There is a growing interest among healthcare organizations to leverage actionable analytics solutions to derive valuable insights from data. Advanced, AI-driven predictive modeling is working to build healthier populations that meet the demands of value-based care, and new digital experiences are reaching providers and patients through a diverse array of touchpoints. Digital health solutions, driven by new and emerging data sources, are creating a unique combination of high-touch care management complemented by automated, virtual care.
This digital transformation in healthcare is being driven by the changing nature of the healthcare landscape, as well as the demands from consumers for more say in their care. The healthcare industry is making significant investments in IT to engage and empower patients, enable caregivers and improve operating efficiencies. However, the industry is also facing pushback from the caregiver community, with many physicians feeling that interacting with an EMR reduces their productivity. Physician burnout and unrealized expectations from technology investments have created a mood of caution in digital investments.
However, the digital transformation wave is still coming, since the proven patient health benefits, as well as industry improvements, are simply too great to ignore. Given the abundance of software-driven tools, technology professionals face the crucial task of integrating applications and data among the various players in the healthcare ecosystem including doctors, hospitals, government, device makers, insurers, employers, pharmaceutical companies and patients. Seamless transitions of care between these constituencies, however, are still a major hurdle, and positive patient experience is decided by the totality of patient care carried out by all those — both within and outside — of a health system. Shared processes between clinical entities are only possible if the data can journey smoothly from one system to another.
The problem today is that there is over-engineering in healthcare with overlapping and rich data standards and formats, and implementations that stay locked tightly in proprietary strongholds.
How to Make Interoperability Work
It is imperative that digital transformation initiatives focus on interoperability and integrations through well-defined application programming interfaces (APIs). APIs are designed so that systems with validated credentials can query and access systems widely available on the internet. Systems are then designed to respond to queries from programs with data that is machine-readable.
APIs deliver the ability to securely and efficiently access repositories of big data from wearable devices, social media, curated public datasets, research, and episodic care. They are the key to better understanding patients’ financial, social and behavioral context, and through predictive and prescriptive analytics can reveal trends across populations and micro-populations. With the explosion of disparate technologies, it will be about connecting them all quickly and efficiently to gain a competitive edge in healthcare.
As we launch into 2018, questions remain about the healthcare policy environment and how it can impact many healthcare initiatives. As Yogi Berra said, “It’s difficult to make predictions – especially about the future.” I feel confident, however, about some fundamental trends in the healthcare landscape. These include a steady shift toward value-based care, an increased focus on data and analytics as a core enabler for digital transformation, and the all-consuming focus on the patient experience.
Here are my four key predictions for the healthcare IT trends that will transform the industry in 2018:
Patient Satisfaction Takes Center Stage
The era of healthcare consumerism is here. Patients are bearing increasing financial responsibility for healthcare costs, and seek improved experiences as a part of the value-for-money equation. In response, providers are taking a 360-degree view of patients, employing better analytics to leverage patient data such as demographic information, lifestyles and individual preferences, to personalize interactions and treatment.
Artificial Intelligence (AI) Becomes Entrenched in Clinical Settings
Despite the overuse of the term AI to describe many types of technology-enabled solutions, the adoption of AI has been steadily gaining ground in a wide range of settings. Deep learning algorithms will increasingly be used in clinical settings to support medical diagnosis and treatment decisions, predict the likelihood of patient re-admissions and help providers better leverage the data that has been accumulating in electronic health records. According to the 2017 Internet Trends Report by venture capital firm Kleiner Perkins, medical knowledge is doubling every three years, and the average hospital is generating more than 40 petabytes of data every year.
While all this accumulated information empowers more informed physicians, the growing range of data and knowledge sources creates a challenge as well, since physicians and clinicians must manage and stay on top of this information on specific conditions, especially in fields such as oncology. AI technologies are enabling time-constrained and overworked physicians to make sense of the vast amounts of data, helping them uncover hidden insights and supporting their medical diagnoses and decisions with timely and relevant input at the point of care.
Open Source Finally Takes Hold
Healthcare organizations have been conservative when it comes to open source technologies, largely due to concerns about data security and privacy. With the growing adoption of cloud-enabled solutions and a gradual shift of enterprise IT workloads to the cloud, they no longer have to worry about risks to the IT environment and can rely on mature cloud service providers, such as Amazon Web Services (AWS) or Microsoft Azure. And, open source architecture can now incorporate robust technology components with rich functionality. Our current collaboration with Partners Healthcare to build a digital platform for clinical care is based on an open source architecture. As the industry shifts rapidly to value-based care, cost pressures will force healthcare enterprises to transform their technology strategies, turning to open source solutions to rapidly build new solutions cost-effectively.