Literature DB >> 19490768

Imminent adopters of electronic health records in ambulatory care.

Rainu Kaushal1, David W Bates, Chelsea A Jenter, Shannon A Mills, Lynn A Volk, Elisabeth Burdick, Micky Tripathi, Steven R Simon.   

Abstract

BACKGROUND: Although evidence suggests electronic health records (EHRs) can improve quality and efficiency, provider adoption rates in the US ambulatory setting are relatively low. Prior studies have identified factors correlated with EHR use, but less is known about characteristics of physicians on the verge of adoption.
OBJECTIVE: To compare characteristics of physicians who are imminent adopters of EHRs with EHR users and non-users. DESIGN AND PARTICIPANTS: A survey was mailed (June - November 2005) to a stratified random sample of all medical practices in Massachusetts. One physician from each practice (n=1884) was randomly selected to participate. Overall, 1345 physicians (71.4%) responded to the survey, with 1082 eligible for analysis due to exclusion criteria. 'Imminent adopters' were those planning to adopt EHRs within 12 months. MEASUREMENTS: We assessed physician and practice characteristics, availability of technology, barriers to adoption or expansion of health information technology (HIT), computer proficiency, and financial considerations.
RESULTS: Compared to non-users, imminent adopters were younger, more experienced with technology, and more often in practices engaged in quality improvement. More imminent adopters owned or partly owned their practices (57.4%) than users (33.5%; p<0.001), but fewer imminent adopters owned their practices than non-users (65.7%; p<0.001). Additionally, more imminent adopters (26.0%) reported personal financial incentives for HIT use than users (14.8%; p<0.001) and non-users (10.8%; p<0.001).
CONCLUSIONS: Imminent adopters of EHRs differed from users and non-users. Financial considerations appear to play a major role in adoption decisions. Knowledge of these differences may assist policy-makers and healthcare leaders as they work to increase EHR adoption rates.

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Year:  2009        PMID: 19490768     DOI: 10.14236/jhi.v17i1.709

Source DB:  PubMed          Journal:  Inform Prim Care        ISSN: 1475-9985


  9 in total

1.  Are physicians' perceptions of healthcare quality and practice satisfaction affected by errors associated with electronic health record use?

Authors:  Jennifer S Love; Adam Wright; Steven R Simon; Chelsea A Jenter; Christine S Soran; Lynn A Volk; David W Bates; Eric G Poon
Journal:  J Am Med Inform Assoc       Date:  2011-12-23       Impact factor: 4.497

2.  Transitioning between ambulatory EHRs: a study of practitioners' perspectives.

Authors:  Stephanie O Zandieh; Erika L Abramson; Elizabeth R Pfoh; Kay Yoon-Flannery; Alison Edwards; Rainu Kaushal
Journal:  J Am Med Inform Assoc       Date:  2011-08-28       Impact factor: 4.497

3.  EHR adopters vs. non-adopters: Impacts of, barriers to, and federal initiatives for EHR adoption.

Authors:  Eric W Jamoom; Vaishali Patel; Michael F Furukawa; Jennifer King
Journal:  Healthc (Amst)       Date:  2014-03-18

4.  Approaches and challenges to optimising primary care teams' electronic health record usage.

Authors:  Nancy Pandhi; Wan-Lin Yang; Zaher Karp; Alexander Young; John W Beasley; Sally Kraft; Pascale Carayon
Journal:  Inform Prim Care       Date:  2014

5.  Adoption of e-health technology by physicians: a scoping review.

Authors:  Chloe de Grood; Aida Raissi; Yoojin Kwon; Maria Jose Santana
Journal:  J Multidiscip Healthc       Date:  2016-08-01

6.  Health Care Provider Adoption of eHealth: Systematic Literature Review.

Authors:  Junhua Li; Amir Talaei-Khoei; Holly Seale; Pradeep Ray; C Raina Macintyre
Journal:  Interact J Med Res       Date:  2013-04-16

7.  Issues Regarding the Implementation of eHealth: Preparing for Future Influenza Pandemics.

Authors:  Junhua Li; Holly Seale; Pradeep Ray; William Rawlinson; Lundy Lewis; C Raina Macintyre
Journal:  Interact J Med Res       Date:  2012-12-06

8.  Generating unique IDs from patient identification data using security models.

Authors:  Emad A Mohammed; Jonathan C Slack; Christopher T Naugler
Journal:  J Pathol Inform       Date:  2016-12-30

9.  Using the technology acceptance model to explore health provider and administrator perceptions of the usefulness and ease of using technology in palliative care.

Authors:  M Nguyen; J Fujioka; K Wentlandt; N Onabajo; I Wong; R S Bhatia; O Bhattacharyya; V Stamenova
Journal:  BMC Palliat Care       Date:  2020-09-07       Impact factor: 3.234

  9 in total

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