Utilizing Digital Tools and Practices to Transform Health and Social Service Delivery
Summary:
Technology is transforming health and social services through digital solutions such as; mobile health (mHealth), telehealth, internet of things, sensors, robotic process automation, artificial intelligence, virtual care, and chatbots. The wide adoption of patient tracking systems such as Electronic Health Records has digitized the tracking of personal health information and altered the consumer experience. Health and social service mechanisms are becoming targeted and personalized to the individual patient/client. Analysis of large-scale patient data (e.g., clinical and genomic data) through machine learning approaches have enhanced diagnostic processes and ushered the era of precision medicine. Healthcare is becoming personalized, targeted, and multi-dimensional. With a convergence among multiple domains of a clients life as being critical for health and wellbeing including home and community-based factors, social networks, socioeconomic status, and education. Social determinants of health are both causal factors and outcomes in health pathways. In an interconnected world where the provision of healthcare is mediated by technology, integrated theoretical approaches are needed to study implementation and causal factors. The Diffusion of Innovation Theory (Rodgers, 1962), Social Determinants of Health (SDoH) (Marmot, 2005), and Generic Implementation Framework (GIF) (Moulin et al., 2015) facilitate and quantify the technological social impact. Theories frame how evidence-based practices can be implemented and aided by technological innovations, how to leverage multisectoral approaches to address health disparities and maintenance of cross-sector collaborations and implementation at scale. Systemic approaches leverage the public and private partnership to enhance care, foster transformational change, and address ethics in care.
Background/Objectives:
Project objectives include assessing; (1) Implementation of digital solutions in social service programing (Generic Implementation Framework), (2) Determining rate of adoption of technological solutions (e.g. Diffusion of Social innovation), (3) Ascertain the level of intervention by social determinants of health (SDoH) (e.g., timely provision of services or referrals to health/behavioral health, social services, education, and employment). The secondary goal of the project was to create closer collaboration between health and social service organizations and enhance the adoption of technological innovations through partnerships with technology vendors. Including creating a digitally literate workforce in leveraging technology for improved patient outcomes.
Description of Intervention and/or Methods/Design:
Design of Digital Transformation in Social Service Settings project was guided by lessons learned from the digitization of the healthcare sector and how implementation practices can be levied in other contexts. This feasibility study was guided by an integrated framework that includes; Diffusion of Innovation Theory (Rodgers, 1962), Social Determinants of Health (SDoH) (Marmot, 2005), and Generic Implementation Framework (GIF) (Moulin et al., 2015). The project addressed the complexity of enhancing health outcomes by implementing the latest technological approaches in digital health and augmented intelligence to social service contexts.
Results/Lessons Learned:
Lessons learned include providing training and implementation support for staff to uptake and utilize technological practices. Augmenting care with technology requires cross-sector partnerships between technology firms and social services as the new modality to enhance digital transformations. The sustainment of tech-based practices requires investment and maintenance funds, including developing a digital infrastructure that is interoperable between health care and social service organizations. To optimize patient care it is important to focus on protection of patient data and identify the appropriate interoperability platforms to share communication in order to enhance care. Data and technology can drive social care integration into health care by building health information exchanges that can aggregate data and share that information across disparate systems. This health information can be used to create a digitized health care system that can identify individual and social risks and precisely target care.
Discussion/Implications for the Field:
Implications of applying the latest health technologies into social service settings include making and communicating organizational commitment to addressing health-related social needs and health disparities at individual and community level. It's also important to recognize that comprehensive care includes an understanding of an individuals social determinants of health. Utilization of patient-centric care models augmented with latest technologies to more routinely assess issues and incorporate comprehensive data-driven decision-making. Lastly, care systems need to utilize human center design elements to engage patients, community partners, front line staff in both the design and utility of technologically enhanced care systems.
Abstract submitted by:
Besa Bauta - NYU Silver School of Social Work
Arik Hill - The New York Foundling
Keng-Yen Huang - NYU Langone Health
Technology is transforming health and social services through digital solutions such as; mobile health (mHealth), telehealth, internet of things, sensors, robotic process automation, artificial intelligence, virtual care, and chatbots. The wide adoption of patient tracking systems such as Electronic Health Records has digitized the tracking of personal health information and altered the consumer experience. Health and social service mechanisms are becoming targeted and personalized to the individual patient/client. Analysis of large-scale patient data (e.g., clinical and genomic data) through machine learning approaches have enhanced diagnostic processes and ushered the era of precision medicine. Healthcare is becoming personalized, targeted, and multi-dimensional. With a convergence among multiple domains of a clients life as being critical for health and wellbeing including home and community-based factors, social networks, socioeconomic status, and education. Social determinants of health are both causal factors and outcomes in health pathways. In an interconnected world where the provision of healthcare is mediated by technology, integrated theoretical approaches are needed to study implementation and causal factors. The Diffusion of Innovation Theory (Rodgers, 1962), Social Determinants of Health (SDoH) (Marmot, 2005), and Generic Implementation Framework (GIF) (Moulin et al., 2015) facilitate and quantify the technological social impact. Theories frame how evidence-based practices can be implemented and aided by technological innovations, how to leverage multisectoral approaches to address health disparities and maintenance of cross-sector collaborations and implementation at scale. Systemic approaches leverage the public and private partnership to enhance care, foster transformational change, and address ethics in care.
Background/Objectives:
Project objectives include assessing; (1) Implementation of digital solutions in social service programing (Generic Implementation Framework), (2) Determining rate of adoption of technological solutions (e.g. Diffusion of Social innovation), (3) Ascertain the level of intervention by social determinants of health (SDoH) (e.g., timely provision of services or referrals to health/behavioral health, social services, education, and employment). The secondary goal of the project was to create closer collaboration between health and social service organizations and enhance the adoption of technological innovations through partnerships with technology vendors. Including creating a digitally literate workforce in leveraging technology for improved patient outcomes.
Description of Intervention and/or Methods/Design:
Design of Digital Transformation in Social Service Settings project was guided by lessons learned from the digitization of the healthcare sector and how implementation practices can be levied in other contexts. This feasibility study was guided by an integrated framework that includes; Diffusion of Innovation Theory (Rodgers, 1962), Social Determinants of Health (SDoH) (Marmot, 2005), and Generic Implementation Framework (GIF) (Moulin et al., 2015). The project addressed the complexity of enhancing health outcomes by implementing the latest technological approaches in digital health and augmented intelligence to social service contexts.
Results/Lessons Learned:
Lessons learned include providing training and implementation support for staff to uptake and utilize technological practices. Augmenting care with technology requires cross-sector partnerships between technology firms and social services as the new modality to enhance digital transformations. The sustainment of tech-based practices requires investment and maintenance funds, including developing a digital infrastructure that is interoperable between health care and social service organizations. To optimize patient care it is important to focus on protection of patient data and identify the appropriate interoperability platforms to share communication in order to enhance care. Data and technology can drive social care integration into health care by building health information exchanges that can aggregate data and share that information across disparate systems. This health information can be used to create a digitized health care system that can identify individual and social risks and precisely target care.
Discussion/Implications for the Field:
Implications of applying the latest health technologies into social service settings include making and communicating organizational commitment to addressing health-related social needs and health disparities at individual and community level. It's also important to recognize that comprehensive care includes an understanding of an individuals social determinants of health. Utilization of patient-centric care models augmented with latest technologies to more routinely assess issues and incorporate comprehensive data-driven decision-making. Lastly, care systems need to utilize human center design elements to engage patients, community partners, front line staff in both the design and utility of technologically enhanced care systems.
Abstract submitted by:
Besa Bauta - NYU Silver School of Social Work
Arik Hill - The New York Foundling
Keng-Yen Huang - NYU Langone Health
Source
Approved abstract for the postponed 2020 SBCC Summit in Marrakech, Morocco. Provided by the International Steering Committee for the Summit. Image credit: Pxfuel