For almost all governments and nations on the planet, providing better health care with lower costs is top of mind. There are conflicting studies on how to improve outcomes and lower costs, and the movement forward is slow, because health care ecosystems are very complex.

Certainly this is the case in the United States. We use the term “ecosystem” purposefully, because health care in most countries isn’t actually a single system, irrespective of public financing models, but it is a multifaceted interaction of numerous disconnected systems and processes. The “diagnosis” for many contemporary health care ecosystems is that they are inefficient.
We will see tremendous improvement in outcomes, delivery efficiencies and efficacy of new drugs/treatment regimens as well as the overall health of our citizens if we can reduce the friction in the health care ecosystem. That entails reducing duplication of efforts, improving the quality of shared data, streamlining processes as well as data sharing and improving insight into how the ecosystem is working with real-time and predictive analytics.
Smarter health care means we’re “instrumenting” all the technical systems, components and devices so that they are able to interconnect and share data with one another (and human users). Then, we’re enabling both systems and humans to be more intelligent in making critical decisions - becoming more knowledge-rich and insightful in achieving smarter health care. The beneficiaries of these advancements are consumers and public health in general.

The Prescription for Smarter Health Care

In order to instrument systems correctly, we need to enable them to operate autonomously, while sharing more consistent data with other systems that need the data. And this major effort requires an ID defined by data governance. Smarter data sharing means we’re increasing interoperability between systems, tools and devices.

It means a monitoring station in a patient’s room or home can communicate seamlessly with a nearby wireless network, enabling other devices and analytical tools to track a patient’s vital signs in real time and look for trends in their health. It means that a prescription can be tied into remote monitors and another monitoring station can look for certain markers associated with the drug’s performanceor for drug interactions. These can drive alerting mechanisms so providers can proactively adjust patient care to improve outcomes and avoid costly readmittances.
Ultimately, we need to improve the health of our citizens, which entails connected and personalized care. Physicians need to be able to work together with other service providers (disease management counselors, social services, therapists, dietitians, etc.) to treat not only “what is the matter” but also what matters to patients. This requires information sharing for service integration and predictive modeling to identify gaps in care and care coordination.
These all require the ability to uniquely identify a patient or their providers. Master data management solutions can communicate in real time with any instrumented system or device, thereby eliminating the need for every system to redundantly implement functions for patient or provider ID management. They can do that within a hospital’s data center, as well as across a health care “exchange” or health care information exchange. By being interconnected, these systems can share patient and provider data in real time, from any point in an ecosystem, eliminating wasted cycles and errors in patient data.

Incidents and recurrences of diseases/illnesses are also reduced by improving a patient’s health and preventing treatments. By enabling the systems and humans to collaborate, caregivers are able to make more well-informed or intelligent decisions. Individual and population health improve from these collective efforts.

Not Just Technology - Governance is Essential

Not only do we need to tear down silos to connect doctors, patients, researchers and insurers, we must specify what kinds of data they can share and under what circumstances they can do so. Defining these policies is the number one job of data governance, and it’s more about organizational dynamics and rationalizing goals, objectives and expectations than it is about technologies involved in data management. With data governance, trust in data sharing is built and maintained. Without it, the underlying systems and technologies will still be inefficient. The worst outcome is that we spend scarce resources building more sophisticated silos, thereby driving the costs of health care up instead of down.
Data governance provides the policies that dictate how data should and should not be shared. But governance needs technologies like MDM to provide the common definition of who the patient is and the providers are to enable the collaboration and knowledge management required to meet the goals of improving individual and public health while containing costs.

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