Impact of room pooling and electronic health record on patient (pt) wait time, clinic work flow, and pts’/providers’ satisfaction.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18191-e18191
Author(s):  
Saroj Vadhan-Raj ◽  
Xiao Zhou ◽  
Meyyammai Narayanan ◽  
Shawn J Janarthanan ◽  
Mary Daniel ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Wait Time ◽  
Work Flow ◽  
Wait Times ◽  
Health Record ◽  
Allocation Model ◽  
Exam Room ◽  
Negative Effect ◽  
Electronic Health ◽  
The Impact

e18191 Background: Excessive pt wait time can have negative effect on clinic work flow and on pts/ providers satisfaction. Increasing pt volume and limited clinic capacity can lead to long wait times for pts. The purpose of this two-part study was to evaluate the impact of Room Pooling Model (RPM) instead of Room Allocation Model (Part 1) and Electronic Health Record (EHR) on pt wait times in clinic and pts’/providers’ satisfaction (Part 2). Methods: The time studies and pts’/providers’ wait time satisfaction surveys were carried out over 2 weeks before (baseline) and 8 weeks after the implementation of RPM (Part 1), prior to the new EHR system, and 6 months after the implementation of EHR (part 2). All times of when pts, mid-level providers (MLP), and doctors (MDs) entered and exited the exam rooms were collected for 887 pts seen during the clinic. Data was analyzed using JMP and SAS. Results: As described earlier (ASCO 2016, abst 6595), the RPM was associated with increase in the proportion of pts seen by MDs within 30 min from the time roomed in the exam room and improvement in pts’/provider’s satisfaction. Post EHR, there were delays with decrease in the proportion of pts seen within 30 min from the time roomed in. Although the pt satisfaction did not change significantly, the number of times MDs had to wait for an open exam room increased from 8% (5/65) to 24% (14/59, p=0.01). The impact of RPM and EHR on pt times are shown below. The delays to see MDs after EHR were associated with longer time spent with the nurse (from median 4 to 7 min) and delays in seeing MLPs (from 11 to 18 min). Conclusions: These findings indicate that RPM can improve pt wait times. During initial stages of EHR implementation, the increase in pt wait time and reduced clinical efficiencies can be related to learning, and adapting to the new system. These data can be useful to design interventions that can target the areas of delays such as training and redesigning workflow to improve the clinical efficiency. [Table: see text]

scholarly journals The electronic health record audit file: the patient is waiting

2016 ◽  
Vol 24 (e1) ◽  
pp. e28-e34 ◽  
Author(s):  
Annemarie G Hirsch ◽  
J B Jones ◽  
Virginia R Lerch ◽  
Xiaoqin Tang ◽  
Andrea Berger ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Wait Time ◽  
Clinical Encounter ◽  
Health Record ◽  
Clinical Workflow ◽  
Waiting Room ◽  
Exam Room ◽  
Physician Time ◽  
Nurse Time ◽  
Electronic Health

Objective: We describe how electronic health record (EHR) audit files can be used to understand how time is spent in primary care (PC). Materials/methods: We used audit file data from the Geisinger Clinic to quantify elements of the clinical workflow and to determine how these times vary by patient and encounter factors. We randomly selected audit file records representing 36 437 PC encounters across 26 clinic locations. Audit file data were used to estimate duration and variance of: (1) time in the waiting room, (2) nurse time with the patient, (3) time in the exam room without a nurse or physician, and (4) physician time with the patient. Multivariate modeling was used to test for differences by patient and by encounter features. Results: On average, a PC encounter took 54.6 minutes, with 5 minutes of nurse time, 15.5 minutes of physician time, and the remaining 62% of the time spent waiting to see a clinician or check out. Older age, female sex, and chronic disease were associated with longer wait times and longer time with clinicians. Level of service and numbers of medications, procedures, and lab orders were associated with longer time with clinicians. Late check-in and same-day visits were associated with shorter wait time and clinician time. Conclusions: This study provides insights on uses of audit file data for workflow analysis during PC encounters. Discussion: Scalable ways to quantify clinical encounter workflow elements may provide the means to develop more efficient approaches to care and improve the patient experience.

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

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
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