Evaluating the Impact of a Point-of-Care Cardiometabolic Clinical Decision Support Tool on Clinical Efficiency Using Electronic Health Record Audit Log Data: Algorithm Development and Validation
BackgroundElectronic health record (EHR) systems are becoming increasingly complicated, leading to concerns about rising physician burnout, particularly for primary care physicians (PCPs). Managing the most common cardiometabolic chronic conditions by PCPs during a limited cl...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2022-09-01
|
| Series: | JMIR Medical Informatics |
| Online Access: | https://medinform.jmir.org/2022/9/e38385 |
| _version_ | 1827857981415358464 |
|---|---|
| author | Xiaowei Yan Hannah Husby Satish Mudiganti Madina Gbotoe Jake Delatorre-Reimer Kevin Knobel Andrew Hudnut J B Jones |
| author_facet | Xiaowei Yan Hannah Husby Satish Mudiganti Madina Gbotoe Jake Delatorre-Reimer Kevin Knobel Andrew Hudnut J B Jones |
| author_sort | Xiaowei Yan |
| collection | DOAJ |
| description |
BackgroundElectronic health record (EHR) systems are becoming increasingly complicated, leading to concerns about rising physician burnout, particularly for primary care physicians (PCPs). Managing the most common cardiometabolic chronic conditions by PCPs during a limited clinical time with a patient is challenging.
ObjectiveThis study aimed to evaluate a Cardiometabolic Sutter Health Advanced Reengineered Encounter (CM-SHARE), a web-based application to visualize key EHR data, on the EHR use efficiency.
MethodsWe developed algorithms to identify key clinic workflow measures (eg, total encounter time, total physician time in the examination room, and physician EHR time in the examination room) using audit data, and we validated and calibrated the measures with time-motion data. We used a pre-post parallel design to identify propensity score–matched CM-SHARE users (cases), nonusers (controls), and nested-matched patients. Cardiometabolic encounters from matched case and control patients were used for the workflow evaluation. Outcome measures were compared between the cases and controls. We applied this approach separately to both the CM-SHARE pilot and spread phases.
ResultsTime-motion observation was conducted on 101 primary care encounters for 9 PCPs in 3 clinics. There was little difference (<0.8 minutes) between the audit data–derived workflow measures and the time-motion observation. Two key unobservable times from audit data, physician entry into and exiting the examination room, were imputed based on time-motion studies. CM-SHARE was launched with 6 pilot PCPs in April 2016. During the prestudy period (April 1, 2015, to April 1, 2016), 870 control patients with 2845 encounters were matched with 870 case patients and encounters, and 727 case patients with 852 encounters were matched with 727 control patients and 3754 encounters in the poststudy period (June 1, 2016, to June 30, 2017). Total encounter time was slightly shorter (mean −2.7, SD 1.4 minutes, 95% CI −4.7 to −0.9; mean –1.6, SD 1.1 minutes, 95% CI −3.2 to −0.1) for cases than controls for both periods. CM-SHARE saves physicians approximately 2 minutes EHR time in the examination room (mean −2.0, SD 1.3, 95% CI −3.4 to −0.9) compared with prestudy period and poststudy period controls (mean −1.9, SD 0.9, 95% CI −3.8 to −0.5). In the spread phase, 48 CM-SHARE spread PCPs were matched with 84 control PCPs and 1272 cases with 3412 control patients, having 1119 and 4240 encounters, respectively. A significant reduction in total encounter time for the CM-SHARE group was observed for short appointments (≤20 minutes; 5.3-minute reduction on average) only. Total physician EHR time was significantly reduced for both longer and shorter appointments (17%-33% reductions).
ConclusionsCombining EHR audit log files and clinical information, our approach offers an innovative and scalable method and new measures that can be used to evaluate clinical EHR efficiency of digital tools used in clinical settings. |
| first_indexed | 2024-03-12T12:48:11Z |
| format | Article |
| id | doaj.art-2f6f7ffcec4441e29b70f9f42a61c9dd |
| institution | Directory Open Access Journal |
| issn | 2291-9694 |
| language | English |
| last_indexed | 2024-03-12T12:48:11Z |
| publishDate | 2022-09-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Medical Informatics |
| spelling | doaj.art-2f6f7ffcec4441e29b70f9f42a61c9dd2023-08-28T23:01:27ZengJMIR PublicationsJMIR Medical Informatics2291-96942022-09-01109e3838510.2196/38385Evaluating the Impact of a Point-of-Care Cardiometabolic Clinical Decision Support Tool on Clinical Efficiency Using Electronic Health Record Audit Log Data: Algorithm Development and ValidationXiaowei Yanhttps://orcid.org/0000-0002-1675-8751Hannah Husbyhttps://orcid.org/0000-0001-8809-5582Satish Mudigantihttps://orcid.org/0000-0003-2776-8952Madina Gbotoehttps://orcid.org/0000-0003-3235-9862Jake Delatorre-Reimerhttps://orcid.org/0000-0002-6074-1236Kevin Knobelhttps://orcid.org/0000-0003-4025-8420Andrew Hudnuthttps://orcid.org/0000-0001-8571-0682J B Joneshttps://orcid.org/0000-0001-8752-877X BackgroundElectronic health record (EHR) systems are becoming increasingly complicated, leading to concerns about rising physician burnout, particularly for primary care physicians (PCPs). Managing the most common cardiometabolic chronic conditions by PCPs during a limited clinical time with a patient is challenging. ObjectiveThis study aimed to evaluate a Cardiometabolic Sutter Health Advanced Reengineered Encounter (CM-SHARE), a web-based application to visualize key EHR data, on the EHR use efficiency. MethodsWe developed algorithms to identify key clinic workflow measures (eg, total encounter time, total physician time in the examination room, and physician EHR time in the examination room) using audit data, and we validated and calibrated the measures with time-motion data. We used a pre-post parallel design to identify propensity score–matched CM-SHARE users (cases), nonusers (controls), and nested-matched patients. Cardiometabolic encounters from matched case and control patients were used for the workflow evaluation. Outcome measures were compared between the cases and controls. We applied this approach separately to both the CM-SHARE pilot and spread phases. ResultsTime-motion observation was conducted on 101 primary care encounters for 9 PCPs in 3 clinics. There was little difference (<0.8 minutes) between the audit data–derived workflow measures and the time-motion observation. Two key unobservable times from audit data, physician entry into and exiting the examination room, were imputed based on time-motion studies. CM-SHARE was launched with 6 pilot PCPs in April 2016. During the prestudy period (April 1, 2015, to April 1, 2016), 870 control patients with 2845 encounters were matched with 870 case patients and encounters, and 727 case patients with 852 encounters were matched with 727 control patients and 3754 encounters in the poststudy period (June 1, 2016, to June 30, 2017). Total encounter time was slightly shorter (mean −2.7, SD 1.4 minutes, 95% CI −4.7 to −0.9; mean –1.6, SD 1.1 minutes, 95% CI −3.2 to −0.1) for cases than controls for both periods. CM-SHARE saves physicians approximately 2 minutes EHR time in the examination room (mean −2.0, SD 1.3, 95% CI −3.4 to −0.9) compared with prestudy period and poststudy period controls (mean −1.9, SD 0.9, 95% CI −3.8 to −0.5). In the spread phase, 48 CM-SHARE spread PCPs were matched with 84 control PCPs and 1272 cases with 3412 control patients, having 1119 and 4240 encounters, respectively. A significant reduction in total encounter time for the CM-SHARE group was observed for short appointments (≤20 minutes; 5.3-minute reduction on average) only. Total physician EHR time was significantly reduced for both longer and shorter appointments (17%-33% reductions). ConclusionsCombining EHR audit log files and clinical information, our approach offers an innovative and scalable method and new measures that can be used to evaluate clinical EHR efficiency of digital tools used in clinical settings.https://medinform.jmir.org/2022/9/e38385 |
| spellingShingle | Xiaowei Yan Hannah Husby Satish Mudiganti Madina Gbotoe Jake Delatorre-Reimer Kevin Knobel Andrew Hudnut J B Jones Evaluating the Impact of a Point-of-Care Cardiometabolic Clinical Decision Support Tool on Clinical Efficiency Using Electronic Health Record Audit Log Data: Algorithm Development and Validation JMIR Medical Informatics |
| title | Evaluating the Impact of a Point-of-Care Cardiometabolic Clinical Decision Support Tool on Clinical Efficiency Using Electronic Health Record Audit Log Data: Algorithm Development and Validation |
| title_full | Evaluating the Impact of a Point-of-Care Cardiometabolic Clinical Decision Support Tool on Clinical Efficiency Using Electronic Health Record Audit Log Data: Algorithm Development and Validation |
| title_fullStr | Evaluating the Impact of a Point-of-Care Cardiometabolic Clinical Decision Support Tool on Clinical Efficiency Using Electronic Health Record Audit Log Data: Algorithm Development and Validation |
| title_full_unstemmed | Evaluating the Impact of a Point-of-Care Cardiometabolic Clinical Decision Support Tool on Clinical Efficiency Using Electronic Health Record Audit Log Data: Algorithm Development and Validation |
| title_short | Evaluating the Impact of a Point-of-Care Cardiometabolic Clinical Decision Support Tool on Clinical Efficiency Using Electronic Health Record Audit Log Data: Algorithm Development and Validation |
| title_sort | evaluating the impact of a point of care cardiometabolic clinical decision support tool on clinical efficiency using electronic health record audit log data algorithm development and validation |
| url | https://medinform.jmir.org/2022/9/e38385 |
| work_keys_str_mv | AT xiaoweiyan evaluatingtheimpactofapointofcarecardiometabolicclinicaldecisionsupporttoolonclinicalefficiencyusingelectronichealthrecordauditlogdataalgorithmdevelopmentandvalidation AT hannahhusby evaluatingtheimpactofapointofcarecardiometabolicclinicaldecisionsupporttoolonclinicalefficiencyusingelectronichealthrecordauditlogdataalgorithmdevelopmentandvalidation AT satishmudiganti evaluatingtheimpactofapointofcarecardiometabolicclinicaldecisionsupporttoolonclinicalefficiencyusingelectronichealthrecordauditlogdataalgorithmdevelopmentandvalidation AT madinagbotoe evaluatingtheimpactofapointofcarecardiometabolicclinicaldecisionsupporttoolonclinicalefficiencyusingelectronichealthrecordauditlogdataalgorithmdevelopmentandvalidation AT jakedelatorrereimer evaluatingtheimpactofapointofcarecardiometabolicclinicaldecisionsupporttoolonclinicalefficiencyusingelectronichealthrecordauditlogdataalgorithmdevelopmentandvalidation AT kevinknobel evaluatingtheimpactofapointofcarecardiometabolicclinicaldecisionsupporttoolonclinicalefficiencyusingelectronichealthrecordauditlogdataalgorithmdevelopmentandvalidation AT andrewhudnut evaluatingtheimpactofapointofcarecardiometabolicclinicaldecisionsupporttoolonclinicalefficiencyusingelectronichealthrecordauditlogdataalgorithmdevelopmentandvalidation AT jbjones evaluatingtheimpactofapointofcarecardiometabolicclinicaldecisionsupporttoolonclinicalefficiencyusingelectronichealthrecordauditlogdataalgorithmdevelopmentandvalidation |