Measuring the Four Dimensions of Digital Health
Today we deliver healthcare to patients. Yet, the future of healthcare is working with people and populations to manage their health to stay well. Healthcare of the future creates the conditions for provider teams to enter into a partnership with their patients, using technology to meaningfully connect people to providers, making it possible for them to be in control of their own health and wellness journey. This is a major shift towards a digital health ecosystem approach that connects and empowers people and populations to manage their own health and wellness. There are four key dimensions that are foundational to achieving digital health: Person-Enabled Health, Predictive Analytics, Governance and Workforce, and Interoperability.
HIMSS recently released a framework to guide health organizations and health systems on strategies to advance digital health ecosystem capacity. The Digital Health Indicator (DHI) provides a measure of progress towards a digital health ecosystem to identify strengths across the four dimensions of digital health and areas for further development. The DHI enables a health system to track their progress, while identifying both strengths and opportunities to inform their digital health strategy.
The Dimensions of Digital Health and How They’re Measured
Person-Enabled Health focuses on the health system meeting and delivering on the individual’s needs, values and personalized health goals. It recognizes the value and importance of connectivity between people and their care teams, creating a partnership based on individual needs and choice. It leverages digital options (such as online tools, handheld devices for “care anywhere” approaches, or apps that enable on-demand health and wellness care) to support self-management of personal health and wellness goals, shaped by the unique life circumstances, preferences, health needs and choices of the individual.
Person-Enabled Health is measured by the following:
- Personalized care delivery is the personalization of care, whereby individuals are the primary decision-maker in managing their health and wellness. People choose the digital tools and technologies (e.g. personal digital tools, mobile devices, wearables) that best suit their unique life circumstances and personalized approaches to healthcare.
- Proactive risk management focuses on care delivery that proactively identifies risks to health and wellness, cues individuals and their provider partners on the risks and strategies to proactively intervene to prevent risk and sustain or strengthen progress toward health goals. Proactive care delivery requires a transformational shift from the siloed, disease management approach of today, to one where seamlessly integrating services and enabling care delivery in digital ecosystem environments enables personalized care delivery to individuals and populations. Proactive care delivery means anticipating and identifying populations who are at risk for deterioration in health and proactively intervening to support and strengthen health to keep people well.
- Predictive population health is whereby health system data is mobilized and robust analytics tools track population health outcomes to anticipate risks (e.g., gaps in health screening, risks of chronic illness, risk of medical error) and prescribe and inform program level strategies to manage and reduce risks to population segments, focused on health and wellness. Predictive population health is informed by a robust analytics infrastructure that mobilizes digital tools, dashboards, and public reporting strategies to strengthen population health outcomes.
Predictive Analytics is the transformation of data into knowledge and real-world insights that inform decisions for individuals, health teams and health system leaders. Predictive analytics brings together health system data, along with digital tools and population data, to inform care delivery and operations, creating personalized healthcare, prediction of risk to optimize outcomes and the tracking of population health proactively to support health and wellness.
Predictive Analytics is measured by the following:
- Personalized analytics collects individual health and wellness data from multiple sources (e.g. personal digital tools, mobile devices, wearables), including progressive data sources (e.g. genomic and biometric), to enable individuals and their provider teams to track progress towards health and wellness goals. Personalized analytics connects people to health teams enabling report of outcomes, side effects, adverse events and progress towards health goals.
- Predictive analytics track and trace outcomes across the journey of care for every individual patient, to identify outcomes that work best for every individual and the conditions under which best outcomes are achieved. Predictive analytics also track program and population level outcomes to identify risk for potential harm or poor outcomes to inform quality and safety strategies, and proactively alert clinician teams and individuals to strategies to keep people well.
- Operational analytics mobilize data to track health system performance outcomes including supply chain, clinical, financial, and adverse events. Operational analytics use digital tools and dashboards to track operational outcomes such as efficiency, productivity, quality, safety, access, equity, and cost. Operational analytics include real-time dashboards for use by leaders and decision makers to assess value, system learning, and sustainability (e.g. workforce sustainability, financial sustainability). Aggregate performance outcomes are reported publicly to inform individuals, manufacturers, suppliers, governments and funders. Analytic tools track, monitor, and measure value- based outcomes to inform health system performance strategy.
Governance and Workforce
Governance and Workforce is the strategic leadership and oversight of digital health systems that ensures the policy and regulatory environment of health systems guards privacy, security, stewardship and accountability. Governance puts priority focus on a sustainable, high-performing workforce that is prepared to deliver digitally-enabled health services. The future of sustainable, high- performing digital health ecosystems requires unique governance structures to transform workplace environments. These digitally-enabled environments, in turn, enable care delivery models that are informed by data analytics, and guided by robust data stewardship, policy and decision-making processes.
Governance and Workforce are measured by the following:
- Stewardship describes the leadership, culture, vision and objectives required to support digital health. It includes the accountability frameworks and management processes such as the responsibility of planning, building, running, and monitoring digital health as well as the resources and expertise to evaluate and use new technologies. The adoption of new digital tools is informed by evidence and best practices to support system-wide adoption and utilization at scale. Criteria aligned with the use of data and digital technologies are guided by, and inform, best-practice decision-making to improve quality of care.
- Policy and decision-making describe the measurement, learning and feedback, resource allocation, and coordination used for governance processes that encompass policy and decision-making required to support digital health transformation. Policy and decision-making processes include evidence informed digital health strategy, alignment of digital processes, value-based health system incentives and frameworks focused on outcomes. The impact of digital transformation requires policy frameworks that support and incentivize performance (e.g. efficiency, productivity, quality and cost) outcomes, and enable health system stakeholders to build and sustain meaningful relationships with the people and populations health systems serve.
- Transparency supports connectivity and relationships with people and populations, including digitally-enabled communication and transparency of quality, safety, and performance outcomes. Every person is considered a partner in healthcare whereby governance and oversight ensures transparent access to personal health information and health system level performance outcomes, as well as equity in access to healthcare services, data, and digitally-enabled care delivery.
- Workforce capacity and competency is the rapid evolution of digital health ecosystems that requires knowledge, skills and abilities across the workforce to support and enable adoption of digital health strategies to support person-enabled care focused on health and wellness. Workforce policies support and retain a high-performing workforce that is incentivized to design, adopt and scale digitally-enabled care processes and operational strategies focused on outcomes, value and impact for people, populations and operational performance that advances health system sustainability.
Interoperability is the ability of different information systems, devices and applications to access, exchange, integrate and cooperatively use data in a coordinated manner. This can happen within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally. Health data exchange architectures, application interfaces and standards enable data to be accessed and shared appropriately and securely across the complete spectrum of care, within all applicable settings and with relevant stakeholders, including by the individual.
Interoperability is measured by the following:
- Foundational interoperability establishes the inter-connectivity requirements needed for one system or application to securely communicate data to, and receive data from, another. It is defined as the exchange of data at the individual level, which is accessible across clinical, social, and community settings. Foundational features of interoperability include: data and information capture, capacity for data storage and data management, access to data to inform communication between individuals and clinicians, teams, and organizations, capacity for wireless and multimedia data exchange, and virtual/remote information exchange to communicate information.
- Structural interoperability defines the format, syntax, and organization of data exchange including at the data field level for interpretation. It describes the flow of data and information that is automated and integrated across multiple and varied sources of data, data reporting and access functions, data center structure, data integrity, and information exchange across multiple and varied platforms.
- Semantic interoperability provides for common underlying models and codification of the data, including the use of data elements with standardized definitions from publicly available value sets and coding vocabularies, providing shared understanding and meaning to the user.
- Organizational interoperability includes governance, policy, social, legal and organizational considerations to facilitate the secure, seamless and timely communication and use of data both within and between organizations, entities and individuals. These features enable shared consent, trust and integrated end-user processes and workflows. Examples include secure access to individual-level data, identity, and access management, centralized authentication, firewall integration, web and email security, and cloud orchestration and coordination (both private and public cloud infrastructure). Organizational indicators also address quality of service and experience for users.
The DHI provides health systems with the ability to measure and assess digital capabilities, but it also guides and informs strategies to advance and accelerate digital ecosystem development. This will offer health systems greater resilience and capacity building to address future challenges and create systems that will offer new capacity to manage when unexpected surges in demand may occur in the future.