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The American Journal of Managed Care January 2019
The Gamification of Healthcare: Emergence of the Digital Practitioner?
Eli G. Phillips Jr, PharmD, JD; Chadi Nabhan, MD, MBA; and Bruce A. Feinberg, DO
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Rajesh Balkrishnan, PhD
The Health Information Technology Special Issue: New Real-World Evidence and Practical Lessons
Mary E. Reed, DrPH
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Vincent X. Liu, MD, MS; Nimah Haq, MPH; Ignatius C. Chan, MD; and Brian Hoberman, MD, MBA
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Impact of Primary and Specialty Care Integration via Asynchronous Communication
Eric D. Newman, MD; Paul F. Simonelli, MD, PhD; Shelly M. Vezendy, BS; Chelsea M. Cedeno, BS; and Daniel D. Maeng, PhD
Patient and Clinician Experiences With Telehealth for Patient Follow-up Care
Karen Donelan, ScD, EdM; Esteban A. Barreto, MA; Sarah Sossong, MPH; Carie Michael, SM; Juan J. Estrada, MSc, MBA; Adam B. Cohen, MD; Janet Wozniak, MD; and Lee H. Schwamm, MD
Understanding the Relationship Between Data Breaches and Hospital Advertising Expenditures
Sung J. Choi, PhD; and M. Eric Johnson, PhD
Organizational Influences on Healthcare System Adoption and Use of Advanced Health Information Technology Capabilities
Paul T. Norton, MPH, MBA; Hector P. Rodriguez, PhD, MPH; Stephen M. Shortell, PhD, MPH, MBA; and Valerie A. Lewis, PhD, MA
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Sunny C. Lin, MS; John M. Hollingsworth, MD, MS; and Julia Adler-Milstein, PhD
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Dori A. Cross, PhD; Jeffrey S. McCullough, PhD; and Julia Adler-Milstein, PhD

Impact of Primary and Specialty Care Integration via Asynchronous Communication

Eric D. Newman, MD; Paul F. Simonelli, MD, PhD; Shelly M. Vezendy, BS; Chelsea M. Cedeno, BS; and Daniel D. Maeng, PhD
Geisinger’s Ask-a-Doc program, which enables direct asynchronous communication between primary and specialty care, was associated with lower healthcare utilization and cost, implying more efficient care.

To test the hypothesis that AAD was associated with reductions in total cost and care utilization, this study relied on a retrospective analysis of data obtained from Geisinger’s EHR and the Geisinger Health Plan (GHP) claims database, comparing the intervention group (ie, patients who had been referred for AAD specialist consults) with a comparison group identified from the same claims database. This study was conducted as a part of Geisinger’s quality improvement initiative and therefore was not subject to institutional review board oversight and review.

From the EHR, the complete list of patients for whom AAD specialty consults had been made at any point between January 1, 2014, and December 31, 2016, was obtained (N = 6434 patients). From that list, the subset of those patients who had GHP coverage during the same period was identified (2190 patients). Because AAD was available for all patients treated by Geisinger PCPs regardless of the patients’ payer status, this subset represented approximately 34% of the patients included in the original list (2190 of 6434). These 2190 patients therefore constituted the intervention group.

The comparison group was identified from the GHP claims data based on the following criteria: those who (1) had not been exposed to the AAD program during the same period (January 1, 2014, through December 31, 2016), (2) were attributed to 1 of Geisinger’s PCPs, and (3) made at least 1 specialist visit with 1 or more of the specialties included in the AAD program. The claims data were aggregated to a per-member-per-month level to capture total cost of care (with and without prescription drug costs) and care utilization (rates of acute hospitalization, emergency department [ED] visits, and physician office visits) during the first, second, and third months after the index AAD consult. For the comparison group, the index date was defined as the first month of the observation period during which the patient had at least 1 primary care visit and at least 2 specialist visits, in which at least 1 of the specialist visits was to an AAD-participating specialty. Those instances in which there was only 1 specialist visit in a given month were specifically not considered as index dates for the comparison group because such situations likely reflect routine follow-up specialist visits that would not be subject to the AAD impact.

Total cost of care was defined as total allowed amount (ie, GHP’s payment to providers plus any out-of-pocket expenses paid to the providers by the member). For those patients who had prescription drug coverage through GHP, the total cost of care included allowed amounts for all prescription drugs purchased by the patient during each month of observation. Approximately 90% of the patients included in the study sample had prescription drug coverage through GHP.


To estimate the AAD program’s impact on care utilization and total cost, a difference-in-differences (DID) approach was used via a set of multivariate linear regression models with patient fixed effects. The key explanatory variables in each regression model were the binary indicator for whether the patient was in the AAD intervention group or not and a set of indicator variables for the postintervention period (ie, 0, 1, 2, and 3 months after the index date), as well as a set of interaction terms between these 2 sets of indicator variables. The coefficient on the interaction term represents the DID estimate of the AAD impact on the dependent variable. The effect of the AAD intervention was then represented via differences between the regression-adjusted “observed” and “expected” values of the dependent variables. Expected values were obtained by setting the coefficient on the interaction term to zero and recalculating the regression-adjusted values.

Other covariates included patient age (18-45, 46-60, 61-70, and ≥71 years), sex, count of selected comorbid conditions (up to 9: chronic kidney disease, diabetes, asthma, congestive heart failure, chronic obstructive pulmonary disease, coronary artery disease, hypertension, cancer, and depression), insurance type (commercial, Medicaid, or Medicare), length of time the patient’s PCP had been part of a PCMH (because PCMHs have been shown to be associated with lower cost and acute care utilization21,22), and case management status. Also included were indicators for whether the patient had prescription drug coverage through GHP and for each calendar year in the sample; these covariates accounted for any confounding effects due to prescription drug coverage and yearly inflation in healthcare prices. Also, a set of interaction terms between sex and age categories were included as covariates to further capture any nonlinear interaction effects between age and gender.

The inclusion of patient fixed effects in the regression models accounted for any time-invariant patient characteristics that may confound the estimates.23 This implied that the AAD binary indicator variable was perfectly collinear with the patient fixed-effect term and its coefficient was therefore not separately identified in the regression model. Nevertheless, because the main coefficients of interest were the coefficients on the interaction terms, this did not pose any issue for the purposes of this study. Similarly, although the patient fixed-effects terms were perfectly collinear with the sex indicator variable and its coefficient was therefore not separately identified, the age–sex interaction effects were identified in the model.


Table 2 suggests that the AAD intervention group and the comparison group differed from each other on several key characteristics at baseline. First, the AAD intervention group was younger and more likely to be female, more likely to have Medicaid, and less likely to have Medicare. However, the AAD intervention group had greater frequencies of acute inpatient admissions and ED visits, leading to a slightly higher average total cost of care at the baseline. Also, the AAD intervention group had a higher rate of primary care visits, whereas it had a comparable rate of specialist visits. In addition, asthma, congestive heart failure, and depression were significantly more prevalent among the AAD intervention group.

Table 3 shows the AAD impact on total cost of care, acute care utilization, and physician office visits. AAD was associated with an approximately 14% reduction in total cost, including prescription drugs, during the first month of follow-up and a 20% reduction during the second month, relative to the comparison group. Similar magnitudes of reductions were observed in terms of total medical cost, excluding prescription drugs (15% and 23%, respectively). These reductions in cost appeared to be driven by reductions in ED visits (11% during the first month), primary care visits (10%), and specialist visits (74%) during the same period, relative to the comparison group. However, there was no statistically significant association between acute inpatient admission rates and AAD program exposure. Also, during the second month of follow-up, some statistically significant increases in the physician office visit rates were observed (13% for primary care visits and 8% for specialist visits). In all cases, however, by the third month of follow-up, there was no statistically significant AAD effect relative to the comparison group.

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