Study the Impact of the CommunityRx Program on Health, Self-care and Cost
Condition: Health Behavior
Study type: Interventional

Summary: 
      CommunityRx is a health information technology-based innovation that, starting with the
      patient-health care provider encounter, facilitates self-care coordination for patients,
      caregivers, and providers. The CommunityRx database interfaces with electronic medical
      records to provide patients with a "HealtheRx." A HealtheRx is a list of community-based
      self-care resources tailored to the patients health needs (e.g., a person with diabetes
      receives information about podiatrists, nutrition classes, and other resources need to manage
      diabetes). CommunityRx aims to measurably improve health and health care while reducing
      health care costs especially in underserved health care settings. Specifically, the proposed
      research aims to 1) evaluate the impact of CommunityRx on health care utilization, cost,
      health, and patient-centered outcomes for program participants compared to controls; 2)
      examine the flow and spread of information to and through primary agents including: program
      participants, community health information experts, healthcare providers, and community-based
      service providers (businesses and organizations providing self-care resources); and 3) build
      and use an agent-based model to test the distributed impact, including economic effects, of
      CommunityRx system adoption on the demonstration area and predict performance over time by
      conducting experiments that vary assumptions about agent, environment, and population-level
      characteristics.
    

Detailed description: 
      CommunityRx connects health care to self-care. CommunityRx begins with the medical encounter
      and functions like an e-prescribing system, modeled after medication e-prescribing.9 Rather
      than sending a medication prescription to the patient's pharmacy, a HealtheRx "prescription"
      is automatically generated at the point of care, delivered to the patient in paper (or
      electronic) form and stored in the Electronic Medical Record (EMR) for future reference. The
      HealtheRx is ontologically-generated, driven by standard demographic (eg. age, gender,
      address, language) data fields in the EMR and customized to patient problems/diagnoses like
      "homelessness," or "obesity." Like e-prescribing medicine, CommunityRx involves prescribing,
      fulfillment (patient signs up for a Weight Watchers class), and administration (patient
      participates in the weight loss program) (Fig. 2).10 The HealtheRx prescription is the
      primary information agent in the system, driven by simple ontologic rules that generate
      complexity, including iteration of the CommunityRx system itself, as human agents interact
      with the self-care information.

      CommunityRx can be best understood as part of a complex adaptive system. The HealtheRx, in
      contrast to a drug prescription, is designed specifically to enhance the interpersonal aspect
      of the patient-physician encounter, informing both agents about self-care resources. A
      patient receives a HealtheRx and then contacts a Community Health Information Specialist
      (CHIS) or not. She seeks self-care resources or not.

      Data captured by the CommunityRx system about patient referrals and needs are distributed to
      community-based service providers (CBSPs) in the form of quarterly reports delivered to CBSP
      contacts cultivated by CHIS. These data efficiently reveal to CBSPs real-time needs and gaps
      in self-care resources. The investigators hypothesize these data will be used by CBSPs over
      time to ensure supply. Youth who generate community resource data through employment with
      MAPSCorps also gain insight to community resources and engage with CBSP personnel and CHIS as
      they gather their data. Youth spread information to their networks about community resources
      and increase their use of these resources. MAPSCorps data about community resources are
      shared publicly (www.healtherx.org, www.southsidehealth.org, www.dondeesta.org). Self-care
      becomes more efficient. Supply meets demand. Patients and providers have more time and
      resources to devote to other salubrious activities and CBSPs become stronger. Over time,
      transparency in the market for self-care resources increases competition and quality.

      This hypothetical dynamic is an example of emergent self-organization from a complex adaptive
      system. The intervention starts with a simple encounter, governed by simple rules, between
      patient and health care provider. As the number of these encounters grow, previously siloed
      sectors - health care and self-care - evolve a new kind of formation that is far more
      efficient for the community than the current state. CommunityRx drives this new formation
      through multiple agents who are unaware of the complexity they are producing: "…the
      self-organized structure simply emerges as a result of each individual doing their own thing,
      independently." In evaluating the economic effects of CommunityRx adoption, agent-based
      modeling (ABM) can test how close the attractor state, or end-point in a CommunityRx system
      configuration (set of assumed behaviors and designed interventions), comes to a Pareto
      efficient point of equilibrium. The effects of the CommunityRx intervention are non-linear,
      involve interactions and feedback loops, and therefore require a complex system modeling
      approach for evaluation.

      A. Purpose or Hypothesis

      Data inputs for agent based modeling (ABM) can come from a range of sources, including
      empirical quantitative and qualitative data, data from the literature, and expert opinion.
      Because CommunityRx targets people of all ages (0-99 yrs, to date), a prospective,
      experimental, community-based design (eg. RCT) to assess outcomes by age strata would be very
      informative about patient agents (the investigators expect age-group differences in behavior,
      social networks, and outcomes) but cost-prohibitive. ABM can accommodate assumptions made
      based on this important, but specific, population subgroup (or "testbed"), includes many
      agents, and allows for multiple simulations to assess the impact of variations in those
      assumptions for the much larger and more diverse population that the system-wide model
      includes. A cost-effectiveness analysis is needed to understand the true economic impact of
      CommunityRx on the total cost of the burden of disease. In addition, the research team brings
      clinical and research expertise and specialized interest in middle-age and older adult
      populations with chronic disease. Focusing on this subgroup builds on this track record and
      will meaningfully extend our contributions to the gerontology and geriatrics fields.

      Specifically, the aims (and associated hypotheses) of this research include:

      Aim 1. Evaluate the impact of CommunityRx on health care utilization, cost, health, and
      patient-centered outcomes for program participants (patients who receive care at the clinical
      demonstration sites and live in an 16 zip code area) compared to controls (patients who
      receive health care at the demonstration sites, but live outside the 16 zip code area), with
      a special focus on middle-age and older adults.

      Aim 1a. Evaluate the impact of CommunityRx on health care utilization, health care costs, and
      on health outcomes for program participants (patients who receive care at the clinical
      demonstration sites and live in an 16 zip code area) compared to controls (patients who
      receive health care at the demonstration sites, but live outside the 11 zip code area) of all
      ages. NOTE: This aim is funded separately and registered on clinicaltrials.gov separately
      (see ID 1C1CMS330997).

      Hypothesis: CommunityRx will decrease emergency/inpatient care utilization, decrease percent
      per beneficiary per year (%PBPY) costs and improve health.

      Aim 1b. Evaluate the impact of the CommunityRx system on patient-centered outcomes in a
      randomized control trail of 200 program participants ages 45-74 and compared to 200 controls.

      Hypothesis: CommunityRx will be associated with clinically meaningful improvements in: a)
      self-care behavior; b) perceived care quality; and c) quality of life.

      Aim 1c. Characterize the economic value of care augmented with the CommunityRx system
      compared to usual care, based on the prospective participant-control study described in Aim
      1b.

      Hypothesis: Compared to usual care, care augmented with the CommunityRx system will be as
      cost-effective as commonly accepted medical devices and treatments.

      Aim 2. Examine the flow and spread of information to and through primary agents including:
      program participants, community health information specialists, healthcare providers, and
      community-based service providers (businesses and organizations providing self-care
      resources).

      Hypotheses: 1) Among the CBSPs receiving high volumes of CommunityRx referrals for people ≥45
      years old (>1000/year), CommunityRx will produce a self-reported increase in: a) knowledge of
      community resources especially for older adults, b) referrals to other CBSPs, c) older client
      volume, and d) aging-related goods/services /programs inventory; and 2) Delivery of the
      CommunityRx intervention at the point of medical care produces knowledge about self-care
      resources in the community that spreads to secondary agents including members of patient and
      provider social networks.

      Aim 3. Build and use an agent-based model to test the distributed impact, including economic
      effects, of CommunityRx adoption on the demonstration area and predict performance over time
      by conducting experiments that vary assumptions about agent, environment, and
      population-level characteristics.

      Hypotheses: 1) The system-level value of CommunityRx is greater than the value quantified as
      %PBPY health care utilization savings and is projected to increase with population aging; 2)
      Experiments run on a systems-based model will predict and quantify the impact of strategies
      to optimize CommunityRx performance for improvements, sustainability, and spread to other
      settings; and 3) Systems-Based Modeling is an effective and efficient tool for large-scale
      evaluation of a health information technology-based intervention to improve health and health
      care.
    

Intervention name(s): HealtheRx
Intervention type(s): Other
Intervention description(s): The HealtheRx is an informational intervention. The HealtheRx is generated and administered at the point of care. It includes a list of community resources, tailored to a patient's needs based on diagnoses, that are located near the patient's home. A health care provider and/or administrative staff administers and reviews the HealtheRx with the patient.
Allocation: Non-Randomized
Intervention model: Parallel Assignment
Masking: None (Open Label)
Primary purpose: Other
Keywords: agent-based modeling; community resources; simulation modeling; information intervention; clinic-to-community linkages