Back

Comparative Analysis of Three Surveys on Primary Care Providers' Experiences with Interoperability and Electronic Health Records

Hendrix, N.; Maisel, N.; Everson, J.; Patel, V.; Bazemore, A.; Rotenstein, L.; Holmgren, A. J.; Krist, A. H.; Adler-Milstein, J.; Phillips, R. L.

2024-01-03 primary care research
10.1101/2024.01.02.24300713 medRxiv
Show abstract

ObjectiveThis study compared primary care physicians self-reported experiences with Electronic Health Records (EHR) interoperability, as reported across three surveys: the 2022 Continuous Certification Questionnaire (CCQ) from the American Board of Family Medicine, the 2022 University of California San Franciscos (UCSF) Physician Health IT Survey, and the 2021 National Electronic Health Records Survey (NEHRS). Materials and MethodsWe used descriptive analyses to identify differences between survey pairs. To account for weighting in NEHRS and UCSF, we assessed the significance of differences using the Rao-Scott corrected chi-square test. ResultsCCQ received 3,991 responses, UCSF received 1,375 from primary care physicians, and NEHRS received 858 responses from primary care physicians. Response rates were 100%, 3.6%, and 18.2%, respectively. Substantial and largely statistically significant differences in response were detected across the three surveys. For instance, 22.2% of CCQ respondents said it was very easy to document care in their EHR, compared to 15.2% in NEHRS, and 14.8% in the UCSF survey. Approximately one-third of respondents across surveys said documenting care in their EHR was somewhat or very difficult. The surveys captured different respondent types with CCQ respondents trending younger, and NEHRS respondents more likely to be in private practice. DiscussionAll surveys pointed to room for improvement in EHR usability and interoperability. The differences observed, likely driven by differences in survey methodology and response bias, were likely substantial enough to impact policy decisions. ConclusionDiversified data sources, such as those from specialty boards, may aid in capturing physicians experiences with EHRs and interoperability.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Journal of General Internal Medicine
20 papers in training set
Top 0.1%
22.6%
2
Journal of Medical Internet Research
85 papers in training set
Top 0.4%
10.1%
3
BMJ Health & Care Informatics
13 papers in training set
Top 0.1%
8.4%
4
Journal of the American Medical Informatics Association
61 papers in training set
Top 0.4%
6.8%
5
BMJ Open
554 papers in training set
Top 3%
6.4%
50% of probability mass above
6
JMIR Public Health and Surveillance
45 papers in training set
Top 0.3%
4.9%
7
PLOS ONE
4510 papers in training set
Top 31%
4.9%
8
British Journal of General Practice
22 papers in training set
Top 0.2%
2.9%
9
BJGP Open
12 papers in training set
Top 0.2%
2.7%
10
BMC Medical Informatics and Decision Making
39 papers in training set
Top 1%
2.6%
11
JAMA
17 papers in training set
Top 0.1%
2.1%
12
PLOS Digital Health
91 papers in training set
Top 1%
1.9%
13
BMJ Open Quality
15 papers in training set
Top 0.4%
1.9%
14
JMIRx Med
31 papers in training set
Top 0.6%
1.8%
15
JMIR Research Protocols
18 papers in training set
Top 0.6%
1.7%
16
Frontiers in Public Health
140 papers in training set
Top 5%
1.5%
17
F1000Research
79 papers in training set
Top 2%
1.3%
18
BMC Health Services Research
42 papers in training set
Top 1%
1.3%
19
International Journal of Environmental Research and Public Health
124 papers in training set
Top 5%
1.1%
20
eLife
5422 papers in training set
Top 55%
0.8%
21
Preventive Medicine Reports
14 papers in training set
Top 0.4%
0.8%
22
The Lancet Digital Health
25 papers in training set
Top 1%
0.7%
23
Health Expectations
12 papers in training set
Top 0.8%
0.6%