Literature DB >> 31091545

Local Investment in Training Drives Electronic Health Record User Satisfaction.

Christopher A Longhurst1, Taylor Davis2, Amy Maneker3, H C Eschenroeder4, Rachel Dunscombe5, George Reynolds6, Brian Clay1, Thomas Moran7, David B Graham8, Shannon M Dean9, Julia Adler-Milstein10.   

Abstract

Entities:  

Year:  2019        PMID: 31091545      PMCID: PMC6520075          DOI: 10.1055/s-0039-1688753

Source DB:  PubMed          Journal:  Appl Clin Inform        ISSN: 1869-0327            Impact factor:   2.342


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Background and Significance

Despite decades of effort and billions of dollars of investment, the electronic health record (EHR) has not lived up to its potential to improve care, reduce costs, or revolutionize the experience for caregivers. 1 Many people point to poor technical usability as a root cause of these failings. 2 To find solutions to these challenges, the Arch Collaborative organizations (signed below) are working together to jointly study the feedback of their EHR users. After collecting responses from over 72,000 physicians, nurses, advanced practice professionals, and residents across 156 provider organizations, we are identifying key opportunities to derive greater value from the EHR investments that our organizations have collectively made (See details of survey methods in the supplementary Material , available in the online version). The extensive feedback from tens of thousands of users reveals critical gaps in users' understanding of how to optimize their EHR. Therefore, we as an industry have an opportunity to improve EHR adoption by investing in EHR learning and personalization support for caregivers. If health care organizations offered higher-quality educational opportunities for their care providers—and if providers were expected to develop greater mastery of EHR functionality—many of the current EHR challenges would be ameliorated. 3 We came to this conclusion after discovering the wide variation in EHR experience that exists within all EHR customer bases (see Table 1 ). This variation cannot be ignored as it is not caused by differences in regulatory burden or programing design. We express concern that user competency often does not receive the strong focus it needs. These findings do not negate the need for EHR developers to continue to improve their user interfaces to be more intuitive, nor do they negate the critical need to reexamine the current regulatory and billing requirements that drive so much of the clinical documentation burden faced by providers today, but we believe that a greater focus on education and training is the overlooked opportunity that could enable EHR technology to drive substantial gains in the quadruple aim. 4 5
Table 1

Variation in experience by EHR

Number of organizations with vendor deployed (and >10 surveys collected)Lowest organization net EHR experience scoreHighest organization net EHR experience score
Vendor 1104−1373
Vendor 226−5143
Vendor 313−5831
Vendor 412−4154
Vendor 57−2642
Vendor 65−1521
Vendor 75−60−42

Abbreviation: EHR, electronic health record.

Abbreviation: EHR, electronic health record.

Variation Driven by Differences in User Experience More than Software

Multiple studies have indicated that users of the same EHR software can have significantly different experiences, from safety performance 6 7 to mortality outcomes. 8 9 Collaborative experience was decomposed to identify the variation that is explained at the EHR, organization, specialty, and user level (see Table 2 ). Less than 20% of all variation was explainable by the EHR in use, with over 50% of variation explained at the physician user level. Similarly, within the seven EHR solutions measured, a very unsuccessful provider organization was identified in each customer base, and a successful customer was identified in six of the seven customer bases (see Table 1 ).
Table 2

Variation decomposition by measurement unit

GroupDefinitionAverage net EHR experience score differencePercent of total variation
Variation by EHRDifference between collaborative average and average for each EHR16.61 points19.8
Variation by organizationDifference between average for each EHR and organizations using that EHR12.74 points15.1
Variation by specialtyDifference between average for each organization by EHR and specialties within that organization12.1 points14.4
Variation by userDifference between average specialty experience in an organization with an EHR and individual user experiences within that specialty.42.42 points50.6

Abbreviation: EHR, electronic health record.

Abbreviation: EHR, electronic health record. This variation at a user level in every software customer base indicates that no current enterprise software solution (as every major U.S. commercial EHR software solution was measured) has been identified that is so user friendly that it removes individual user variation in experience. Provider organizations seeking to create a positive user experience cannot expect the usability of the software to create consistent physician user success. Instead, factors unique to individual physician users must be identified to reduce negative variation.

EHR Training/Education is the Major Predictor of Positive User Experience

In the Arch Collaborative large dataset, the single greatest predictor of user experience is not which EHR a provider uses nor what percent of an organization's operating budget is spent on information technology, but how users rate the quality of the EHR-specific training they received. These data are consistent with multiple anecdotal case studies. 10 11 12 13 14 Across collaborative organizations, we have observed 475 instances in which two physicians of the same specialty using the same EHR in the same organization reported antipodal responses as to whether their EHR enables them to deliver high-quality health care (in each instance, one physician “strongly agreed” and the other “strongly disagreed”). In over 89% of these instances, the physician who strongly agreed also reported better training, more training efforts, or more effort expended in setting up EHR personalization.

Physicians Indicate Higher Quality EHR Training Drives Better Care

For a physician, feeling safe with the tools of medicine is about more than just the user interface. Physicians who report poor training are over 3.5 times more likely to report that their EHR does not enable them to deliver quality care (see Table 3 ).
Table 3

Training feedback on arch collaborative survey

N Percent of care providers who disagree or strongly disagree with the statement “This EHR enables me to deliver high-quality care”
All care providers who agree or strongly agree that their “initial training prepared them well”15,42510.3
All care providers who disagree or strongly disagree that their “initial training prepared them well”9,73938.4
Providers with scribes who agree or strongly agree that their “initial training prepared them well”79615.1
Providers with scribes who disagree or strongly disagree that their “initial training prepared them well”56244.4

Abbreviation: EHR, electronic health record.

Abbreviation: EHR, electronic health record. While user interface matters, practice does also. A scalpel is a tool that has a very simple interface and use, but using it with confidence and safety requires knowledge of anatomy and surgical techniques coupled with practice to use it skillfully. In other industries, it is well recognized that education and training are of paramount importance to the successful use of professional-grade software. We need to recognize that this also holds true for EHRs and the practice of medicine. While documentation is a common burden, expert EHR users report a greater ability to find critical clinical information in the ever-growing pool of data available to caregivers today.

Standards Needed for EHR Education

When it comes to EHR education, it is critical to consider both the quantity and quality of the educational opportunities. Responses to the survey given to collaborative participants show that significant jumps in users' overall satisfaction with the EHR experience occur for every additional hour of initial EHR education they receive. Organizations requiring less than 4 hours of education for new providers appear to be creating a frustrating experience for their clinicians (see Table 4 ). These organizations have lower training satisfaction, lower self-reported proficiency, and are less likely to report that their EHR enables them to deliver quality care.
Table 4

Hours of training versus average next EHR experience score

Hours of required new-provider trainingOrganizationsAverage net EHR experience score
<4 h116
4 h2117.2
5–6 h1320.6
7–8 h2016.7
10–16 h1227.5

Abbreviation: EHR, electronic health record.

Abbreviation: EHR, electronic health record. Abbreviations: EHR, electronic health record; NEES, net EHR experience score. Abbreviations: EHR, electronic health record; NEES, net EHR experience score. Regarding the quality of EHR education, there is huge variation between the collaborative organizations in terms of how they have structured training and educational programs and user perceptions of the quality of these programs, for both initial and ongoing training. Much has been published regarding educational best practices. 11 12 13 14 This research indicates high variation between organizations in the quality of the EHR education in the outcomes this variation creates. While outside the scope of this research, significant variation exists between organizations in the quality and quantity of EHR education that is provided to users, resulting in significant variation in user experiences between organizations.

EHR Personalization Tools—A Key to Success

One key aspect of EHR use that we find significantly underutilized in EHR training is the power of user personalization. Personalization features are common with nearly every consumer technology (phones, computers, web browsers, and car computers) and help users get from a common technology what they want and need for their specific situations. One of the most consistent observations seen across the collaborative organizations is how powerful EHR personalization can be and how much adoption is lacking today. Focusing organizational support and resources on ongoing education that helps providers personalize the retrieval and presentation of data leads to marked improvement in physician satisfaction.

The Future—More Investment in EHR Training Needed

Looking forward, we foresee that increasingly sophisticated decision support will be integrated into EHRs, positively affecting patient care in a dramatic way. 6 For this vision to become a reality, physicians will need to know the limits of their technology's advice in the same way that pilots know the limits of a plane's autopilot. Without clearly understanding the EHR's limits or how to use the technology, care providers will not trust the technology they work with. While the Arch Collaborative research has convinced us that the greatest opportunity for progressing the value of the EHR currently lies in improved user training, this approach clearly needs to be balanced with a parallel focus on better designed and smarter software that can better meet nuanced needs of health care. For EHR software to revolutionize health care, both the software and the use of that complicated software need to progress in parallel. As users redouble their efforts to understand and utilize the full functionality available to them, EHR vendors can better anticipate the needs of end users trying to leverage the strengths of an intelligent electronic system. We invite care providers to consider our claims that (1) delivering high-quality care in the 21st century requires caregivers to be well educated on the technology they utilize daily and (2) caregivers who do not understand EHR technology are a threat to quality care and will likely not realize any efficiency gains in using the EHR nor be able to use the technology fully to advance care quality. Given these results, we advocate for health care delivery organizations to increase the EHR/technology education and support they make available to their providers. We also advocate for caregivers to adopt EHR technology expertise as a core competency of their profession. We are collectively encouraged that these changes, along with future improvements in EHR interfaces and technology, will unlock the potential of care providers working with information technology to revolutionize care quality and efficiency.

Clinical Relevance Statement

Dissatisfaction with the EHR is often associated with physician burnout. Physicians who reported receiving strong EHR training were significantly more likely to report feeling that the EHR enables them to deliver high-quality care ( p  < .01). Health care delivery organizations should invest in EHR training with a focus on adoption of EHR personalization tools. The single greatest predictor of EHR satisfaction at a given health care delivery organization is: IT budget. EHR training quality. EHR vendor software choice. EHR vendor software version. Correct Answer: The correct answer is option b. In this large dataset, user perception of EHR training quality was strongly associated with EHR satisfaction, more so than vendor choice or IT budget. One EHR training best practice for achieving physician EHR mastery: Classroom training of at least 8 hours. Classroom training of at least 4 hours. EHR personalization. Vendor demos. Correct Answer: The correct answer is option c. In this large dataset, EHR personalization was strongly associated with a perception of high-quality training.
Table 5

Correlation between personalization adoption and net EHR experience score for users

Personalization/optimizationNumber of personalization adoptersNet EHR experience score for personalization adoptersNumber of personalization nonadoptersNet EHR experience score for personalization nonadoptersPercent adoptionNEES difference
Template personalization25,24029.36,196−1.18030.4
Order list personalization18,58331.211,46810.36220.8
Order set optimization15,22929.312,78614.35415.0
Navigation macro personalization15,01129.414,51815.65113.8
Filter personalization14,28935.316,29811.94723.4
Personalization of shortcuts12,59637.417,06214.24223.2
Layouts personalization10,35739.417,36815.33724.1
Personalization of report views9,31640.919,23218.43322.5
Personalization of sort orders for lists6,66440.219,45017.726%22.5

Abbreviations: EHR, electronic health record; NEES, net EHR experience score.

Table 6

Correlation between personalization adoption and NEES for organizations

Percent of personalization adopted by providersAverage net EHR experience score for organizationsNumber of organizations
10–20−21.53
20–30−29-6
30–40−21.115
40–5015.744
50–6027.354
60–7025.110

Abbreviations: EHR, electronic health record; NEES, net EHR experience score.

  14 in total

1.  Mixed results in the safety performance of computerized physician order entry.

Authors:  Jane Metzger; Emily Welebob; David W Bates; Stuart Lipsitz; David C Classen
Journal:  Health Aff (Millwood)       Date:  2010-04       Impact factor: 6.301

2.  Transitional Chaos or Enduring Harm? The EHR and the Disruption of Medicine.

Authors:  Lisa Rosenbaum
Journal:  N Engl J Med       Date:  2015-10-22       Impact factor: 91.245

3.  Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system.

Authors:  Yong Y Han; Joseph A Carcillo; Shekhar T Venkataraman; Robert S B Clark; R Scott Watson; Trung C Nguyen; Hülya Bayir; Richard A Orr
Journal:  Pediatrics       Date:  2005-12       Impact factor: 7.124

4.  The triple aim: care, health, and cost.

Authors:  Donald M Berwick; Thomas W Nolan; John Whittington
Journal:  Health Aff (Millwood)       Date:  2008 May-Jun       Impact factor: 6.301

5.  From triple to quadruple aim: care of the patient requires care of the provider.

Authors:  Thomas Bodenheimer; Christine Sinsky
Journal:  Ann Fam Med       Date:  2014 Nov-Dec       Impact factor: 5.166

6.  The value of clinical teachers for EMR implementations and conversions.

Authors:  L A Stevens; J L Pantaleoni; C A Longhurst
Journal:  Appl Clin Inform       Date:  2015-02-11       Impact factor: 2.342

7.  Improving the safety of health information technology requires shared responsibility: It is time we all step up.

Authors:  Dean F Sittig; Elisabeth Belmont; Hardeep Singh
Journal:  Healthc (Amst)       Date:  2017-07-15

8.  Collaborating for Competency-A Model for Single Electronic Health Record Onboarding for Medical Students Rotating among Separate Health Systems.

Authors:  Anne G Pereira; Michael Kim; Marcus Seywerd; Brooke Nesbitt; Michael B Pitt
Journal:  Appl Clin Inform       Date:  2018-03-21       Impact factor: 2.342

9.  A New EHR Training Curriculum and Assessment for Pediatric Residents.

Authors:  Kathryn Stroup; Benjamin Sanders; Bruce Bernstein; Leah Scherzer; Lee M Pachter
Journal:  Appl Clin Inform       Date:  2017-12-14       Impact factor: 2.342

10.  Designing An Individualized EHR Learning Plan For Providers.

Authors:  Lindsay A Stevens; Yumi T DiAngi; Jonathan D Schremp; Monet J Martorana; Roberta E Miller; Tzielan C Lee; Natalie M Pageler
Journal:  Appl Clin Inform       Date:  2017-12-20       Impact factor: 2.342

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1.  Promoting Quality Face-to-Face Communication during Ophthalmology Encounters in the Electronic Health Record Era.

Authors:  Sally L Baxter; Helena E Gali; Michael F Chiang; Michelle R Hribar; Lucila Ohno-Machado; Robert El-Kareh; Abigail E Huang; Heather E Chen; Andrew S Camp; Don O Kikkawa; Bobby S Korn; Jeffrey E Lee; Christopher A Longhurst; Marlene Millen
Journal:  Appl Clin Inform       Date:  2020-02-19       Impact factor: 2.342

2.  Clinical Documentation as End-User Programming.

Authors:  Adam Rule; Isaac H Goldstein; Michael F Chiang; Michelle R Hribar
Journal:  Proc SIGCHI Conf Hum Factor Comput Syst       Date:  2020-04

3.  Multidisciplinary Sprint Program Achieved Specialty-Specific EHR Optimization in 20 Clinics.

Authors:  Amber Sieja; Eric Kim; Heather Holmstrom; Stephen Rotholz; Chen Tan Lin; Christine Gonzalez; Cortney Arellano; Sarah Hutchings; Denise Henderson; Katie Markley
Journal:  Appl Clin Inform       Date:  2021-04-21       Impact factor: 2.342

4.  The association between perceived electronic health record usability and professional burnout among US nurses.

Authors:  Edward R Melnick; Colin P West; Bidisha Nath; Pamela F Cipriano; Cheryl Peterson; Daniel V Satele; Tait Shanafelt; Liselotte N Dyrbye
Journal:  J Am Med Inform Assoc       Date:  2021-07-30       Impact factor: 4.497

5.  A systematic review of contributing factors of and solutions to electronic health record-related impacts on physician well-being.

Authors:  Oliver T Nguyen; Nyasia J Jenkins; Neel Khanna; Shivani Shah; Alexander J Gartland; Kea Turner; Lisa J Merlo
Journal:  J Am Med Inform Assoc       Date:  2021-04-23       Impact factor: 4.497

6.  Building the evidence-base to reduce electronic health record-related clinician burden.

Authors:  Christine Dymek; Bryan Kim; Genevieve B Melton; Thomas H Payne; Hardeep Singh; Chun-Ju Hsiao
Journal:  J Am Med Inform Assoc       Date:  2021-04-23       Impact factor: 4.497

7.  Electronic Health Record Use among Ophthalmology Residents while on Call.

Authors:  Christopher P Long; Ming Tai-Seale; Robert El-Kareh; Jeffrey E Lee; Sally L Baxter
Journal:  J Acad Ophthalmol       Date:  2020-07

8.  Association between evidence-based training and clinician proficiency in electronic health record use.

Authors:  Laura Hollister-Meadows; Rachel L Richesson; Jennie De Gagne; Neil Rawlins
Journal:  J Am Med Inform Assoc       Date:  2021-03-18       Impact factor: 4.497

9.  Multicenter Analysis of Electronic Health Record Use among Ophthalmologists.

Authors:  Sally L Baxter; Helena E Gali; Mitul C Mehta; Scott E Rudkin; John Bartlett; James D Brandt; Catherine Q Sun; Marlene Millen; Christopher A Longhurst
Journal:  Ophthalmology       Date:  2020-06-07       Impact factor: 12.079

Review 10.  Factors associated with nurse well-being in relation to electronic health record use: A systematic review.

Authors:  Oliver T Nguyen; Shivani Shah; Alexander J Gartland; Arpan Parekh; Kea Turner; Sue S Feldman; Lisa J Merlo
Journal:  J Am Med Inform Assoc       Date:  2021-06-12       Impact factor: 4.497

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