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The American Journal of Managed Care Special Issue: Health Information Technology
Improving Adherence to Cardiovascular Disease Medications With Information Technology
William M. Vollmer, PhD; Ashli A. Owen-Smith, PhD; Jeffrey O. Tom, MD, MS; Reesa Laws, BS; Diane G. Ditmer, PharmD; David H. Smith, PhD; Amy C. Waterbury, MPH; Jennifer L. Schneider, MPH; Cyndee H. Yo
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Patrick Kierkegaard, PhD; Rainu Kaushal, MD, MPH; and Joshua R. Vest, PhD, MPH
The 3 Key Themes in Health Information Technology
Julia Adler-Milstein, PhD
Leveraging EHRs to Improve Hospital Performance: The Role of Management
Julia Adler-Milstein, PhD; Kirstin Woody Scott, MPhil; and Ashish K. Jha, MD, MPH
Electronic Alerts and Clinician Turnover: The Influence of User Acceptance
Sylvia J. Hysong, PhD; Christiane Spitzmuller, PhD; Donna Espadas, BS; Dean F. Sittig, PhD; and Hardeep Singh, MD, MPH
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Eta S. Berner, EdD; Jeffrey H. Burkhardt, PhD; Anantachai Panjamapirom, PhD; and Midge N. Ray, MSN, RN
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Primary Care Capacity as Insurance Coverage Expands: Examining the Role of Health Information Technology
Renuka Tipirneni, MD, MSc; Ezinne G. Ndukwe, MPH; Melissa Riba, MS; HwaJung Choi, PhD; Regina Royan, MPH; Danielle Young, MPH; Marianne Udow-Phillips, MHSA; and Matthew M. Davis, MD, MAPP
Health Information Exchange and the Frequency of Repeat Medical Imaging
Joshua R. Vest, PhD, MPH; Rainu Kaushal, MD, MPH; Michael D. Silver, MS; Keith Hentel, MD, MS; and Lisa M. Kern, MD
Information Technology and Hospital Patient Safety: A Cross-Sectional Study of US Acute Care Hospitals
Ajit Appari, PhD; M. Eric Johnson, PhD; and Denise L. Anthony, PhD
Automated Detection of Retinal Disease
Lorens A. Helmchen, PhD; Harold P. Lehmann, MD, PhD; and Michael D. Abràmoff, MD, PhD
Trending Health Information Technology Adoption Among New York Nursing Homes
Erika L. Abramson, MD, MS; Alison Edwards, MS; Michael Silver, MS; Rainu Kaushal, MD, MPH; and the HITEC investigators
Electronic Health Record Availability Among Advanced Practice Registered Nurses and Physicians
Janet M. Coffman, PhD, MPP, MA; Joanne Spetz, PhD; Kevin Grumbach, MD; Margaret Fix, MPH; and Andrew B. Bindman, MD
The Value of Health Information Technology: Filling the Knowledge Gap
Robert S. Rudin, PhD; Spencer S. Jones, PhD; Paul Shekelle, MD, PhD; Richard J. Hillestad, PhD; and Emmett B. Keeler, PhD
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David Newman, JD, PhD; Carolina-Nicole Herrera, MA; and Stephen T. Parente, PhD
The Effects of Health Information Technology Adoption and Hospital-Physician Integration on Hospital Efficiency
Na-Eun Cho, PhD; Jongwha Chang, PhD; and Bebonchu Atems, PhD

Primary Care Capacity as Insurance Coverage Expands: Examining the Role of Health Information Technology

Renuka Tipirneni, MD, MSc; Ezinne G. Ndukwe, MPH; Melissa Riba, MS; HwaJung Choi, PhD; Regina Royan, MPH; Danielle Young, MPH; Marianne Udow-Phillips, MHSA; and Matthew M. Davis, MD, MAPP
Primary care physicians using more health information technology were less likely to accept new patients.

Objectives
Under the Affordable Care Act, many newly insured Americans have the challenge of establishing care with a primary care physician (PCP). We sought to examine whether health information technology (HIT) use in primary care practices was associated with anticipated capacity to accept new patients.

Study Design
Secondary analysis of a cross-sectional survey of Michigan PCPs from the specialties of pediatrics, internal medicine, and family medicine, conducted from October to December 2012. HIT use was considered independently for 8 types of HIT and in aggregate as a total count of HIT in use. Primary care capacity was assessed as self-reported capacity to accept new patients.

Results
Of 739 respondents, 83% reported they anticipated capacity to accept new patients. In multivariable analysis, we found that physicians using a greater number of HITs were significantly less likely to anticipate capacity to accept new patients (adjusted odds ratio [OR] = 0.86; 95% CI, 0.76-0.97). PCPs with higher HIT use were also less likely to accept patients with private insurance (adjusted OR 0.87; 95% CI, 0.77-0.97), but not with Medicaid (adjusted OR 0.94; 95% CI, 0.84-1.05) or Medicare (adjusted OR 0.91; 95% CI, 0.83-1.01). Among individual HITs, electronic health records (adjusted OR 0.54; 95% CI, 0.30-0.96) and electronic access to admitting hospital records (adjusted OR 0.46; 95% CI, 0.22-0.96) were the only HITs significantly associated with lower anticipated primary care capacity.

Conclusions
PCPs using a greater number of HITs were less likely to anticipate capacity to accept new patients. Implementation of HIT and other practice innovations must be carefully coordinated to optimize capacity to care for the newly insured.

Am J Manag Care. 2014;20(11 Spec No. 17):SP547-SP554

Health information technology (HIT) has been widely touted for its potential to improve primary care practice efficiency and capacity, but this study’s findings call into question whether this is occurring.
  • Physicians using a greater number of HITs were significantly less likely to anticipate capacity to accept new patients.
  • In an era of concurrent HIT and insurance coverage expansions, policy makers must weigh the unintended consequences of each in order to optimize capacity to care for the newly insured.

  Millions of Americans who have recently gained insurance coverage through the Patient Protection and Affordable Care Act (ACA) now face the challenge of establishing care with a primary care provider (PCP). For coverage to translate to access, primary care providers will need adequate capacity to accept new patients. Efforts have long been under way to increase primary care capacity, with a principal emphasis on expanding the primary care workforce.1 More recently, innovations in primary care practices, such as improvements in clinic work flow and care coordination, have also been in- troduced to improve both capacity and efficiency of care.2,3 Many of these innovation efforts have included the implementation of health information technology (HIT), such as electronic health records (EHRs), patient portals, and reminder systems.

Since the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, HIT has been widely promoted as potentially cost- and time-saving for practices, and the early stages of “meaningful use” of these technologies have been heavily incentivized. While some studies have shown improvements in practice efficiency associated with use of EHR and other HIT resources,4-8
others have shown decreases in efficiency or mixed results.9-20 It is conceivable that improvements in efficiency could allow primary care physicians to see more patients in the office, thus increasing their overall capacity. In fact, this very idea has been espoused by the Office of the National Coordinator for Health Information Technology.21 Nonetheless, empirical data to support the connection between HIT use and primary care capacity are limited, and it is possible that adjusting to newly implemented HITs may instead cause physicians to limit their efficiency and have reduced capacity to see patients. Our objective was to examine whether use of HIT in primary care practices was independently associated with anticipated capacity to accept new patients.

METHODS

Study Design

We performed a secondary analysis of data from the Center for Healthcare Research & Transformation and the University of Michigan Child Health Evaluation and Research Unit 2012 Survey of Michigan Physicians. The purpose of this cross-sectional survey was to understand the challenges PCPs face in their practices during the era of health reform, with a focus on anticipated capacity to care for newly insured residents. Questions were developed by members of the research team experienced in survey design (MR, MUP, MMD). Survey questions were piloted with primary care physicians and modified to optimize respondent understanding.

Between October and December 2012, surveys were mailed to PCPs across the state of Michigan. Potential participants received up to 3 mailings, and a $5 incentive was included in the first mailing to encourage response. The study was granted exempt status by the University of Michigan Medical School Institutional Review Board, as it included only de-identified data.

Sample

Five hundred Michigan physicians from each of 3 primary care specialties (pediatrics, internal medicine, and family medicine) were randomly selected from the American Medical Association (AMA) Physician Masterfile, a comprehensive list that includes both AMA members and nonmembers, for a total of 1500 physicians in the original sample. Physicians who responded but reported they were not practicing primary care (eg, retired, or exclusively practicing inpatient medicine) were excluded from the analysis.

Measures

Primary Care Capacity. The primary outcome was PCPs’ assessment of their future capacity to accept new patients. Participants were asked, “If the number of Michigan patients with insurance coverage increases in the future, will you have the capacity to accept additional patients?” with yes/no response options (see eAppendix A, available at www.ajmc.com) for all relevant questions from the survey instrument. For those who responded yes, we also asked about ability to expand practice capacity by payer type (eg, for patients with private insurance, Medicaid, or Medicare) and examined this as a secondary outcome.

HIT use. The primary predictor variable was PCPs’ use of HIT. Participants were asked whether they utilized any of 8 types of HIT (see eAppendix A): EHRs, patient registries, electronic prescribing, Web portals for patients to schedule their own appointments, Web portals for patients to request prescription refills, reminder/ recall systems to contact patients about recommended services, state immunization registry participation, and electronic access to admitting hospital records. A panel of stakeholders from the Michigan State Medical Society and the Michigan Osteopathic Association was convened to develop survey items that corresponded to meaningful use and patient-centered medical home initiatives that primary care physicians faced at the time of the survey. After consensus-building among panel participants, the HIT types that best represented current incentives for implementation in primary care practices were selected for inclusion in the survey. We examined each HIT type independently, and also in aggregate by creating an index of HIT use. The index was defined as a total count of the number of HITs currently in use by the physician.


Covariates. Survey items included physician demographic characteristics such as gender, specialty, number of years in practice (<10 years, 10-20 years, or >20 years, categorized for the multivariable analysis as either ≤20 or >20 years), and self-reported weekly visit volume (categorized as ≤100 or >100 patients seen per week per physician), as well as practice characteristics such as size (number of physicians in practice considered as a continuous variable), self-reported current payer mix, and zip code of the practice.

For payer mix, we created a composite variable to determine the predominant payer for the practice. A payer (eg, private insurance, Medicaid, or Medicare) was considered predominant if a physician had only 1 payer type that constituted more than 30% of the physician’s patient population. We defined a “mixed” payer category for those practices with more than 1 payer representing greater than 30% of patients, or with no predominant payer representing at least 30% of patients. We assessed urbanicity of the practice setting by linking Federal Information Processing Standard county codes obtained from self-reported zip codes to the US Department of Agriculture Economic Research Service 2013 Urban Influence Codes. Urban Influence Codes provide a standard 12-point classification scheme to distinguish counties by population density and proximity of the population to the largest town or city, allowing categorization into metropolitan (Urban Influence Codes 1-2) and nonmetropolitan (Urban Influence Codes 3-12) counties.22

Statistical Analysis

We used standard descriptive statistics to characterize physician respondents’ baseline characteristics, as well as their overall use of HIT and anticipated capacity to accept new patients. We then used logistic regression to perform bivariate analyses of associations between predictor variables and the primary outcome of capacity, as well as the secondary outcome of capacity by payer type. To examine the independent relationship between HIT use and primary care capacity after adjusting for covariates, we conducted multivariable logistic regression analysis and expressed the results as adjusted odds ratios (ORs) with 95% confidence intervals. Using the same estimation model, we also obtained the adjusted predicted probabil- ity of capacity to accept new patients at different numbers of HIT in use. A 2-sided P <.05 was considered statistically significant. We included the physician-, practice- and community-level covariates mentioned above in order to control for potential confounders. The unit of analysis was the physician.

In order to examine possible moderation of the HIT/capacity relationship by practice size, we subsequently included an interaction term between HIT use and practice size in the logistic regression model. We assessed the main relationship within each group (eg, within small practice size) and compared that between groups (small vs large practice size).

The proportion of responses with missing data was less than 5% for all individual items. To address missing values in the final multivariable model (12% in aggregate), we performed multiple imputation by using a chained equation23,24 and generated 10 replications of the imputed data set. We repeated our main multivariable analysis with the imputed data sets and observed similar results to the analysis using the non-imputed data set (ie, with no significant difference in effect size or confidence intervals). The results reported here are from the original, non-imputed dataset.

To assess goodness of fit, we checked the final model with both the area under the curve method (C statistic = 0.69) and the Hosmer-Lemeshow goodness of fit test (which was nonsignificant, suggesting good model fit). All analyses were performed using STATA version 13 (Stata Corp, College Station, Texas).

RESULTS

Survey respondents (N = 739, response rate = 49%) were similar to the original sample of physicians from the AMA Physician Masterfile (see eAppendix B). While the response rate is lower than in surveys of nonphysicians, it is consistent with other surveys of physicians with small financial incentives. The respondents included an approximately equal number of men and women (356 women, or 48%), with similar representation from each of the 3 specialties (Table 1). Most physician respondents had been in practice for more than 10 years (76%) and saw fewer than 100 patients in a typical week (66%). Sixty-one percent of physician respondents practiced in settings with fewer than 6 physicians. Many practices accepted a diversity of payer types, with 43% of practices having no predominant payer. The majority of respondents’ practices (85%) were located in urban areas.

In aggregate, PCPs used a mean of 5.1 HITs (SD = 1.9) in their practices. Among these, the most common HIT in use was electronic prescribing (89% of PCPs). The least common HIT in use was a Web portal for patients to schedule their own appointments (21% of PCPs) (Figure 1).

 
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