A Conceptual Framework for Addressing Social Needs Through the Accountable Health Communities Model

December 9, 2020
Linda Highfield, PhD

,
Gayla Ferguson, MPH

,
Ryan Walsh, MD

,
Carol Paret, MPH

,
Deborah Ganelin, BS

,
Kevin O. Hwang, MD, MPH

,
Robert Morgan, PhD

The American Journal of Accountable Care, December 2020, Volume 8, Issue 4

No published studies exist on use of intervention design, implementation strategies, and theory in the CMS Accountable Health Communities Model, the largest-scale test of social prescribing in the United States.

ABSTRACT

Objectives: To present a conceptual framework to address social needs through accountable health programs in the United States. We present our implementation of the Accountable Health Communities (AHC) Model as an example.

Study Design: Conceptual framework and case study.

Methods: Our conceptual framework of the AHC Model is adapted from the UK Rainbow Model of Integrated Care for the US health care context based on current literature. Our approach is further underpinned by Medical Research Council guidance on intervention development stages, recent framework development in intervention coproduction, and quality improvement methods.

Results: Our team used the adapted framework coupled with Medical Resource Council stages for intervention development to create a program scope and sequence. Standard operating procedures and an implementation plan were created and approved by the Center for Medicare and Medicaid Innovation. Implementation was successfully launched at 13 clinical delivery sites and completed screenings of more than 10,000 patients have been done to date.

Conclusions: Our conceptual framework, which we are applying as a bridge organization in the AHC Model, can serve as a model that organizations can use to successfully design interventions to address social needs in clinical care settings in the United States. Further application and testing of the framework are warranted to advance understanding of social needs interventions in the United States.

Am J Accountable Care. 2020;8(4):15-23

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Recognition of the driving role of social determinants in health outcomes, coupled with the untenable cost curve of US health care, has led to calls for action in the health care system. Major professional groups1-5 have challenged health systems to assess and address social determinants of health (SDOH) in a variety of ways. This has led to a plethora of activity across the US health system, including recent efforts to delineate between upstream SDOH interventions and midstream social needs interventions.6-9 Recent efforts driven through health system reform have focused primarily on social needs. These are individual-level factors, such as housing instability, that can be assessed and addressed within a specific patient interaction (visit) or as part of a patient panel, as opposed to a broader whole-population or community effort to address upstream SDOH.8 Although interventions addressing social needs are new in the United States, the United Kingdom has been engaged in similar social prescribing innovations for several years and offers the opportunity to inform practice development in the US context.

Although there is no standard definition, the concept focuses on expanding the role of health care (typically primary care) to include factors outside medicine such as social needs.10-12 Programs implemented to date generally have included 3 components: (1) referral from a health care professional related to an identified social need, (2) consultation with a link worker, such as a community health worker or patient navigator, to set goals and action plans, and (3) use of local community organizations and programs to address identified needs.12-15 These align with programs to address social needs that are gaining traction in the United States. The CMS Accountable Health Communities (AHC) Model is one social needs intervention that is currently undergoing testing and has similarities to social prescribing programs in the United Kingdom. The AHC Model focuses on screening beneficiaries in 5 domains: housing instability, difficulty paying utility bills, food insecurity, transportation, and interpersonal violence. Screening is coupled with referral to community resources and patient navigation (typically through a link worker).16 A bridge organization serves as the “hub” to coordinate activities.

The AHC Model is designed to be flexible and allows bridge organizations to use theories, conceptual frameworks, and strategies best suited to their clinical delivery system and community needs.16 The literature published to date on accountable health–type programs and social needs screening in the United States has discussed some factors that influence implementation, such as readiness for change, organizational factors, culture or climate, capacity, and use of implementation strategies (eg, facilitation). However, a number of gaps remain in the evidence base, including a lack of specification on how authors have designed specific social needs interventions and chosen implementation strategies and whether theory or frameworks were used in these processes.17 In other words, the AHC Model and social needs intervention movement nationally are very much an opportunity for learning. The current state of the field is further complicated by the lack of integrated health services research and dissemination and by the lack of implementation sciences frameworks to guide the development of interventions that require both intra- and interorganizational change.18 Our objective with this paper is to propose a conceptual framework for social prescribing tailored to the US context and to share our application of the framework as a bridge organization participating in the CMS AHC Model.

Conceptual Framework

The overarching goals of social needs interventions align with the Triple Aim and include improved connections of patients to community services, reduced health care utilization, reduced health care costs, and improved health outcomes. We hypothesize that adoption and implementation of social needs interventions in health care systems is similar to adoption of any innovation and is influenced by characteristics ranging from the individual to environmental. Using the 2016 version of the Rainbow Model of Integrated Care (RMIC) conceptual framework,19-21 we present a conceptual framework. The RMIC was originally developed by Valentin et al in 2013 to combine integrated care and primary care, with particular attention to the multicomponent, complex nature of integrating social and health care interventions, including social prescribing.19 The model was expanded in 2016 to include the Triple Aim outcomes of cost and utilization, population health, and care experience.21 Related studies have conducted international expert consensus panels to determine a taxonomy of constructs for the framework.20 The framework is premised on population and patient-focused care, which includes personal preferences, values, and medical, psychological, and social needs of individuals, and on service delivery concerns that are inclusive of health, economic, social, and environmental needs and promote health equity and well-being.19

Macro-level integration includes alignment of inter­organizational frameworks and policy decisions that aid the delivery of integrated care to populations.19-21 Determinants of system integration that apply to social prescribing interventions include resources, engagement of stakeholders, and the political, economic, and social climate in which integration is taking place.20

Meso-level integration includes organizational and professional integration. Determinants of organizational integration can include culture, language, backgrounds, bureaucratic structures, professional roles and responsibilities, organizational visions, and regulations among social and health organizations.22,23 Professional integration focuses on inter- and intraorganizational relationships.19 Determinants for professional integration can include trust; shared understanding of roles, responsibilities, ethics, and common language; and respect and communication.22,24,25

Micro-level integration includes clinical integration. This refers to the extent to which services are effectively coordinated.19 Determinants of clinical integration can include continuity of care, quality of interaction between the professional and patient, use of care plans, and resources such as personnel and competing demands.

The framework further delineates functional and normative integration as key to success. Functional integration is the coordination of key support functions and activities—for example, financial and information management, strategic planning, human resources, communication, support systems, regular feedback, and quality improvement.19,21 Normative integration refers to the development and maintenance of a common frame of reference among organizations, professional groups, and individuals.19 Key determinants of normative integration are trust, visionary leadership, conflict management, and perceived need and purpose to collaborate.20 Normative integration is important to facilitate integration at all levels and to achieve a sustainable intervention aimed toward achieving the Triple Aim outcomes.19-21 Previous research has found that normative integration is a critical enabler to the implementation of integrated care.19-21

Proposed Adapted Conceptual Framework for Social Needs (Social Prescribing) Interventions in the United States

We propose the addition of 3 categories to the RMIC model as shown in Figure 120,26-39: intervention characteristics, hub organization characteristics, and community integration; these would increase alignment with the US health care and social services delivery systems and current implementations of social needs programs. We also suggest several determinants to be considered and specified by authors under each integration level of the RMIC as they relate to social needs intervention implementation as shown in Figure 1.20,26-39 References for selection of factors in each category are noted in that figure.20,26-39

Case Study: Applying the Social Needs Conceptual Framework and Medical Research Council Guidance to Designing Our AHC Model

The eAppendix Figure20,26-39 (available at ajmc.com) identifies the selected factors from the framework that we are targeting in our implementation (shown in white). Our research team chose factors that we believed to be modifiable, and we identified those which were not readily modifiable but could moderate the impact of our implementation, as shown in red in the eAppendix Figure. We selected the constructs for planning our implementation based on literature review and previous research.40,41

Our implementation plan for the AHC Model was further developed using the Medical Research Council (MRC) guidance on intervention development and quality improvement methods, as shown in Figure 2.42,43 We operationalized our framework using the MRC process stages on intervention development: (1) evidence review and stakeholder consultation, (2) coproduction, and (3) prototyping. We used quality improvement cycles to help us improve readiness to implement and also to address trialability and adaptability of the intervention in each clinical delivery setting at the preimplementation phase. Following prototyping, we created a final intervention scope and sequence (Table).

Stage 1: Evidence Review and Stakeholder Consultation

As shown in the Table, we began with a series of meetings with all levels of the clinical delivery site (CDS) and implementers to promote normative integration by garnering buy-in, support, and a sense of shared purpose and vision for the AHC Model in our community. We focused on functional integration by understanding the CDS’ current workflows and any potential constraints we needed to solve as a leadership team for successful AHC Model implementation. We conducted site visits and developed process maps of patient flows and workflows for intervention development as a team. At the intervention level, we discussed the trialability and relative advantage of implementing the AHC Model with executive leadership through frontline staff in a series of meetings to discuss how the program would be operationalized. We identified opinion leaders and a program champion at each CDS. A series of meetings were held with information technology leadership and staff to inform them about the AHC Model and to create and prioritize data transfer protocols among organizations. Meetings were held with link workers to discuss patient interactions with a focus on social needs, present the AHC Model, discuss intervention strategies for patient navigation, and garner their input and buy-in for using a theory-based navigation script. At the management level, we focused on both normative and functional integration through meetings held with CDS leadership to ensure executive-level engagement and support and to assign roles and responsibilities for program staff. The input of CDS leadership was sought on the approach to navigation, alignment with broader organizational goals, and brainstorming potential barriers and facilitators to implementation.

Stage 2: Coproduction

We then moved to development of standard operating procedures (SOPs). Coproduction was used to create the project SOPs. The bridge organization first created draft SOPs based on our in-person meetings and process maps using the prespecified Center for Medicare and Medicaid Innovation (CMMI) format. These were presented to our clinical delivery partners in an in-person meeting in which the collective team reviewed the content and edited the documents in real time. The resulting SOP draft was then emailed to CDS leadership and staff for final edits and approval.

Stage 3: Prototyping

The SOPs were prototyped and tested in each CDS prior to development of our AHC Model training protocols. Bridge organization leadership and the AHC Model program manager met CDS managers and staff on each unit to pilot-test our procedures. We visited a minimum of 4 patients in each unit to complete screening, referral, and navigation processes using the SOPs. After each patient, the team debriefed about the process and identified any steps that needed additional review or changes. Once final processes were agreed upon, staff were provided formal training for AHC Model implementation. Training was done on multiple levels: first in a structured didactic environment and then with hands-on training conducted in each CDS. Hands-on training consisted of embedding the academic team in the CDS and having the members work alongside the staff (screening, referring, navigating) until they expressed readiness to start program components. Structured debriefings took place during the hands-on training. Once staff began implementation, we moved to quality improvement and assurance. A linkage system with mirrored operational teams housed at the academic site was set up to provide ongoing technical support and assistance and to continue to foster the conditions for successful implementation through expert facilitation. Monthly meetings were scheduled with CDS leadership and staff to support implementation. Meeting agendas were cocreated by the CDS and bridge organization.

RESULTS

Stage 1: Evidence Review and Stakeholder Consultation

Our approach to AHC Model evidence review and stakeholder consultation resulted in the creation of process maps detailing current patient encounters in each CDS. Based on our meetings, all 13 CDSs expressed readiness to implement the AHC Model. All sites continue to successfully implement the model. To support implementation and based on our meetings, the bridge organization embedded behavior change and theory into our navigation protocols. We scripted these for staff using the transtheoretical model of change.40,41 Clinical integration focused on creating secure data transmission protocols using HL7 messages or flat files from each CDS to our secure CMS Certified Data Center. Our software was created to allow staff across all locations to log in and complete all 3 processes (screening, referral, and navigation) in a seamless fashion. We used funds from the cooperative agreement to support packaging the intervention through the creation of a software platform and development of training protocols for implementation. At the professional level, we discussed each CDS’ philosophy of delivering care so we could tailor scripts for their patients and staff based on this. For example, one CDS focuses on wellness as a core tenet. We embedded the use of the term throughout our introductory scripting and in the text provided to patients in the community referral summary. We used standard language for staff across the CDSs when discussing the AHC Model and provided definitions for key model terms.

Stage 2: Coproduction

Coproduced SOPs were successfully created for all 13 CDSs. Our process of creating SOPs crossed functional and normative integration levels by incorporating operational factors and focusing on collaborative creation of an implementation approach. As we moved toward implementation, we evaluated, adapted, or created any necessary data systems and data transmission protocols using human-centered design approaches to ensure fit with the individual implementers and to support overall program uptake, coordination, and maintenance.

Stage 3: Prototyping

The prototyping step allowed our team to identify missing components needed for successful implementation. For example, we discovered that even clinical staff needed a scripted introduction to use when introducing the AHC Model screening tool to patients. During the prototyping step, we were able to try different scripts and develop tailored language for each CDS. We also identified that use of laptops was impractical in the clinical delivery environment and switched to tablets in rolling kiosks to improve ease of use. By switching to rolling kiosks, we were able to streamline the AHC Model workflow for staff members during the screening. The kiosks housed the screening tablet as well as a printer to instantly distribute program material. The kiosk also housed hospital-grade cleaning wipes. Staff members no longer needed to return to patients’ rooms after retrieving documents from a stationary workstation printer as would have occurred with the use of laptops. The kiosks also allowed for an additional level of privacy for participants completing the screening with the addition of a 4-way privacy screen. With the privacy screen in place, only the participant completing the screening tool could see the questions and responses, so clinical staff could confidently let patients self-complete the screening questions. Functional integration, including identification of where to store kiosks and supplies, was done in the prototyping step, as this practical need was not raised by any stakeholders in previous interactions. Thirty-two staff members successfully completed training and expressed readiness to implement. Following training, staff achieved an average 57.1% survey completion rate.

Final Scope and Sequence for AHC Model Implementation

As shown in the Table, our team created the final scope and sequence for developing our AHC Model implementation.

CONCLUSIONS

The adapted RMIC conceptual framework was successfully applied across 13 CDSs including emergency departments (EDs), labor and delivery departments, and ambulatory clinics. Previous studies have shown the importance of stakeholder involvement in program design.42 We found that the MRC stages were easy to apply and fit well with our pragmatic orientation to intervention development. Extending the MRC stages to include implementation through the use of quality improvement cycles and ongoing technical assistance and support for CDSs has been helpful to support implementation of the model. To date, our collaborative team has successfully completed screening on more than 10,000 patients across these locations, and 43% of patients screened were found to be high risk (positive for a need and had been in the ED 2 or more times in the past 12 months) (n = 4300). The needs reported with the highest frequency by all patients were food insecurity (40%), housing instability (30%), and transportation (28%). The primary outcome measures of focus for the AHC Model currently are health care utilization (ED) and health care costs, both of which will be evaluated post model by CMMI and the contracted evaluation organizations using patient claims data (beyond 2022). We are unable to provide details on the evaluation plan for the AHC Model given that our role as a bridge organization is focused on our implementation. That said, our perspective as a bridge organization is that navigation and the creation of new navigation cases represent the hypothesized mechanism through which these outcomes will be changed, and related data may serve as useful indicators for the model’s implementation. Each bridge organization is expected to create new navigation cases for 2048 patients annually. To date, our team has achieved approximately 95% of the goal across all sites. Navigation cases created do vary by site, with ambulatory sites having fewer high-risk patients and generating fewer navigation cases than ED locations. The data system created for this project allows for the generation of reports showing the number of navigation cases created by each site at custom time periods, which allows us to monitor and adjust staffing at regular intervals. Of patients deemed eligible to receive navigation, 96% agreed to receive the service (opt in) and 92% were successfully contacted by our navigators to create a patient action plan and for ongoing follow-up. The team held weekly staff and monthly leadership meetings to discuss model metrics (specifically navigation) and barriers and facilitators of implementation related to model constructs that could affect success, including self-efficacy, communication, outcome expectations, and competing demands. During staff training, we created curricula to focus on building self-efficacy and communication skills along with targeting perceptions of intervention constructs (eg, adaptability, complexity, trialability). Training also focuses on constructs at the integration levels including buy-in, communication, readiness for change, and organizational support. To date, 92% of staff reported feeling confident to implement the model following training.

Implementation using this model was not a quick process. As with any endeavor aiming to build integration among organizations, finding time to consistently come together to collaborate was a challenge. To ensure participation, meetings had to be scheduled far enough in advance to accommodate busy schedules. Navigating the mandatory policy and procedures of multiple bureaucratic organizations delayed the speed at which decisions were ratified. The differences in the types of organizations also led to different and sometimes competing outcome expectations, goals, and priorities. We addressed the issues of navigating organizational polices, outcome expectations, goals, and priorities through transparency and the sharing of information using community-based participatory research principles. We adapted project tools to meet organizations’ concerns and changing needs wherever possible, treating our approach as quality improvement in all facets. These accommodations directly affected the perception of our partners’ packaging, adaptability, and trialability of the intervention. We scheduled regular check-ins with staff members to continually build their self-efficacy. We devised advanced organizers and reports to provide managers with additional support while tracking their staff’s activities. These organizers reminded managers of critical implementation functions that needed to be routinely monitored, and the reports offered an avenue for progress to be checked. We also reframed retraining sessions as opportunities to address the fit of the current SOPs. Lastly, personnel turnover is a challenge, as buy-in, trust, and the transfer of knowledge were necessary for successful implementation. Packaging of trainings (eg, videos, online) could serve as a tool to reduce training burden for future organizations. Future studies implementing and testing the model constructs proposed herein would further the field of social needs interventions in the United States. Application of the MRC framework in the United States also warrants further application and testing.

Author Affiliations: Department of Management, Policy and Community Health Practice, UTHealth School of Public Health (LH, GF, RM), Houston, TX; Department of Internal Medicine, McGovern Medical School at UTHealth (LH, KOH), Houston, TX; Ambulatory and Population Health (RW) and Community Benefit Corporation (CP, DG), Memorial Hermann Health System, Houston, TX.

Source of Funding: This publication was supported by the Centers for Medicare and Medicaid Services (CMS) of the US Department of Health and Human Services (HHS) as part of a financial assistance award totaling $529,632 with 100% funded by CMS/HHS and $0 amount and 0% funded by nongovernment source(s). The contents are those of the author(s) and do not necessarily represent the official views of nor an endorsement by CMS/HHS or the US government.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (LH, RW, KOH, RM); acquisition of data (LH, GF, RW); analysis and interpretation of data (LH, RM); drafting of the manuscript (LH, GF, KOH, RM); critical revision of the manuscript for important intellectual content (LH, GF, KOH, RM); statistical analysis (LH); provision of study materials or patients (LH, CP, DG); obtaining funding (LH, RW, RM); administrative, technical, or logistic support (LH, RW, CP, DG, RM); and supervision (LH, CP, DG).

Send Correspondence to: Linda Highfield, PhD, UTHealth School of Public Health, 1200 Pressler St, RAS Bldg E909, Houston, TX 77030. Email: Linda.d.highfield@uth.tmc.edu.


REFERENCES

1. Hughes LS, Likumahuwa-Ackman S. Acting on social determinants of health: a primer for family physicians. Am Fam Physician. 2017;95(11):695-696.

2. Council on Community Pediatrics. Poverty and child health in the United States. Pediatrics. 2016;137(4):e20160339. doi:10.1542/peds.2016-0339

3. Daniel H, Bornstein SS, Kane GC; Health and Public Policy Committee of the American College of Physicians. Addressing social determinants to improve patient care and promote health equity: an American College of Physicians position paper. Ann Intern Med. 2018;168(8):577-578. doi:10.7326/M17-2441

4. AHRQ challenge seeks innovative presentations of community-level information on social determinants of health. News release. Agency for Healthcare Research and Quality; March 7, 2019. Accessed October 1, 2019. https://www.ahrq.gov/news/newsroom/press-releases/ahrq-challenge-social-determinants-health.html

5. Institute of Medicine. Capturing Social and Behavioral Domains in Electronic Health Records: Phase 1. The National Academies Press; 2014.

6. Wilkinson GW, Sager A, Selig S, et al. No equity, no Triple Aim: strategic proposals to advance health equity in a volatile policy environment. Am J Public Health. 2017;107(S3):S223-S228. doi:10.2105/AJPH.2017.304000

7. Lantz PM. The medicalization of population health: who will stay upstream? Milbank Q. 2019;97(1):36-39. doi:10.1111/1468-0009.12363

8. Castrucci BC, Auerbach J. Meeting individual social needs falls short of addressing social determinants of health. Health Affairs. January 16, 2019. Accessed November 5, 2019. https://www.healthaffairs.org/do/10.1377/hblog20190115.234942/full/

9. Marmot M, Friel S, Bell R, Houweling TAJ, Taylor S; Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health. Lancet. 2008;372(9650):1661-1669. doi:10.1016/S0140-6736(08)61690-6

10. Carnes D, Sohanpal R, Frostick C, et al. The impact of a social prescribing service on patients in primary care: a mixed methods evaluation. BMC Health Serv Res. 2017;17(1):835. doi:10.1186/s12913-017-2778-y

11. Husk K, Blockley K, Lovell R, et al. What approaches to social prescribing work, for whom, and in what circumstances? a realist review. Health Soc Care Community. 2020;28(2):309-324. doi:10.1111/hsc.12839

12. Brandling J, House W. Social prescribing in general practice: adding meaning to medicine. Br J Gen Pract. 2009;59(563):454-456. doi:10.3399/bjgp09X421085

13. Friedli L, Watson S. Social Prescribing for Mental Health. Northern Centre for Mental Health; 2004.

14. Friedli L, Jackson C, Abernethy H, Stansfield J. Social Prescribing for Mental Health: A Guide to Commissioning and Delivery. Care Services Improvement Partnership; 2008.

15. South J, Higgins TJ, Woodall J, White SM. Can social prescribing provide the missing link? Prim Health Care Res Dev. 2008;9(4):310-318. doi:10.1017/S146342360800087X

16. Alley DE, Asomugha CN, Conway PH, Sanghavi DM. Accountable Health Communities—addressing social needs through Medicare and Medicaid. N Engl J Med. 2016;374(1):8-11. doi:10.1056/NEJMp1512532

17. Gottlieb L, Cottrell EK, Park B, Clark KD, Gold R, Fichtenberg C. Advancing social prescribing with implementation science. J Am Board Fam Med. 2018;31(3):315-321. doi:10.3122/jabfm.2018.03.170249

18. Allen JD, Towne SD Jr, Maxwell AE, et al. Measures of organizational characteristics associated with adoption and/or implementation of innovations: a systematic review. BMC Health Serv Res. 2017;17(1):591. doi:10.1186/s12913-017-2459-x

19. Valentijn PP, Schepman SM, Opheij W, Bruijnzeels MA. Understanding integrated care: a comprehensive conceptual framework based on the integrative functions of primary care. Int J Integr Care. 2013;13:e010. doi:10.5334/ijic.886

20. Valentijn PP, Vrijhoef HJM, Ruwaard D, Boesveld I, Arends RY, Bruijnzeels MA. Towards an international taxonomy of integrated primary care: a Delphi consensus approach. BMC Fam Pract. 2015;16:64. doi:10.1186/s12875-015-0278-x

21. Valentijn PP, Biermann C, Bruijnzeels MA. Value-based integrated (renal) care: setting a development agenda for research and implementation strategies. BMC Health Serv Res. 2016;16:330. doi:10.1186/s12913-016-1586-0

22. RAND Europe. National evaluation of the Department of Health’s integrated care pilots. RAND Corporation. March 2012. Accessed March 10, 2020. https://www.rand.org/content/dam/rand/pubs/technical_reports/2012/RAND_TR1164.pdf

23. Cameron A, Lart R, Bostock L, Coomber C. Factors that promote and hinder joint and integrated working between health and social care services: a review of research literature. Health Soc Care Community. 2014;22(3):225-233. doi:10.1111/hsc.12057

24. Mackie S, Darvill A. Factors enabling implementation of integrated health and social care: a systematic review. Br J Community Nurs. 2016;21(2):82-87. doi:10.12968/bjcn.2016.21.2.82

25. White JM, Cornish F, Kerr S. Front-line perspectives on “joined-up” working relationships: a qualitative study of social prescribing in the west of Scotland. Health Soc Care Community. 2017;25(1):194-203. doi:10.1111/hsc.12290

26. Plsek PE, Greenhalgh T. The challenge of complexity in health care. BMJ. 2001;323(7313):625-628. doi:10.1136/bmj.323.7313.625

27. Denis JL, Hébert Y, Langley A, Lozeau D, Trottier LH. Explaining diffusion patterns for complex health care innovations. Health Care Manage Rev. 2002;27(3):60-73. doi:10.1097/00004010-200207000-00007

28. Coker RJ, Atun R, McKee M. Untangling Gordian knots: improving tuberculosis control through the development of “programme theories.” Int J Health Plann Manage. 2004;19(3):217-226. doi:10.1002/hpm.759

29. Atun RA, Kyratsis I, Jelic G, Rados-Malicbegovic D, Gurol-Urganci I. Diffusion of complex health innovations—implementation of primary health care reforms in Bosnia and Herzegovina. Health Policy Plan. 2007;22(1):28-39. doi:10.1093/heapol/czl031

30. Ross J, Stevenson F, Lau R, Murray E. Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update). Implement Sci. 2016;11(1):146. doi:10.1186/s13012-016-0510-7

31. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50. doi:10.1186/1748-5908-4-50

32. Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. 2010;12(1):e4. doi:10.2196/jmir.1376

33. Bickerdike L, Booth A, Wilson PM, Farley K, Wright K. Social prescribing: less rhetoric and more reality. a systematic review of the evidence. BMJ Open. 2017;7(4):e013384. doi:10.1136/bmjopen-2016-013384

34. Lau R, Stevenson F, Ong BN, et al. Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews. Implement Sci. 2016;11:40. doi:10.1186/s13012-016-0396-4

35. Tierney S, Wong G, Roberts N, et al. Supporting social prescribing in primary care by linking people to local assets: a realist review. BMC Med. 2020;18(1):49. doi:10.1186/s12916-020-1510-7

36. Gruß I, Bunce A, Davis J, Dambrun K, Cottrell E, Gold R. Initiating and implementing social determinants of health data collection in community health centers. Popul Health Manag. Published online March 2, 2020. doi:10.1089/pop.2019.0205

37. Perry CK, Damschroder LJ, Hemler JR, Woodson TT, Ono SS, Cohen DJ. Specifying and comparing implementation strategies across seven large implementation interventions: a practical application of theory. Implement Sci. 2019;14(1):32. doi:10.1186/s13012-019-0876-4

38. Mongeon M, Levi J, Heinrich J. Elements of accountable communities for health: a review of the literature. National Academy of Medicine. November 6, 2017. Accessed May 15, 2020. https://nam.edu/elements-of-accountable-communities-for-health-a-review-of-the-literature/

39. Siegel B, Erickson J, Milstein B, Pritchard KE. Multisector partnerships need further development to fulfill aspirations for transforming regional health and well-being. Health Aff (Millwood). 2018;37(1):30-37. doi:10.1377/hlthaff.2017.1118

40. Highfield L, Rajan SS, Valerio MA, Walton G, Fernandez ME, Bartholomew LK. A non-randomized controlled stepped wedge trial to evaluate the effectiveness of a multi-level mammography intervention in improving appointment adherence in underserved women. Implement Sci. 2015;10:143. doi:10.1186/s13012-015-0334-x

41. Highfield L, Valerio MA, Fernandez ME, Eldridge-Bartholomew LK. Development of an implementation intervention using intervention mapping to increase mammography among low income women. Front Public Health. 2018;6:300. doi:10.3389/fpubh.2018.00300

42. Hawkins J, Madden K, Fletcher A, et al. Development of a framework for the co-production and prototyping of public health interventions. BMC Public Health. 2017;17(1):689. doi:10.1186/s12889-017-4695-8

43. Moore GF, Evans RE, Hawkins J, et al. From complex social interventions to interventions in complex social systems: future directions and unresolved questions for intervention development and evaluation. Evaluation (Lond). 2019;25(1):23-45. doi:10.1177/1356389018803219