title: Personal health record reach in the Veterans Health Administration: a cross-sectional analysis.
abstract: BACKGROUND
  My HealtheVet (MHV) is the personal health record and patient portal developed by the United States Veterans Health Administration (VA). While millions of American veterans have registered for MHV, little is known about how a patient's health status may affect adoption and use of the personal health record.
  
  
  OBJECTIVE
  Our aim was to characterize the reach of the VA personal health record by clinical condition.
  
  
  METHODS
  This was a cross-sectional analysis of all veterans nationwide with at least one inpatient admission or two outpatient visits between April 2010 and March 2012. We compared adoption (registration, authentication, opt-in to use secure messaging) and use (prescription refill and secure messaging) of MHV in April 2012 across 18 specific clinical conditions prevalent in and of high priority to the VA. We calculated predicted probabilities of adoption by condition using multivariable logistic regression models adjusting for sociodemographics, comorbidities, and clustering of patients within facilities.
  
  
  RESULTS
  Among 6,012,875 veterans, 6.20% were women, 61.45% were Caucasian, and 26.31% resided in rural areas. The mean age was 63.3 years. Nationwide, 18.64% had registered for MHV, 11.06% refilled prescriptions via MHV, and 1.91% used secure messaging with their clinical providers. Results from the multivariable regression suggest that patients with HIV, hyperlipidemia, and spinal cord injury had the highest predicted probabilities of adoption, whereas those with schizophrenia/schizoaffective disorder, alcohol or drug abuse, and stroke had the lowest. Variation was observed across diagnoses in actual (unadjusted) adoption and use, with registration rates ranging from 29.19% of patients with traumatic brain injury to 14.18% of those with schizophrenia/schizoaffective disorder. Some of the variation in actual reach can be explained by facility-level differences in MHV adoption and by differences in patients' sociodemographic characteristics (eg, age, race, income) by diagnosis.
  
  
  CONCLUSIONS
  In this phase of early adoption, opportunities are being missed for those with specific medical conditions that require intensive treatment and self-management, which could be greatly supported by functions of a tethered personal health record.
authors.affiliation: Center for Healthcare Organization and Implementation Research / eHealth Quality Enhancement Research Initiative, Edith Nourse Rogers Memorial VA Medical Center, Bedford, MA, United States. stephanie.shimada@va.gov.
authors.forename: Stephanie Leah
authors.identifier: http://orcid.org/0000-0002-6517-5122
authors.initials: SL
authors.lastname: Shimada
authors.forename: Cynthia A
authors.identifier: http://orcid.org/0000-0001-8179-1796
authors.initials: CA
authors.lastname: Brandt
authors.forename: Hua
authors.identifier: http://orcid.org/0000-0002-6163-6550
authors.initials: H
authors.lastname: Feng
authors.forename: D Keith
authors.identifier: http://orcid.org/0000-0002-9941-5470
authors.initials: DK
authors.lastname: McInnes
authors.forename: Sowmya R
authors.identifier: http://orcid.org/0000-0003-4912-4525
authors.initials: SR
authors.lastname: Rao
authors.forename: James A
authors.identifier: http://orcid.org/0000-0001-6149-9376
authors.initials: JA
authors.lastname: Rothendler
authors.forename: David A
authors.identifier: http://orcid.org/0000-0003-3637-6950
authors.initials: DA
authors.lastname: Haggstrom
authors.forename: Erica A
authors.identifier: http://orcid.org/0000-0001-7714-8693
authors.initials: EA
authors.lastname: Abel
authors.forename: Lisa S
authors.identifier: http://orcid.org/0000-0002-4022-7109
authors.initials: LS
authors.lastname: Cioffari
authors.forename: Thomas K
authors.identifier: http://orcid.org/0000-0002-2909-4018
authors.initials: TK
authors.lastname: Houston
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003430:Cross-Sectional Studies; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D055991:Health Records, Personal; D006304:Health Status; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D012648:Self Care; D014481:United States; D014493:United States Department of Veterans Affairs; D014728:Veterans / Q000706:statistics & numerical data*; D058014:Veterans Health
keywords: medical conditions; patient characteristics; personal health records; veterans