Impact of a simulated electronic health record on pharmacy students’ perceptions of preparedness for clinical practice

2018 ◽  
Vol 10 (12) ◽  
pp. 1624-1630 ◽  
Author(s):  
Jennifer N. Smith ◽  
Jean M. Scholtz
Keyword(s):  
Clinical Practice ◽  
Electronic Health Record ◽  
Pharmacy Students ◽  
Health Record ◽  
Electronic Health

scholarly journals Implementation of Surprise Question Assessments using the Electronic Health Record in Older Adults with Advanced CKD

Kidney360 ◽  
2021 ◽  
pp. 10.34067/KID.0007062020
Author(s):  
Natalie C. Ernecoff ◽  
Khaled Abdel-Kader ◽  
Mangi Cai ◽  
Jonathan Yabes ◽  
Nirav Shah ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Electronic Health Record ◽  
Best Practice ◽  
Health Record ◽  
Clinical Workflow ◽  
Goals Of Care ◽  
Prognostic Utility ◽  
Electronic Health ◽  
Best Practice Alert ◽  
Surprise Question

Background. The Surprise Question (SQ; "Would you be surprised if this patient died in the next 12 months?") is a validated prognostication tool for mortality and hospitalization among patients with advanced CKD. Barriers in clinical workflow have slowed SQ implementation into practice. Objectives. (1) To evaluate implementation outcomes following use of electronic health record (EHR) decision support to automate collection of the SQ. (2) To assess the prognostic utility of the SQ for mortality and hospitalization/emergency room (ER) visits. Methods. We developed and implemented a best practice alert (BPA) in the electronic health record (EHR) to identify nephrology outpatients > 60 years of age with an eGFR<30 ml/min. At appointment, the BPA prompted the physician to answer the SQ. We assessed the rate and timeliness of provider responses. We conducted a post-hoc open-ended survey to assess physician perceptions of SQ implementation. We assessed the SQ's prognostic utility in survival and time-to-hospital encounter (hospitalization/ER visit) analyses. Results. Among 510 patients for whom the BPA triggered, 95 (18.6%) had the SQ completed by 16 physicians. Among those completed, nearly all (97.9%) were on appointment day, and 61 (64.2%) the first time the BPA fired. Providers answered "No" for 27 (28.4%) and "Yes" for 68 (71.6%) patients. By 12 months, 6 (22.2%) "No" patients died; 3 (4.4%) "Yes" patients died (hazards ratio [HR] 2.86, ref:Yes, 95% CI[1.06, 7.69]). About 35% of "No" patients and 32% of "Yes" patients had a hospital encounter by 12 months (HR 1.85, ref:Yes, 95% CI[0.93, 3.69]). Physicians noted (1) they had goals-of-care conversations unprompted; (2) EHR-based interventions alone for goals-of-care are ineffective; and (3) more robust engagement is necessary. Conclusions. We successfully integrated the SQ into the EHR to aid in clinical practice. Additional implementation efforts are needed to encourage further integration of the SQ in clinical practice.

Small Practices, Big (QI) Dreams: Customizing QI Efforts for Under-resourced Primary Care Practices to Improve Diabetes Disparities (Preprint)

2020 ◽  
Author(s):  
Sahnah Lim ◽  
Nadia S. Islam
Keyword(s):  
Primary Care ◽  
Clinical Practice ◽  
Electronic Health Record ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Low Income ◽  
Great Promise ◽  
Health Record ◽  
Small Practice ◽  
Electronic Health

UNSTRUCTURED Electronic health record quality improvement (QI) initiatives hold great promise in improving adoption of clinical practice guidelines, including those related to diabetes. QI initiatives implemented in under-resourced primary care settings that primarily serve racial/ethnic minority populations have potential to improve quality of care and ultimately improve diabetes disparities. The “Screen at 23” campaign was launched in 2011 to increase screening for prediabetes and diabetes at lower body mass index (BMI) thresholds (i.e., 23 kg/m2) for Asian Americans, in line with the new guidelines put forth by the American Diabetes Association. Here, we describe the implementation of a customized electronic health record QI initiative in under-resourced practices that primarily serve low-income South Asian populations in New York City, designed to increase diabetes screening using updated BMI guidelines and in alignment with the “Screen at 23” campaign. The customization involved the implementation of an innovative, semi-manual alternate solution to automated clinical decision support systems (CDSS) alerts in order to address the restrictions on customizing CDSS alerts in electronic health record platforms used in small practice settings. We also discuss challenges and strategies with this customized QI effort. Our experience suggests that multi-sector partnership engagement, user-centered approaches, and relationship-building with key stakeholders are even more critical in under-resourced, and small practice settings. Relatively simple technological solutions can be greatly beneficial in enhancing small practice capacity to engage in larger-scale QI initiatives. Tailored, context-driven approaches for implementation of equity-focused QI initiatives such as the one we describe can increase adoption of clinical practice guidelines, improve diabetes-related outcomes, and improve health disparities among under-served populations. INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1186/s13063-019-3711-y

Pharmacy students' readiness to use the electronic health record: A tale of two institutions

2020 ◽  
Author(s):  
Kristen Cook ◽  
Gary Cochran ◽  
Helena Gali ◽  
Thomas Hatch ◽  
Linda Awdishu ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Pharmacy Students ◽  
Health Record ◽  
Electronic Health

scholarly journals Identifying clinical features in primary care electronic health record studies: methods for codelist development

BMJ Open ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. e019637 ◽  
Author(s):  
Jessica Watson ◽  
Brian D Nicholson ◽  
Willie Hamilton ◽  
Sarah Price
Keyword(s):  
Primary Care ◽  
Sensitivity Analysis ◽  
Clinical Practice ◽  
Electronic Health Record ◽  
Clinical Features ◽  
Complete Agreement ◽  
Health Record ◽  
Shortness Of Breath ◽  
Statistical Software ◽  
Electronic Health

ObjectiveAnalysis of routinely collected electronic health record (EHR) data from primary care is reliant on the creation of codelists to define clinical features of interest. To improve scientific rigour, transparency and replicability, we describe and demonstrate a standardised reproducible methodology for clinical codelist development.DesignWe describe a three-stage process for developing clinical codelists. First, the clear definition a priori of the clinical feature of interest using reliable clinical resources. Second, development of a list of potential codes using statistical software to comprehensively search all available codes. Third, a modified Delphi process to reach consensus between primary care practitioners on the most relevant codes, including the generation of an ‘uncertainty’ variable to allow sensitivity analysis.SettingThese methods are illustrated by developing a codelist for shortness of breath in a primary care EHR sample, including modifiable syntax for commonly used statistical software.ParticipantsThe codelist was used to estimate the frequency of shortness of breath in a cohort of 28 216 patients aged over 18 years who received an incident diagnosis of lung cancer between 1 January 2000 and 30 November 2016 in the Clinical Practice Research Datalink (CPRD).ResultsOf 78 candidate codes, 29 were excluded as inappropriate. Complete agreement was reached for 44 (90%) of the remaining codes, with partial disagreement over 5 (10%). 13 091 episodes of shortness of breath were identified in the cohort of 28 216 patients. Sensitivity analysis demonstrates that codes with the greatest uncertainty tend to be rarely used in clinical practice.ConclusionsAlthough initially time consuming, using a rigorous and reproducible method for codelist generation ‘future-proofs’ findings and an auditable, modifiable syntax for codelist generation enables sharing and replication of EHR studies. Published codelists should be badged by quality and report the methods of codelist generation including: definitions and justifications associated with each codelist; the syntax or search method; the number of candidate codes identified; and the categorisation of codes after Delphi review.

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