitative and qualitative results from the pilot. Methods The Ambulatory EHR Evaluation Tool closely mirrors the inpatient version of the tool, which is administered by The Leapfrog Group. The tool was piloted with seven clinics in the United States, each using a different EHR. The tool consists of a medication safety test and a medication reconciliation module. For the medication test, clinics entered test patients and associated test orders into their EHR and recorded any decision support they received. An overall percentage score of unsafe orders detected, and order category scores were provided to clinics. For the medication reconciliation module, clinics demonstrated how their EHR electronically detected discrepancies between two medication lists. Results For the medication safety test, the clinics correctly alerted on 54.6% of unsafe medication orders. Clinics scored highest in the drug allergy (100%) and drug–drug interaction (89.3%) categories. Lower scoring categories included drug age (39.3%) and therapeutic duplication (39.3%). None of the clinics alerted for the drug laboratory or drug monitoring orders. In the medication reconciliation module, three (42.8%) clinics had an EHR-based medication reconciliation function; however, only one of those clinics could demonstrate it during the pilot. Conclusion Clinics struggled in areas of advanced decision support such as drug age, drug laboratory, and drub monitoring. Most clinics did not have an EHR-based medication reconciliation function and this process was dependent on accessing patients' medication lists. Wider use of this tool could improve outpatient medication safety and can inform vendors about areas of improvement.

Using Statewide Electronic Health Record and Influenza Vaccination Data to Plan and Prioritize COVID-19 Vaccine Outreach and Communications in Wisconsin Communities

2021 ◽  
Vol 111 (12) ◽  
pp. 2111-2114
Author(s):  
Jessica Bonham-Werling ◽  
Allie J. DeLonay ◽  
Kristina Stephenson ◽  
Korina A. Hendricks ◽  
Lauren Bednarz ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Influenza Vaccination ◽  
Data Visualization ◽  
Health Record ◽  
Health Departments ◽  
Potential Barriers ◽  
University Of Wisconsin ◽  
Electronic Health ◽  
The University ◽  
Zip Code

The University of Wisconsin Neighborhood Health Partnerships Program used electronic health record and influenza vaccination data to estimate COVID-19 relative mortality risk and potential barriers to vaccination in Wisconsin ZIP Code Tabulation Areas. Data visualization revealed four groupings to use in planning and prioritizing vaccine outreach and communication based on ZIP Code Tabulation Area characteristics. The program provided data, visualization, and guidance to health systems, health departments, nonprofits, and others to support planning targeted outreach approaches to increase COVID-19 vaccination uptake. (Am J Public Health. 2021;111(12):2111–2114. https://doi.org/10.2105/AJPH.2021.306524 )

scholarly journals External Validity of Electronic Health Record Studies of Cancer Patients

2017 ◽  
Vol 4 (3) ◽  
pp. 150
Author(s):  
Anqi Jin ◽  
Scarlett Gomez ◽  
Harold Luft ◽  
Daphne Lichtensztajn ◽  
Caroline Thompson
Keyword(s):  
Electronic Health Record ◽  
Cancer Patients ◽  
External Validity ◽  
Health Record ◽  
Electronic Health

scholarly journals Caregivers' role in using a personal electronic health record: a qualitative study of cancer patients and caregivers in Germany

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Aline Weis ◽  
Sabrina Pohlmann ◽  
Regina Poss-Doering ◽  
Beate Strauss ◽  
Charlotte Ullrich ◽  
...  
Keyword(s):  
Qualitative Study ◽  
Electronic Health Record ◽  
Cancer Patients ◽  
Health Record ◽  
Electronic Health

scholarly journals Validation of an Association Rule Mining-Based Method to Infer Associations Between Medications and Problems

2013 ◽  
Vol 04 (01) ◽  
pp. 100-109 ◽  
Author(s):  
A. McCoy ◽  
S. Henkin ◽  
M. Flaherty ◽  
D. Sittig ◽  
A. Wright
Keyword(s):  
Electronic Health Record ◽  
Association Rule ◽  
Association Rule Mining ◽  
Health Science ◽  
Health Record ◽  
Rule Mining ◽  
University Of Texas ◽  
Medication Problem ◽  
Electronic Health ◽  
The University

SummaryBackground: In a prior study, we developed methods for automatically identifying associations between medications and problems using association rule mining on a large clinical data warehouse and validated these methods at a single site which used a self-developed electronic health record. Objective: To demonstrate the generalizability of these methods by validating them at an external site.Methods: We received data on medications and problems for 263,597 patients from the University of Texas Health Science Center at Houston Faculty Practice, an ambulatory practice that uses the Allscripts Enterprise commercial electronic health record product. We then conducted association rule mining to identify associated pairs of medications and problems and characterized these associations with five measures of interestingness: support, confidence, chi-square, interest and conviction and compared the top-ranked pairs to a gold standard.Results: 25,088 medication-problem pairs were identified that exceeded our confidence and support thresholds. An analysis of the top 500 pairs according to each measure of interestingness showed a high degree of accuracy for highly-ranked pairs.Conclusion: The same technique was successfully employed at the University of Texas and accuracy was comparable to our previous results. Top associations included many medications that are highly specific for a particular problem as well as a large number of common, accurate medication-problem pairs that reflect practice patterns.Citation: Wright A, McCoy A, Henkin S, Flaherty M, Sittig D. Validation of an association rule mining-based method to infer associations between medications and problems. ppl Clin Inf 2013; 4: 100–109http://dx.doi.org/10.4338/ACI-2012-12-RA-0051

Electronic health record use among cancer patients: Insights from the Health Information National Trends Survey

2017 ◽  
Vol 25 (1) ◽  
pp. 83-90 ◽  
Author(s):  
Yulia A Strekalova
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Cancer Patients ◽  
Health Information ◽  
Information Seeking ◽  
Health Record ◽  
Online Information ◽  
Health Records ◽  
Electronic Health ◽  
National Trends

Over 90% of US hospitals provide patients with access to e-copy of their health records, but the utilization of electronic health records by the US consumers remains low. Guided by the comprehensive information-seeking model, this study used data from the National Cancer Institute’s Health Information National Trends Survey 4 (Cycle 4) and examined the factors that explain the level of electronic health record use by cancer patients. Consistent with the model, individual information-seeking factors and perceptions of security and utility were associated with the frequency of electronic health record access. Specifically, higher income, prior online information seeking, interest in accessing health information online, and normative beliefs were predictive of electronic health record access. Conversely, poorer general health status and lack of health care provider encouragement to use electronic health records were associated with lower utilization rates. The current findings provide theory-based evidence that contributes to the understanding of the explanatory factors of electronic health record use and suggest future directions for research and practice.

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.

The Electronic Health Record as the Primary Data Source in a Pragmatic Trial: A Case Study

2022 ◽  
pp. 0272989X2110699
Author(s):  
Louise B. Russell ◽  
Qian Huang ◽  
Yuqing Lin ◽  
Laurie A. Norton ◽  
Jingsan Zhu ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Ejection Fraction ◽  
Electronic Health Record ◽  
Body Mass ◽  
Hospital Admissions ◽  
Pragmatic Trial ◽  
Patient Characteristics ◽  
Health Record ◽  
State Data ◽  
Electronic Health

Introduction. Pragmatic clinical trials test interventions in patients representative of real-world medical practice and reduce data collection costs by using data recorded in the electronic health record (EHR) during usual care. We describe our experience using the EHR to measure the primary outcome of a pragmatic trial, hospital readmissions, and important clinical covariates. Methods. The trial enrolled patients recently discharged from the hospital for treatment of heart failure to test whether automated daily monitoring integrated into the EHR could reduce readmissions. The study team used data from the EHR and several data systems that drew on the EHR, supplemented by the hospital admissions files of three states. Results. Almost three-quarters of enrollees’ readmissions over the 12-mo trial period were captured by the EHRs of the study hospitals. State data, which took 7 mo to more than 2 y from first contact to receipt of first data, provided the remaining one-quarter. Considerable expertise was required to resolve differences between the 2 data sources. Common covariates used in trial analyses, such as weight and body mass index during the index hospital stay, were available for >97% of enrollees from the EHR. Ejection fraction, obtained from echocardiograms, was available for only 47.6% of enrollees within the 6-mo window that would likely be expected in a traditional trial. Discussion. In this trial, patient characteristics and outcomes were collected from existing EHR systems, but, as usual for EHRs, they could not be standardized for date or method of measurement and required substantial time and expertise to collect and curate. Hospital admissions, the primary trial outcome, required additional effort to locate and use supplementary sources of data. Highlights Electronic health records are not a single system but a series of overlapping and legacy systems that require time and expertise to use efficiently. Commonly measured patient characteristics such as weight and body mass index are relatively easy to locate for most trial enrollees but less common characteristics, like ejection fraction, are not. Acquiring essential supplementary data—in this trial, state data on hospital admission—can be a lengthy and difficult process.

scholarly journals Design and Pilot Implementation of an Electronic Health Record-Based System to Automatically Refer Cancer Patients to Tobacco Use Treatment

2020 ◽  
Vol 17 (11) ◽  
pp. 4054 ◽  
Author(s):  
Thulasee Jose ◽  
Joshua W. Ohde ◽  
J. Taylor Hays ◽  
Michael V. Burke ◽  
David O. Warner
Keyword(s):  
Electronic Health Record ◽  
Cancer Patients ◽  
Tobacco Use ◽  
Mayo Clinic ◽  
Cancer Center ◽  
Health Record ◽  
Oncology Patients ◽  
Tobacco Treatment ◽  
Opt Out ◽  
Electronic Health

Continued tobacco use after cancer diagnosis is detrimental to treatment and survivorship. The current reach of evidence-based tobacco treatments in cancer patients is low. As a part of the National Cancer Institute Cancer Center Cessation Initiative, the Mayo Clinic Cancer Center designed an electronic health record (EHR, Epic©)-based process to automatically refer ambulatory oncology patients to tobacco use treatment, regardless of intent to cease tobacco use(“opt out”). The referral and patient scheduling, accomplished through a best practice advisory (BPA) directed to staff who room patients, does not require a co-signature from clinicians. This process was piloted for a six-week period starting in July of 2019 at the Division of Medical Oncology, Mayo Clinic, Rochester, MN. All oncology patients who were tobacco users were referred for tobacco treatment by the rooming staff (n = 210). Of these, 150 (71%) had a tobacco treatment appointment scheduled, and 25 (17%) completed their appointment. We conclude that an EHR-based “opt-out” approach to refer patients to tobacco dependence treatment that does not require active involvement by clinicians is feasible within the oncology clinical practice. Further work is needed to increase the proportion of scheduled patients who attend their appointments.

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