scholarly journals The Relationship Between Electronic Health Record System and Performance on Quality Measures in the American College of Rheumatology’s Rheumatology Informatics System for Effectiveness (RISE) Registry: Observational Study

10.2196/31186 ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. e31186
Author(s):  
Nevin Hammam ◽  
Zara Izadi ◽  
Jing Li ◽  
Michael Evans ◽  
Julia Kay ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Quality Measures ◽  
Independent Effect ◽  
Health Record ◽  
Practice Level ◽  
Patient Reported ◽  
Electronic Health ◽  
And Performance ◽  
The Relationship ◽  
Better Than

Background Routine collection of disease activity (DA) and patient-reported outcomes (PROs) in rheumatoid arthritis (RA) are nationally endorsed quality measures and critical components of a treat-to-target approach. However, little is known about the role electronic health record (EHR) systems play in facilitating performance on these measures. Objective Using the American College Rheumatology’s (ACR’s) RISE registry, we analyzed the relationship between EHR system and performance on DA and functional status (FS) quality measures. Methods We analyzed data collected in 2018 from practices enrolled in RISE. We assessed practice-level performance on quality measures that require DA and FS documentation. Multivariable linear regression and zero-inflated negative binomial models were used to examine the independent effect of EHR system on practice-level quality measure performance, adjusting for practice characteristics and patient case-mix. Results In total, 220 included practices cared for 314,793 patients with RA. NextGen was the most commonly used EHR system (34.1%). We found wide variation in performance on DA and FS quality measures by EHR system (median 30.1, IQR 0-74.8, and median 9.0, IQR 0-74.2), respectively). Even after adjustment, NextGen practices performed significantly better than Allscripts on the DA measure (51.4% vs 5.0%; P<.05) and significantly better than eClinicalWorks and eMDs on the FS measure (49.3% vs 29.0% and 10.9%; P<.05). Conclusions Performance on national RA quality measures was associated with the EHR system, even after adjusting for practice and patient characteristics. These findings suggest that future efforts to improve quality of care in RA should focus not only on provider performance reporting but also on developing and implementing rheumatology-specific standards across EHRs.

scholarly journals The Relationship Between Electronic Health Record System and Performance on Quality Measures in the American College of Rheumatology’s Rheumatology Informatics System for Effectiveness (RISE) Registry: Observational Study (Preprint)

2021 ◽  
Author(s):  
Nevin Hammam ◽  
Zara Izadi ◽  
Jing Li ◽  
Michael Evans ◽  
Julia Kay ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Quality Measures ◽  
Independent Effect ◽  
Health Record ◽  
Practice Level ◽  
Patient Reported ◽  
Electronic Health ◽  
And Performance ◽  
The Relationship ◽  
Better Than

BACKGROUND Routine collection of disease activity (DA) and patient-reported outcomes (PROs) in rheumatoid arthritis (RA) are nationally endorsed quality measures and critical components of a treat-to-target approach. However, little is known about the role electronic health record (EHR) systems play in facilitating performance on these measures. OBJECTIVE Using the American College Rheumatology’s (ACR’s) RISE registry, we analyzed the relationship between EHR system and performance on DA and functional status (FS) quality measures. METHODS We analyzed data collected in 2018 from practices enrolled in RISE. We assessed practice-level performance on quality measures that require DA and FS documentation. Multivariable linear regression and zero-inflated negative binomial models were used to examine the independent effect of EHR system on practice-level quality measure performance, adjusting for practice characteristics and patient case-mix. RESULTS In total, 220 included practices cared for 314,793 patients with RA. NextGen was the most commonly used EHR system (34.1%). We found wide variation in performance on DA and FS quality measures by EHR system (median 30.1, IQR 0-74.8, and median 9.0, IQR 0-74.2), respectively). Even after adjustment, NextGen practices performed significantly better than Allscripts on the DA measure (51.4% vs 5.0%; <i>P</i>&lt;.05) and significantly better than eClinicalWorks and eMDs on the FS measure (49.3% vs 29.0% and 10.9%; <i>P</i>&lt;.05). CONCLUSIONS Performance on national RA quality measures was associated with the EHR system, even after adjusting for practice and patient characteristics. These findings suggest that future efforts to improve quality of care in RA should focus not only on provider performance reporting but also on developing and implementing rheumatology-specific standards across EHRs. CLINICALTRIAL

What does social isolation have to do with my arthritis? Patient and provider views of the implementation of an electronic Social Determinants of Health risk assessment tool

2020 ◽  
Vol 64 (1) ◽  
pp. 707-710
Keyword(s):  
Electronic Health Record ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Assessment Tool ◽  
Determinants Of Health ◽  
Health Record ◽  
Institute Of Medicine ◽  
Patient Reported ◽  
Item Responses ◽  
Electronic Health

Social Determinants of Health (SDoH) are the conditions in which people are born, live, learn, work, and play that can affect health, functioning, and quality-of-life outcomes. The Institute of Medicine charged healthcare institutions with capturing and measuring patient SDoH risk factors through the electronic health record. Following the implementation of a social determinants of health electronic module across a major health institution, the response to institutional implementation was evaluated. To assess the response, a multidisciplinary team interviewed patients and providers, mapped the workflow, and performed simulated tests to trace the flow of SDoH data from survey item responses to visualization in EHR output for clinicians. Major results of this investigation were: 1) the lack of patient consensus about value of collecting SDOH data, and 2) the disjointed view of patient reported SDoH risks across patients, providers, and the electronic health record due to the way data was collected and visualized.

scholarly journals 1087 Transitioning from Paper to Electronic Health Record Collection of Epworth Sleepiness Scale (ESS) for Quality Measures

SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A404-A404 ◽  
Author(s):  
Y Chang ◽  
B Staley ◽  
S Simonsen ◽  
M Breen ◽  
B Keenan ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Epworth Sleepiness Scale ◽  
Quality Measures ◽  
Health Record ◽  
Electronic Health ◽  
Paper To Electronic

scholarly journals 1082 Using an Electronic Health Record (EHR) to Collect and Use Quality-Of-Life Data for AASM Process and Outcomes Quality Measures

SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A402-A402 ◽  
Author(s):  
B Staley ◽  
B T Keenan ◽  
S Simonsen ◽  
R Warrell ◽  
R Schwab ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Electronic Health Record ◽  
Quality Measures ◽  
Health Record ◽  
Life Data ◽  
Electronic Health

scholarly journals The Relationship between Electronic Health Record Use and Quality of Care over Time

2009 ◽  
Vol 16 (4) ◽  
pp. 457-464 ◽  
Author(s):  
L. Zhou ◽  
C. S. Soran ◽  
C. A. Jenter ◽  
L. A. Volk ◽  
E. J. Orav ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health ◽  
The Relationship ◽  
Over Time

scholarly journals Frequency and Types of Patient-Reported Errors in Electronic Health Record Ambulatory Care Notes

2020 ◽  
Vol 3 (6) ◽  
pp. e205867 ◽  
Author(s):  
Sigall K. Bell ◽  
Tom Delbanco ◽  
Joann G. Elmore ◽  
Patricia S. Fitzgerald ◽  
Alan Fossa ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Ambulatory Care ◽  
Health Record ◽  
Patient Reported ◽  
Electronic Health

scholarly journals The Relationship Between Magnet Designation, Electronic Health Record Adoption, and Medicare Meaningful Use Payments

2017 ◽  
Vol 35 (8) ◽  
pp. 385-391
Author(s):  
Christine Lippincott ◽  
Cynthia Foronda ◽  
Martin Zdanowicz ◽  
Brian E. McCabe ◽  
Todd Ambrosia
Keyword(s):  
Electronic Health Record ◽  
Meaningful Use ◽  
Health Record ◽  
Electronic Health ◽  
The Relationship

Integrating patient-reported outcomes data into the electronic health record.

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 186-186
Author(s):  
Brandon Bosch ◽  
Scott Hartman ◽  
Lauren Caldarello ◽  
Diane Denny, DBA
Keyword(s):  
Electronic Health Record ◽  
Population Analysis ◽  
Treatment Plan ◽  
Symptom Burden ◽  
Symptom Assessment ◽  
Patient Reported Outcome ◽  
Health Record ◽  
National Network ◽  
Patient Reported ◽  
Electronic Health

186 Background: As a national network of hospitals that specialize in the treatment of patients fighting complex or advanced-stage cancer, the network was an early adopter of using patient reported outcome (PRO) data as part of its routine patient assessment and treatment. Since 2012 an externally validated tool has been used to capture patients’ perceived symptom burden for real-time clinical intervention, from the point of first visit throughout the course of treatment, at intervals of 21 days or greater. Research has demonstrated the use of PRO data as a valuable component of a patient’s treatment plan, promoting improved quality and length of life. Methods: The use of this data across the network was expanded such that results once only accessible on paper and via electronically stored images, has now been fully integrated into the electronic health record (EHR). A multidisciplinary project team formulated the specifications for a successful integration of PRO data into the EHR. Results: The project achieved its goal and went beyond data integration to include implementation of a solution to facilitate documentation of intervention against patients’ symptoms. Provider workflow efficiency is greatly enhanced via single system access and visual notification, with critical values flagged, to focus providers’ attention on severe symptoms. Incorporation of a unified EHR flowsheet provides a paperless, one-stop symptom assessment approach and streamlined mechanism for intervention documentation. The documentation module leverages structured data fields and linkage of PRO data with interventions, such as specialist referrals or medication orders, to support enhanced patient care and quality improvement. Conclusions: The ability to easily view an array of patient reported concerns and document interventions against severe or significantly worsening symptoms provides clinicians an enhanced ability to address quality of life related needs. PRO data is now stored electronically in the enterprise warehouse, thus enabling aggregation with data from which to perform population analysis and eventually, pursue opportunities for predictive modeling.

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