Should we Set a Formalized Discharge Instruction Education Standard?

Smart AIM: With a more formalized discharge instruction evaluation process for PGY-1s, discharge instructions for specific diagnoses will have less error in a year’s time.https://jdc.jefferson.edu/patientsafetyposters/1035/thumbnail.jp

Discrepancies in Written Versus Calculated Durations in Opioid Prescriptions: Pre-Post Study.

BACKGROUND: The United States is in the midst of an opioid epidemic. Long-term use of opioid medications is associated with an increased risk of dependence. The US Centers for Disease Control and Prevention makes specific recommendations regarding opioid prescribing, including that prescription quantities should not exceed the intended duration of treatment. OBJECTIVE: The purpose of this study was to determine if opioid prescription quantities written at our institution exceed intended duration of treatment and whether enhancements to our electronic health record system improved any discrepancies. METHODS: We examined the opioid prescriptions written at our institution for a 22-month period. We examined the duration of treatment documented in the prescription itself and calculated a duration based on the quantity of tablets and doses per day. We determined whether requiring documentation of the prescription duration affected these outcomes. RESULTS: We reviewed 72,314 opioid prescriptions, of which 16.96% had a calculated duration that was greater than what wasdocumented in the prescription. Making the duration a required field significantly reduced this discrepancy (17.95% vs 16.21%,P CONCLUSIONS: Health information technology vendors should develop tools that, by default, accurately represent prescription durations and/or modify doses and quantities dispensed based on provider-entered durations. This would potentially reduce unintended prolonged opioid use and reduce the potential for long-term dependence

Understanding subscription-based automated electronic notification usage in hospitals: a qualitative study

Introduction: The potential exists for patient harm when clinicians do not respond to clinical results in a timely manner. Much of the research on asynchronous electronic notifications (pager, cellphone or wearable devices) comes from home-grown electronic health records (EHR). Little is known about factors influencing notifications from vendor-based EHRs and the effect of subscription-based notifications, where providers select which results for notification. This study investigates what factors influence a clinician’s decision to order result notifications, the perceived role of these notifications in the clinical setting, and ways to improve notifications in a vendor-based EHR. Methods: We queried the EHR to identify clinicians using the “bell” notification functionality of Epic (Epic Systems, Verona WI) in an academic health system comprised of one tertiary care center and one community hospital. We distributed an online survey to individuals identified to have used the functionality via retrospective review of clinical results over a 12-month period. We received completed surveys from a diverse group of clinical staff, and we used descriptive statistics to analyze the survey data. Results: Via retrospective review, we identified 874 individuals who used the system. Of these, we received 67 (7.7%) survey responses. Of those who responded, 72% reported the functionality is “likely to help” patients, and 66% of respondents believe the functionality “speeds up workflow.” Additionally, 41% of clinicians stated they had experiences where the “bell” notification “improved patient outcomes.” Discussion: A majority of clinicians viewed the subscription-based “bell” notification functionality favorably, would recommend it to other clinicians, and stated that it has a positive impact on workflow. However, only a minority of clinicians identified specific instances where the functionality improved patient care. Further research should prospectively identify what factors impact how and why healthcare providers use subscription-based notifications and if use of these systems can improve patient outcomes

The “OK” Guideline: Implementing an Electronic Electrolyte Repletion Guideline for Improving Rates of Oral Potassium and Magnesium Delivery

Nearly 500,000 doses of potassium (K) and magnesium (Mg) are given at Thomas Jefferson University Hospital (TJUH) each year. More than 80% of these doses are given intravenously. Guidelines that encourage both intravenous and oral (PO) repletion options increase rates of PO dosing and more successfully attain goal levels than standard care. Our goal was to increase the percent of K and Mg doses delivered by oral route to \u3e50% of total doses distributed at TJUH within one year of implementation of an Epic-based electronic order set

Decision Counseling in Prostate Cancer Screening

No abstract available

Reduction in Hospital System Opioid Prescribing for Acute Pain Through Default Prescription Preference Settings: Pre-Post Study

BACKGROUND: The United States is in an opioid epidemic. Passive decision support in the electronic health record (EHR) through opioid prescription presets may aid in curbing opioid dependence. OBJECTIVE: The objective of this study is to determine whether modification of opioid prescribing presets in the EHR could change prescribing patterns for an entire hospital system. METHODS: We performed a quasi-experimental retrospective pre-post analysis of a 24-month period before and after modifications to our EHR\u27s opioid prescription presets to match Centers for Disease Control and Prevention guidelines. We included all opioid prescriptions prescribed at our institution for nonchronic pain. Our modifications to the EHR include (1) making duration of treatment for an opioid prescription mandatory, (2) adding a quick button for 3 days\u27 duration while removing others, and (3) setting the default quantity of all oral opioid formulations to 10 tablets. We examined the quantity in tablets, duration in days, and proportion of prescriptions greater than 90 morphine milligram equivalents/day for our hospital system, and compared these values before and after our intervention for effect. RESULTS: There were 78,246 prescriptions included in our study written on 30,975 unique patients. There was a significant reduction for all opioid prescriptions pre versus post in (1) the overall median quantity of tablets dispensed (54 [IQR 40-120] vs 42 [IQR 18-90]; PPP\u3c.001). CONCLUSIONS: Modifications of opioid prescribing presets in the EHR can improve prescribing practice patterns. Reducing duration and quantity of opioid prescriptions could reduce the risk of dependence and overdose

Development and Validation of ML-DQA -- a Machine Learning Data Quality Assurance Framework for Healthcare

The approaches by which the machine learning and clinical research communities utilize real world data (RWD), including data captured in the electronic health record (EHR), vary dramatically. While clinical researchers cautiously use RWD for clinical investigations, ML for healthcare teams consume public datasets with minimal scrutiny to develop new algorithms. This study bridges this gap by developing and validating ML-DQA, a data quality assurance framework grounded in RWD best practices. The ML-DQA framework is applied to five ML projects across two geographies, different medical conditions, and different cohorts. A total of 2,999 quality checks and 24 quality reports were generated on RWD gathered on 247,536 patients across the five projects. Five generalizable practices emerge: all projects used a similar method to group redundant data element representations; all projects used automated utilities to build diagnosis and medication data elements; all projects used a common library of rules-based transformations; all projects used a unified approach to assign data quality checks to data elements; and all projects used a similar approach to clinical adjudication. An average of 5.8 individuals, including clinicians, data scientists, and trainees, were involved in implementing ML-DQA for each project and an average of 23.4 data elements per project were either transformed or removed in response to ML-DQA. This study demonstrates the importance role of ML-DQA in healthcare projects and provides teams a framework to conduct these essential activities.Comment: Presented at 2022 Machine Learning in Health Care Conferenc

Treatment Outcomes of Patients With Tardive Dyskinesia and Chronic Schizophrenia

We compared the response to antipsychotic treatment between patients with and without tardive dyskinesia (TD) and examined the course of TD

Stopping Clots While Saving Time: Creating an EPIC Index for a Vital Daily Task

Introduction Appropriate thromboprophylaxis is a pressing issue across the united states and the rate of VTE at Thomas Jefferson University Hospital is higher than hospitals of similar complexity. A new tool was created for our EPIC EMR, the VTE Merli Index, that provided at a glance and detailed feedback regarding VTE prophylaxis status Prior to implementation of the index, we studied its ease of use. Our goal was to show the tool would decrease the amount of time and number of clicks required to interrogate the EMR for relevant VTE information by at least 50%.https://jdc.jefferson.edu/patientsafetyposters/1136/thumbnail.jp