Importance of Including Community Organization in Artificial Intelligence (AI) Healthcare Initiatives
Introduction
Artificial Intelligence has a limitless potential that can be utilized in diagnosis, patient care, and treatments. The changes brought by this artificial technology have already been termed unheralded, with some people even suggesting the need for legal interventions to regulate the advancements and a society ill-prepared to deal with overwhelming changes. For gains to be realized in healthcare through AI, community organizations have to be involved. This approach would support increased levels of trust and participation among diverse patient cohorts, contribute to better quality data, more equitable provision of care, and the design of sustainable and inclusive structures for health systems. Community organizations will become essential intermediaries in linking healthcare providers with the communities they serve to make AI-based solutions both effective and equitable. This paper captures real-life examples and ideas by scholars on how such community organizations should be included in AI initiatives within health care to build a better and fairer system.
Promoting Trust and People’s Willingness to Participate
Collaboration with community organizations remains critical in fostering the willingness and interest of the target population to influence trust in the care providers insisting on AI solutions. Well-established relationships between community organizations and residents are resourceful and can be used to make people embrace and believe in new healthcare projects (Yasmin et al., 2022). Community relations play a vital role in this process, mainly where institutions well known to the locals endorse the health projects, and the residents' perceptions are that they are part of the initiative. Community-based organizations often know about historical and ongoing problems that lead to people's distrust of healthcare systems. Such close working relationships allow the care providers to understand these concerns, find a way to appeal to the community and gain their trust without making the same mistakes as their predecessors.
When community organizations are part of the initiatives, it brings a sense of responsibility and empowerment among residents. This involvement makes them feel valued and in control (Yasmin et al., 2022). The collaborative approach implies that the voice of each involved party is important, which sets the foundation for building mutual trust and respect. Yasmin et al.'s (2022) study highlights real-world examples of partnerships in which community organizations have been shown to promote trust and success of initiatives. They illustrate how these partnerships use the robust CBO framework and relationships with local communities to respond quickly and effectively to community needs by building trust in the community. For example, close and mutually trusting partnerships between public health agencies and CBOs played an important role during COVID-19 by combatting vaccine hesitance and delivering pertinent, community-specific health information (Yasmin et al., 2022). Trust is crucial in implementing AI-driven strategies because people must be assured that their private health data will be securely stored without unauthorized access. When people believe that AI healthcare initiatives are trustworthy, they are encouraged to share authentic, accurate, and comprehensive information without holding back, which allows the tools to function effectively and optimally. Community-based organizations whose staff have long-standing relationships with local populations can help to overcome the challenges faced by healthcare providers and engage patients, influencing them to be truthful in survey responses.
Optimizing Data Collection and Quality
Collaborating with community organizations can also help overcome data collection challenges to ensure accuracy and optimal functioning of AI-based healthcare tools. Complete and accurate data is the foundation of high-performing AI systems. Unfortunately, data collection is riddled with some significant challenges in the form of language barriers and cultural misunderstandings (Al Shamsi et al., 2020). People may hold differing beliefs about health, illness, and treatment. For example, certain cultures heavily rely on traditional medicine and may suspect modern medical practices (Al Shamsi et al., 2020). Failure to factor in such culture-specific perspectives into data collection methodologies will also result in incomplete insights and bias. One must consider these unique issues and develop tailored, robust strategies to try and bring conservative community members to participate in modern, sophisticated healthcare initiatives. In addition, social-cultural aspects of communication influence some people's willingness to provide critical information to care providers during surveys. For example, mental health problems can be less reported in specific countries than in others because discussing mental health issues is considered taboo in some countries (National Library of Medicine, 2021). Community groups can address these problems by finding ways to communicate with patients in their own languages and using culturally appropriate techniques.
Schouten et al. (2020) highlight certain approaches to embrace in order to foster effective intercultural health communication. They propose replacing interpreters with healthcare providers who speak the same language and have the same culture as patients. Schouten et al. (2020) suggest that this approach could promote positive health outcomes due to linguistic and cultural concordance. They further propose that the activities of medical workers with backgrounds similar to their patients should be complemented by family members playing the role of informal interpreters in the mixed approach to break this language barrier. Professional interpreters ensure communication accuracy, while informal interpreters provide emotional support. The approach further engages the community in co-creation methods, which may eventually translate into interventions building on local realities. The strategy helps align the solutions to the cultural and linguistic needs of the community through this intersectional approach. Patients communicate more accurately and completely when they are understood and taken seriously—all these results in better AI model predictions and, ultimately, better patient outcomes as well. Therefore, community involvement in AI healthcare initiatives can go a long way in facilitating meaningful communication between care providers and survey participants or patients, which translates into high-quality data that can inform meaningful intervention. Failure to engage the locals can lead to misalignments because the providers may obtain incomplete or inaccurate information. This leads to misunderstanding of the community-specific problems and misguided solutions, rendering the AI technology unhelpful.
Promoting Health Equity
The collaborative approach can also help care providers avoid perpetuating persistent healthcare disparities and promote equity in new AI initiatives. Artificial intelligence in healthcare can inadvertently exacerbate health disparities if it is not planned and implemented thoughtfully. It is important to note that the effectiveness of these technologies is contingent on the quality of the data used to train the systems (Friend, 2023). AI is bound to disadvantage the underrepresented groups if the tools are primarily fed information from biased sources that lack diversity and do not reflect the general population. For example, suppose an AI system is trained predominantly with data from wealthy white communities. In that case, it may not categorize conditions effectively for minority communities, resulting in misdiagnosis or inapplicability of the tools for marginalized individuals (Friend, 2023). Involving community organizations in developing and implementing AI healthcare initiatives will ensure that the technologies are focused on the issues pertinent to these communities. Such organizations typically work on the ground in these communities and have a nuanced understanding of their specific needs and challenges (Crowder et al., 2022). These entities serve as avenues for amplifying the voices of historically marginalized populations. Therefore, community organizations can promote the representativeness of healthcare initiatives by providing vital information about their health-related needs to inform the algorithm better. The collaborative approach can go a long way in addressing the underlying factors causing poor health among marginalized groups, preventing the use of misguided generalized solutions that are based on overrepresented groups.
Promoting Sustainability and Inclusivity
There is a need for AI health programs to support the development of sustainable and inclusive healthcare through community organizations' engagement, helping address social needs like housing, education, and employment, ultimately influencing health outcomes. This inclusivity is also an opportunity for the providers to better understand the specific gaps and challenges a community struggles with. For example, living in overcrowded spaces, unsafe drinking water, poor sanitation, exposure to indoor air pollution, and inadequate control over home temperatures may collectively predispose residents to a wide range of health issues: respiratory, cardiovascular, and infectious diseases. Community organizations are aware of the local housing issues and are better placed to identify the people and families' needs (Crowder et al., 2022). Clinicians may request community organizations to help take in individuals and families who need the treatment.
In some cases, health outcomes cannot be addressed before education because they are somewhat interrelated. An individual with adequate education will be more health literate and hence practice healthy behavior. Community organizations in the education sector are likely to assist in pinpointing the community's problems and potentials concerning education. They collude quite well with health institutions to craft culturally and linguistically appropriate health education programs designed to enhance literacy relating to health and foster a balanced and healthier lifestyle. Employment, or rather what type of work one does, can also significantly influence health. Cumulatively, unemployment and job insecurity, in conjunction with poor-quality working conditions, are associated with stress, anxiety, and various physical health problems—factors that community-level advocacy organizations can be tasked with looking into more deeply with a focus on employment and workforce development. These can also contribute to programs for job availability, quality of jobs, working conditions, and ultimately better health outcomes.
Crowder et al. (2022) discuss the need for coalitions to work towards alleviating health inequities. They highlight partnerships like those of the Latinx Advocacy Team and Interdisciplinary Network for COVID-19 (LATIN-19), the Black Coalition against COVID (BCAC), the Camden Coalition, and the National Coalition of Ethnic Minority Nurse Associations (NCEMNA). These collaborations exploited community-based organizations through shareholders' sustainable health initiatives targeting the social determinants of health that facilitated long-term solutions in meeting healthcare provision needs. AI-driven initiatives can equally take this route to enhance a more holistic approach to patient care interventions that do not necessarily have to stop at clinical insights. Secondly, community organizations can be used to disseminate information on the need to acquire new health technologies by not leaving any faction of society behind in these innovations.
Conclusion and Final Remarks
In conclusion, community organization efforts in artificial intelligence healthcare initiatives are quite necessary for successful implementation and sustainability. These local entities are great instruments for these strategies; they all work together in building trust, improving data quality and health equity, and addressing the social determinants of health. Due to these long affiliations and inroads, local sensitivities about the wide-ranging needs and challenges of people from diverse populations can be harnessed through such local entities for the best support for AI-centered healthcare solutions. More so, it is clear that most community-based organizations are culturally sensitive and often well prepared to make solutions proposed by a people inclusive and effective in a society bedeviled by inherent inequalities. In addition, it proves an essentially complex process of effecting any health-related programs, with many details somewhat hard to perceive from the outside. Although AI is a game-changer and a promising disruptive technology likely to reorganize most systems within healthcare and create new norms of giving care, one should maintain sight of all the factors. For example, existing healthcare disparities and the struggle with trust in public institutions must be considered for the implementation of technologies to be effective. The partnership between a tech company and a healthcare service provider can create an even fairer system in which all patients of diverse backgrounds are bound to access quality treatment due to the strengths of community organizations. AI health systems should prioritize building trustworthiness and community power to address health inequities while promoting sustainability.
References
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Crowder, S. J., Tanner, A. L., Dawson, M. A., Felsman, I. C., Hassmiller, S. B., Miller, L. C., Rinehard, S. C., & Toney, D. A. (2022). Better together: Coalitions committed to advancing health equity. Nursing Outlook, 70(6). https://doi.org/10.1016/j.outlook.2022.02.013
Friend, D. (2023). Council post: AI is only as good as the data that trains it. Forbes. https://www.forbes.com/sites/forbestechcouncil/2023/08/10/ai-is-only-as-good-as-the-data-that-trains-it/
National Library of Medicine. (2021). Culture counts: The influence of culture and society on mental health. National Library of Medicine; Substance Abuse and Mental Health Services Administration (US). https://www.ncbi.nlm.nih.gov/books/NBK44249/
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Yasmin, S., Haque, R., Kadambaya, K., Maliha, M., & Sheikh, M. (2022). Exploring how public health partnerships with community-based organizations (cbos) can be leveraged for health promotion and community health. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 59, 004695802211393. https://doi.org/10.1177/00469580221139372
