Building and analyzing a wealth of health and health-related data such as health outcomes, prescribing, insurance, consumer-generated, population health, patient-reported outcomes and genetics form the foundation of predictive, preventive and personalized health care.
The road to leveraging artificial intelligence is built from good data
As health care organizations consider how to move ahead, creating the right data environment to support a more human-centered approach to health care is an urgent need. Five key trends in data will bring this about.
Building and analyzing a wealth of health and health-related data such as health outcomes, prescribing, insurance, consumer-generated, population health, patient-reported outcomes and genetics form the foundation of predictive, preventive and personalized health care.
Figure 1: A new ecosystem built around the needs of the individual
Trend 2: The rise of digital remote care supported by data liquidity
Anywhere, anytime care is built upon consumer-oriented virtual health technologies and care models. Apps, wearables and environmental sensors capture and share permissioned information across the care continuum.
As 5G networks further penetrate, they will make the capture of real-time data much faster and more robust. The potential of 5G is immediately apparent for acute home-based care, new community services and connected hospital devices. More complex health programs and analytics tools become possible, such as virtual reality, gamification, robotics, video coaching and the smart home.
The data generated by these rising technologies will need equally powerful tools to organize, interpret and draw insights from them — and AI is critical in this journey.
Volumes of patient data can be aggregated from multiple sources. AI and analytics turn complex information into usable insights, including individualized wellness solutions and show how to efficiently provide care across the ecosystem.
For consumers, this data-driven connected health environment will anticipate their needs, passively monitor their health, and improve the quality and timeliness of their care. For health care organizations, AI analytics help address operational challenges, such as waste across workflows, supply chains, and duplicative procedures, as well as help to anticipate clinical risks.
Trend 3: Interconnecting broad-based data for highly personalized care
To keep up with the velocity and variety of health data generated today, the health information infrastructure must enable providers to connect, combine, analyze, and share health and social data. Current health information architectures have some integration capabilities but these, such as SMART or Fast Healthcare Interoperability Resource (FHIR), are generally viewed as workarounds. There are multiple limitations around what can be integrated, as the sheer quantity of solutions (and thus integration points), diverse data models, and lack of standard data nomenclature make sharing data within and across systems difficult and expensive.1, 2
Combining all the relevant sources of data offers the necessary underpinning for a preventive model of health where people have wellness as usual and clinical care by exception. Data analytics can shed light on individual behavior patterns and predict future behaviors, barriers to change, and high probability solutions.
Well-established scientific consensus holds that behavior is critical to health outcomes. It is now clear that to deliver better outcomes to individuals across the population, lasting behavioral change needs to become a central part of health care.3, 4
Most importantly, behavior change needs to be treated as an integral aspect of the way health care is personalized and managed. Future products and services need to be delivered within an influencing environment where sensors and AI can enable a continuous “judge and nudge” assessment of patient behavior and steer them toward better health.5, 6
Trend 4: Trusted intelligence drives participation and engagement
Increasing mobility, transparency, and availability of health information brings both benefit for consumers and clinicians, as well as risk associated with a fluid system.
While data sharing brings immense value, connecting data also brings risk. To share data, you need to trust the other party’s data security and have tools which support digital use authorizations, traceability and control – much of which is currently a serious unmet need across the sector. Governance structures, policies and practices must be sufficiently robust and cover the ethical, legal and moral aspects of collecting, storing and sharing of sensitive health data.
As connectivity becomes more central to health care, regulators will continue to take a strong stance on the need to secure data. Attention will be directed toward ensuring consumers have control over their health information. Gaining consumer and clinician trust is critical, particularly when it comes to the safety, validity, and integrity of the data generated.
In the near future, data will be passively captured by unobtrusive remote monitoring, and continuously analyzed via AI. The onus will be on health care organizations to maintain high standards of transparency around the integrity and security of data and devices. All elements must meet accepted data security frameworks, and safety standards for personal health and social care information. This includes clarity around the ownership rights regarding personal data, secondary uses of data, and the protection of an individual’s privacy.7, 8
Trend 5: A future-ready culture and workforce that embraces digital
Health care organizations will achieve success when they see that the way forward is built around data, technologies and human capacities that grow the business of tomorrow, rather than just repeating today’s procurement and training cycles. The winning organizations will build from an ecosystem mindset, identifying what data are critical and the right strategy to access them. They will understand they must, in parallel, attract the right workforce to fully leverage technology innovations. For these organizations, data will become the central asset in the organization.
Fostering innovation is key to creating and testing effective blueprints for doing things differently by using data and technology. Health care organizations must adapt operating models and partnering strategies to the realities of the emerging ecosystem, augmenting their skills mix and capabilities by working with other organizations.
Business model architecture should reflect the core capabilities in the new data environment. This includes a governance model that steers the enterprise from a siloed to a frictionless data environment. New commercial and operating models built around creating value will be required to support new ways of delivering care. A workforce strategy will need to address the shifts in workforce supply and demand that arise through automation and a shift in the mix of skills and the nature of job roles. And finally, a new mix of leadership skills will be needed to lead health care providers in the digital era.9
A health data agenda
To build for the beyond, prioritizing a health data agenda may allow health care organizations to anticipate and plan for a connected health ecosystem in the future. Right now, entities should anticipate how they can create future value and enable personalized outcomes driven by the power of data.
Three elements we consider important to a health data agenda are:
1. Harnessing the volume, variety and velocity of health data.
The potential of massive health data sets may allow organizations to capitalize upon the promise of transformative technologies. Data science models are developing that inform clinical decision making. These are moving past simply reporting data to providers, to machine learning algorithms in a dynamic environment of predictive analytical models for application in multiple care settings.
At the enterprise level, intelligence functions convert data into actionable insights around population health, clinical decision support, and streamlining operations for greater efficiencies. Rather than waiting for the ideal data model or vendor, health care organizations need to start implementing data-driven care models and operations in parallel to uplifting capabilities and defining the future state.
2. Envisioning for the future
Envisioning for the future should guide stakeholders to see beyond the present, to what’s not (yet) possible. Implementing future-proof infrastructure and capabilities will be critical. This means adopting the principles of modularity, agility, interoperability and heterogeneity.
Through a lens of “now, next, and beyond”, data generating technologies can be considered as bringing benefit as:
- Foundational: to bring agility and efficiency into the present
- Supporting: to provide appropriate capabilities in the near future, and
- Differentiators: to support new models and advanced technologies into the future
3. Laying the foundations for the next generation of patient-centered products and services
The next generation of products and services will be built around data-driven intelligence technologies that support key high-value areas of clinician productivity, patient experience, innovation and insights, sustainability, business operations, permissioned and secure access and asset utilization.
Centered around the patient, the end result is a connected everywhere ecosystem — connecting all people and all things for better care outcomes, health equity and more sustainable business models.
Contributors: Emily Mailes, Director, Health Consulting, Ernst & Young - New Zealand; Sheryl Coughlin, PhD, EY Global Health Sciences & Wellness Senior Analyst
Resumen
Without good data, health systems won’t be smart. To deliver better care in the right place and at the right time needs an information architecture built around data liquidity. This article explores five trends that are shaping a data-driven foundation for the future health industry. To transition to a connected health ecosystem, health care organizations need to get the technical, operational and cultural changes right in order to capitalize upon the potential of massive health data sets and the promise of transformative technologies.
