Business Finance - Management APA assignment

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Leveraging Technology to Address SDOH Challenges

Marneze Davis

Walden University

DBAX 8006: Contemporary Challenges in Business

Dr. Ashley Reibel

January 11, 2026

Leveraging Technology to Address SDOH Challenges

Social Determinants of Health (SDOH) have created issues and can create barriers to limit resources in the daily life of many. Boston Medical Center (BMC) CEO Alastair Bell is challenged to create a safety net hospital in an urban setting of Boston, BMC can utilize disruptive technology in a manner that addresses the Social Determinants of Health (SDOH) factors so crucial to its population, while at the same time driving financial sustainability at the organization (Craig et al., 2021). This report will define SDOH, discuss SDOH in conjunction with technology in a business setting, determine which SDOH factors are crucial to an organization like BMC, and offer a plan on how disruptive technology can be factored into a viable business model.

Background of the Problem

BMC is ensconced in an intimate healthcare ecosystem, treating only part of its patients’ healthcare concerns. Most of their patients derive from a population affected by deep-rooted socioeconomic adversities like poverty, homelessness, lack of access to nutritious food, and lack of transportation. These represent Social Determinants Of Health, namely: “the circumstances in which people are born, live, grow, and everything in between.” The social determinants influence health and healthcare, often resulting in adverse health outcomes, higher rates of chronic diseases, and subsequently unnecessary use of the Emergency Department, as well as Inpatient services (Gibbings & Wickramasinghe, 2021). BMC finds itself facing two challenges: its social mission to treat its underserved population, as well as its unsustainable revenue streams because of its acute social incentives’ costs.

Key Factors Supporting Recommendations

There are 4 important considerations identified for underlying the strategic application of disruptive technology. The first is a clear financial opportunity afforded by the movement towards value and Medicaid 1115 waivers. Second consideration is the application of leverage afforded by technology that can integrate many more patients simultaneously and automatically track data as opposed to individually tracking and addressing patients' social needs manually (Sensmeier, 2020). Third is the fundamental importance of engaging and empowering patients to manage their conditions on their own outside the four walls of BMC hospitals because mobile technology can undeniably engage and connect patients to support services and aids. Finally, there is a clear private and public imperative to address SDOH problems that cannot and should not be solved by BMC or other single entities; rather, there is a clear use and application for technology platforms to integrate various community organizations and services to form a single support ecosystem that BMC can leverage and manage (Abbott et al., 2024).

Recommendations

BMC undoubtedly should continue to explore the possibility of designing and deploying a well-integrated and patient-centric technology tool specifically made to evaluate, treat, and lessen SDOH impact. This tool will function as an integrated channel. It will be composed of three essential revolutionary elements: To start there will be an AI-based SDOH screening and risk stratification mechanism placed right inside an electronic health record so that the algorithm can locate patients with social needs through the identification process (He et al., 2023). Then, there will be a very considerate, "closed-loop", patient relationship-building and engagement tool that is very logically connected, linking patients to verified social resources and, at the same time, evaluating the levels of patient engagement. Providing quality healthcare with and closing the gap in marginalized demographics is important.

Goal(s)

The aim is to minimize avoidable hospitalizations and visits to the emergency department for high-risk patients by 15% in the third year when their social needs are addressed in advance. Its secondary objectives are to improve patient scores for food security/availability and housing security/availability by 25%, patient engagement in preventive care by 20%, and the development of new revenue streams based on success in risk contracts (Craig et al., 2021).

Action Items

First, no more than six months into the project, form a cross-functional ‘Health Equity Technology’ team, with a team lead for each of the following areas: clinical, IT, community partnerships, and finance. Second, develop and test the AI-driven patient screener and referral tool in two patient populations with high needs: pediatric asthma sufferers and congestive heart failure patients in nine months of project development (Gibbings & Wickramasinghe, 2021). Third, develop and establish community partnerships with at least five core providers of community resources in the first year of development and include their offerings in the referral system. Fourth, develop the patient-facing app in the patient advisory council in 12 months.

Action Plans

The action plan will include vendor development or internal development of the platform’s core, based on guidance provided by the cross-functional team. Phase II of the pilot should include training members of the clinical staff in the new process, implementing the tool in the EHR, and determining success metrics. Developing partnerships will include guidance from the Community Partnerships team, who will manage all negotiations, development of agreements, and validation of resource availability (Sensmeier, 2020). The last action is the development of the mobile app will include agile development methodology with feedback from patients.

Metrics to Support Learning so That Continuous Improvement Is Possible

Continuous improvement will be made based on a known set of metrics. Process metrics will measure the percentage of patients screened who are identified as needing SDOH and the completion rates of these referrals. Outcomes will measure changes to the HbA1c values of diabetic patients or the number of hospital visits related to asthma symptoms compared to social service use. Cost metrics will measure the cost to care compared to the control group (Abbott et al., 2024). Patient outcome metrics will come from the app. Quarterly review of these metrics is required by the leadership group to improve the functionality of the app and provide a return on investment to continue to fund the project.

References

Abbott, E. E., Apakama, D., Richardson, L. D., Chan, L., & Nadkarni, G. N. (2024). Leveraging artificial intelligence and data science for integration of social determinants of health in emergency medicine: scoping review.  JMIR Medical Informatics,  12, e57124.

Craig, K. J. T., Fusco, N., Gunnarsdottir, T., Chamberland, L., Snowdon, J. L., & Kassler, W. J. (2021). Leveraging data and digital health technologies to assess and impact social determinants of health (SDoH): a state-of-the-art literature review—Online Journal of Public Health Informatics,  13(3), E14.

Gibbings, R., & Wickramasinghe, N. (2021). Social determinants of health in the US: A framework to support superior care coordination and leverage digital health solutions.  Health Policy and Technology,  10(2), 100523.

He, Z., Pfaff, E., Guo, S. J., Guo, Y., Wu, Y., Tao, C., ... & Bian, J. (2023). Enriching real-world data with social determinants of health for health outcomes and health equity: successes, challenges, and opportunities.  Yearbook of Medical Informatics,  32(01), 253-263.

Sensmeier, J. (2020). Achieving health equity through the use of information technology to address social determinants of health.  CIN: Computers, Informatics, Nursing,  38(3), 116–119.