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.

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 How well is the electronic health record supporting the clinical tasks of hospital physicians? A survey of physicians at three Norwegian hospitals

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Thomas Roger Schopf ◽  
Bente Nedrebø ◽  
Karl Ove Hufthammer ◽  
Inderjit Kaur Daphu ◽  
Hallvard Lærum
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Electronic Health Record System ◽  
System Stability ◽  
Medical Decision ◽  
Health Record ◽  
Clinical Workflow ◽  
Online Questionnaire ◽  
Record System ◽  
Electronic Health

Abstract Background The electronic health record is expected to improve the quality and efficiency of health care. Many novel functionalities have been introduced in order to improve medical decision making and communication between health care personnel. There is however limited evidence on whether these new functionalities are useful. The aim of our study was to investigate how well the electronic health record system supports physicians in performing basic clinical tasks. Methods Physicians of three prominent Norwegian hospitals participated in the survey. They were asked, in an online questionnaire, how well the hospital’s electronic health record system DIPS supported 49 clinical tasks as well as how satisfied they were with the system in general, including the technical performance. Two hundred and eight of 402 physicians (52%) submitted a completely answered questionnaire. Results Seventy-two percent of the physicians had their work interrupted or delayed because the electronic health record hangs or crashes at least once a week, while 22% had experienced this problem daily. Fifty-three percent of the physicians indicated that the electronic health record is cumbersome to use and adds to their workload. The majority of physicians were satisfied with managing tests, e.g., requesting laboratory tests, reading test results and managing radiological investigations and electrocardiograms. Physicians were less satisfied with managing referrals. There was high satisfaction with some of the decision support functionalities available for prescribing drugs. This includes drug interaction alerts and drug allergy warnings, which are displayed automatically. However, physicians were less satisfied with other aspects of prescribing drugs, including getting an overview of the ongoing drug therapy. Conclusions In the survey physicians asked for improvements of certain electronic health record functionalities like medication, clinical workflow support including planning and better overviews. In addition, there is apparently a need to focus on system stability, number of logins, reliability and better instructions on available electronic health record features. Considerable development is needed in current electronic health record systems to improve usefulness and satisfaction.

scholarly journals Metrics for assessing physician activity using electronic health record log data

2020 ◽  
Vol 27 (4) ◽  
pp. 639-643 ◽  
Author(s):  
Christine A Sinsky ◽  
Adam Rule ◽  
Genna Cohen ◽  
Brian G Arndt ◽  
Tait D Shanafelt ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Clinical Environment ◽  
Log Data ◽  
Multiple Dimensions ◽  
Time Work ◽  
Physician Time ◽  
Electronic Health ◽  
Practice Efficiency ◽  
Core Measures

Abstract Electronic health record (EHR) log data have shown promise in measuring physician time spent on clinical activities, contributing to deeper understanding and further optimization of the clinical environment. In this article, we propose 7 core measures of EHR use that reflect multiple dimensions of practice efficiency: total EHR time, work outside of work, time on documentation, time on prescriptions, inbox time, teamwork for orders, and an aspirational measure for the amount of undivided attention patients receive from their physicians during an encounter, undivided attention. We also illustrate sample use cases for these measures for multiple stakeholders. Finally, standardization of EHR log data measure specifications, as outlined here, will foster cross-study synthesis and comparative research.

scholarly journals Period Prevalence of Rheumatic Heart Disease and the Need for a Centralized Patient Registry in American Samoa, 2016 to 2018

2021 ◽  
Author(s):  
Rebecca C. Woodruff ◽  
Ipuniuesea Eliapo‐Unutoa ◽  
Howard Chiou ◽  
Maria Gayapa ◽  
Sara Noonan ◽  
...  
Keyword(s):  
Heart Disease ◽  
Electronic Health Record ◽  
Rheumatic Heart Disease ◽  
American Samoa ◽  
Clinical Encounter ◽  
Health Record ◽  
Period Prevalence ◽  
Icd 10 ◽  
Electronic Health ◽  
Rheumatic Heart

Background Rheumatic heart disease (RHD) is a severe, chronic complication of acute rheumatic fever, triggered by group A streptococcal pharyngitis. Centralized patient registries are recommended for RHD prevention and control, but none exists in American Samoa. Using existing RHD tracking systems, we estimated RHD period prevalence and the proportion of people with RHD documented in the electronic health record. Methods and Results RHD cases were identified from a centralized electronic health record system, which retrieved clinical encounters with RHD International Classification of Diseases, Tenth Revision, Clinical Modification ( ICD‐10‐CM ) codes, clinical problem lists referencing RHD, and antibiotic prophylaxis administration records; 3 RHD patient tracking spreadsheets; and an all‐cause mortality database. RHD cases had ≥1 clinical encounter with RHD ICD‐10‐CM codes, a diagnostic echocardiogram, or RHD as a cause of death, or were included in RHD patient tracking spreadsheets. Period prevalence per 1000 population among children aged <18 years and adults aged ≥18 years from 2016 to 2018 and the proportion of people with RHD with ≥1 clinical encounter with an RHD ICD‐10‐CM code were estimated. From 2016 to 2018, RHD was documented in 327 people (57.2%: children aged <18 years). Overall RHD period prevalence was 6.3 cases per 1000 and varied by age (10.0 pediatric cases and 4.3 adult cases per 1000). Only 67% of people with RHD had ≥1 clinical encounter with an RHD ICD‐10‐CM code. Conclusions RHD remains a serious public health problem in American Samoa, and the existing electronic health record does not include all cases. A centralized patient registry could improve tracking people with RHD to ensure they receive necessary care.

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