The Impact of the COVID-19 Pandemic and Telemedicine Implementation on Practice Patterns and Electronic Health Record Utilization in an Academic Rheumatology Practice

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Mithu Maheswaranathan ◽  
Philip Chu ◽  
Andrew Johannemann ◽  
Lisa Criscione-Schreiber ◽  
Megan Clowse ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Practice Patterns ◽  
Health Record ◽  
Electronic Health ◽  
The Impact ◽  
Rheumatology Practice

scholarly journals Management of a parenteral opioid shortage using ASHP guidelines

2021 ◽  
Vol 78 (5) ◽  
pp. 426-435
Author(s):  
Peter Vo ◽  
Daniel A Sylvia ◽  
Loay Milibari ◽  
John Ryan Stackhouse ◽  
Paul Szumita ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Drug Product ◽  
Health Record ◽  
Large Hospital ◽  
Hospital Administrators ◽  
Utilization Data ◽  
Electronic Health ◽  
Opioid Medications ◽  
American Society ◽  
The Impact

Abstract Purpose Management of an acute shortage of parenteral opioid products at a large hospital through prescribing interventions and other guideline-recommended actions is described. Summary In early 2018, many hospitals were faced with a shortage of parenteral opioids that was predicted to last an entire year. The American Society of Health-System Pharmacists (ASHP) has published guidelines on managing drug product shortages. This article describes the application of these guidelines to manage the parenteral opioid shortage and the impact on opioid dispensing that occurred in 2018. Our approach paralleled that recommended in the ASHP guidelines. Daily dispensing reports generated from automated dispensing cabinets and from the electronic health record were used to capture dispenses of opioid medications. Opioid prescribing and utilization data were converted to morphine milligram equivalents (MME) to allow clinical leaders and hospital administrators to quickly evaluate opioid inventories and consumption. Action steps included utilization of substitute opioid therapies and conversion of opioid patient-controlled analgesia (PCA) and opioid infusions to intravenous bolus dose administration. Parenteral opioid supplies were successfully rationed so that surgical and elective procedures were not canceled or delayed. During the shortage, opioid dispensing decreased in the inpatient care areas from approximately 2.0 million MME to 1.4 million MME and in the operating rooms from 0.56 MME to 0.29 million MME. The combination of electronic health record alerts, increased utilization of intravenous acetaminophen and liposomal bupivacaine, and pharmacist interventions resulted in a 67% decline in PCA use and a 65% decline in opioid infusions. Conclusion A multidisciplinary response is necessary for effective management of drug shortages through implementation of strategies and practices for notifying clinicians of shortages and identifying optimal alternative therapies.

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.

AIRA, an Artificial Intelligence Realistic Assistant that will save your time, a before-and-after study during Pandemic (Preprint)

2021 ◽  
Author(s):  
Kaio Bin ◽  
Adler Araújo Ribeiro Melo ◽  
José Guilherme Franco Da Rocha ◽  
Renata Pivi De Almeida ◽  
Vilson Cobello Junior ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Record ◽  
Medical Care ◽  
Health Screening ◽  
Urgent Care ◽  
Health Record ◽  
Before And After ◽  
Electronic Health ◽  
The Impact ◽  
Before And After Study

BACKGROUND AIRA is an AI designed to reduce the time that doctors dedicate filling out EHR, winner of the first edition of MIT Hacking Medicine held in Brazil in 2020. As a proof of concept, AIRA was implemented in administrative process before its application in a medical process. OBJECTIVE The aim of the study is to determinate the impact of AIRA by eliminating the Medical Care Registration (MCR) on Electronic Health Record (EHR) by Administrative Officer. METHODS This is a comparative before-and-after study following the guidance “Evaluating digital health products” from Public Health England. An Artificial Intelligence named AIRA was created and implemented at CEAC (Employee Attention Center) from HCFMUSP. A total of 25,507 attendances were evaluated along 2020 for determinate AIRA´s impact. Total of MCR, time of health screening and time between the end of the screening and the beginning of medical care, were compared in the pre and post AIRA periods. RESULTS AIRA eliminated the need for Medical Care Registration by Administrative Officer in 92% (p<0.0001). The nurse´s time of health screening increased 16% (p<0.0001) during the implementation, and 13% (p<0.0001) until three months after the implementation, but reduced in 4% three months after implementation (p<0.0001). The mean and median total time to Medical Care after the nurse’ Screening was decreased in 30% (p<0.0001) and 41% (p<0.0001) respectively. CONCLUSIONS The implementation of AIRA reduced the time to medical care in an urgent care after the nurse´ screening, by eliminating non-value-added activity the Medical Care Registration on Electronic Health Record (EHR) by Administrative Officer.

scholarly journals Opinions, perceptions and attitudes toward an electronic health record system among practicing nurses

2017 ◽  
Vol 8 (3) ◽  
pp. 12
Author(s):  
Ahmad H. Abu Raddaha ◽  
Arwa Obeidat ◽  
Huda Al Awaisi ◽  
Jahara Hayudini
Keyword(s):  
Electronic Health Record ◽  
Clinical Information System ◽  
Research Work ◽  
Healthcare Delivery ◽  
Future Research ◽  
Health Record ◽  
Randomized Sampling ◽  
Positive Attitudes ◽  
Electronic Health ◽  
The Impact

Background: Despite worldwide expanding implementation of electronic health record (EHR) systems, healthcare professionals conducted limited number of studies to explore factors that might facilitate or jeopardize using these systems. This study underscores the impact of nurses’ opinions, perceptions, and computer competencies on their attitudes toward using an EHR system.Methods: With randomized sampling, a cross-sectional exploratory design was used. The sample consisted of 169 nurses who worked at a public teaching hospital in Oman. They completed self-administered questionnaire. Several standardized valid and reliable instruments were utilized.Results: Seventy-four percent of our study nurses had high positive attitudes toward the EHR system. The least ranked perception scores (60.4%) were linked to perceiving that suggestions made by nurses about the system would be taken into account. Nurses who reported that the hospital sought for suggestions for customization of the system [OR: 2.54 (95% CI: 1.09, 5.88), p = .03], who found the system as an easy-to-use clinical information system [OR: 6.53 (95% CI: 1.72, 24.75), p = .01], who reported the presence of good relationship with the system’s managing personnel [OR: 3.59 (95% CI: 1.13, 11.36), p = .03] and who reported that the system provided all needed health information [OR: 2.97 (95% CI: 1.16, 7.62), p = .02] were more likely to develop high positive attitudes toward the system.Conclusions: To better develop plans to foster the EHR system’s use facilitators and overcome its usage barriers by nursing professionals, more involvement of nurses in system’s customization endeavors is highly suggested. When the system did not disrupt workflows, it would decrease clinical errors and expand nursing productivity. In order to maximize the utilization of the system in healthcare delivery, future research work to investigate the effect of the system on other healthcare providers and inter-professional communications is pressingly needed.

scholarly journals Beyond Getting Rid of Stupid Stuff in the Electronic Health Record (Beyond-GROSS): Protocol for a User-Centered, Mixed-Method Intervention to Improve the Electronic Health Record System

10.2196/25148 ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. e25148
Author(s):  
Ahmed Umar Otokiti ◽  
Catherine K Craven ◽  
Avniel Shetreat-Klein ◽  
Stacey Cohen ◽  
Bruce Darrow
Keyword(s):  
Health Care ◽  
Data Analysis ◽  
Electronic Health Record ◽  
Health Record ◽  
User Centered Design ◽  
Pilot Phase ◽  
Direct Input ◽  
Electronic Health ◽  
The Impact ◽  
User Centered

Background Up to 60% of health care providers experience one or more symptoms of burnout. Perceived clinician burden resulting in burnout arises from factors such as electronic health record (EHR) usability or lack thereof, perceived loss of autonomy, and documentation burden leading to less clinical time with patients. Burnout can have detrimental effects on health care quality and contributes to increased medical errors, decreased patient satisfaction, substance use, workforce attrition, and suicide. Objective This project aims to improve the user-centered design of the EHR by obtaining direct input from clinicians about deficiencies. Fixing identified deficiencies via user-centered design has the potential to improve usability, thereby increasing satisfaction by reducing EHR-induced burnout. Methods Quantitative and qualitative data will be obtained from clinician EHR users. The input will be received through a form built in a REDCap database via a link embedded in the home page of the EHR. The REDCap data will be analyzed in 2 main dimensions, based on nature of the input, what section of the EHR is affected, and what is required to fix the issue(s). Identified issues will be escalated to relevant stakeholders responsible for rectifying the problems identified. Data analysis, project evaluation, and lessons learned from the evaluation will be incorporated in a Plan-Do-Study-Act (PDSA) manner every 4-6 weeks. Results The pilot phase of the study began in October 2020 in the Gastroenterology Division at Mount Sinai Hospital, New York City, NY, which includes 39 physicians and 15 nurses. The pilot is expected to run over a 4-6–month period. The results of the REDCap data analysis will be reported within 1 month of completing the pilot phase. We will analyze the nature of requests received and the impact of rectified issues on the clinician EHR user. We expect that the results will reveal which sections of the EHR have the highest deficiencies while also highlighting issues about workflow difficulties. Perceived impact of the project on provider engagement, patient safety, and workflow efficiency will also be captured by evaluation survey and other qualitative methods where possible. Conclusions The project aims to improve user-centered design of the EHR by soliciting direct input from clinician EHR users. The ultimate goal is to improve efficiency, reduce EHR inefficiencies with the possibility of improving staff engagement, and lessen EHR-induced clinician burnout. Our project implementation includes using informatics expertise to achieve the desired state of a learning health system as recommended by the National Academy of Medicine as we facilitate feedback loops and rapid cycles of improvement. International Registered Report Identifier (IRRID) PRR1-10.2196/25148

scholarly journals Changes in hospital bond ratings after the transition to a new electronic health record

2018 ◽  
Vol 25 (5) ◽  
pp. 572-574 ◽  
Author(s):  
Dustin McEvoy ◽  
Michael L Barnett ◽  
Dean F Sittig ◽  
Skye Aaron ◽  
Ateev Mehrotra ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Apparent Difference ◽  
Net Income ◽  
Bond Ratings ◽  
Bond Rating ◽  
Electronic Health ◽  
Rating Changes ◽  
And Control ◽  
The Impact

Abstract Objective To assess the impact of electronic health record (EHR) implementation on hospital finances. Materials and Methods We analyzed the impact of EHR implementation on bond ratings and net income from service to patients (NISP) at 32 hospitals that recently implemented a new EHR and a set of controls. Results After implementing an EHR, 7 hospitals had a bond downgrade, 7 had a bond upgrade, and 18 had no changes. There was no difference in the likelihood of bond rating changes or in changes to NISP following EHR go-live when compared to control hospitals. Discussion Most hospitals in our analysis saw no change in bond ratings following EHR go-live, with no significant differences observed between EHR implementation and control hospitals. There was also no apparent difference in NISP. Conclusions Implementation of an EHR did not appear to have an impact on bond ratings at the hospitals in our analysis.

scholarly journals MP03: Strategies to minimize impact of electronic health record implementation on emergency department flow

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S42-S43
Author(s):  
E. Grafstein ◽  
S. Horak ◽  
J. Kung ◽  
J. Bonilla ◽  
R. Stenstrom
Keyword(s):  
Electronic Health Record ◽  
Productivity Loss ◽  
Admission Rate ◽  
Richmond Hospital ◽  
Health Record ◽  
Chi Square ◽  
Control Hospital ◽  
Time Periods ◽  
Electronic Health ◽  
The Impact

Introduction: Electronic health record (EHR) implementation can be associated with a slowdown in performance and delayed return to pre go-live productivity. The objective of this study is to describe the impact of a go-live strategy including diversion, public advertising of the go-live, and extra physician staffing to mitigate productivity loss. Methods: Lions Gate Hospital (LGH), an urban community hospital and rural referral centre with 250 beds and 65,000 annual ED visits went live with Cerner HER (Cerner Corporation, Kansas, MO) on April 28, 2018. The implementation included complete electronic ordering and electronic physician documentation. We compared patients seen per hour, time to physician (TTMD), ED length of stay (EDLOS), patients per hour left without being seen (LWBS), and admission rate (AR) for the 6 weeks prior to implementation (Pre), 2 weeks during (Imp), and 6 weeks after (Post) for LGH and a control hospital (Richmond Hospital – comparable in size/acuity) for the same periods. Medians were compared using the Mann-Whitney test for patients/hour, EDLOS and TTMD, and chi-square for AR and LWBS. Results: Patients/hour seen went from 2.1/hour in the pre phase, but dropped to 1.7/hr in the 2 week period following implementation (P &lt; 0.05). During weeks 2-8 post implementation, 2,3 patients per hour were seen (P = 0.38 compared to Pre phase). At the control hospital, patients per hour were comparable across all time periods (Ps &gt; 0.3). Median time to physician was 54, 56, and 54 minutes at LGH for the Pre, Imp, and Post time periods (Ps &gt; 0.3). Median EDLOS was 184, 196, and 184 minutes in the pre, Imp, and post phases (P Imp versus pre = 0.11; Pre versus post = 0.54). LWBS rate was 1.3%, 2.9, and 2.4% (Ps for Imp and Post versus pre &lt;0.05) at LGH, but the pattern was similar for the control hospital (2.9%, 4.1% and 4.0%’ Ps &lt;0.05). There was no significant change in ambulance arrivals or admission rate at either hospital (Ps &gt; 0.2). Conclusion: A deliberate implementation strategy that focuses on ED physician upstaffing and visit diversion can smooth the impact of the implementation of an EHR so that patient care is not impacted significantly. Return to normal productivity occurred by 8 weeks post go-live. We demonstrate a strategy that may support easier implementation at other sites.

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