Why can’t EMRs fight Population Health Management Challenges?

For health care providers, choosing the right population health solution path is riddled with apprehensions because of the many challenges that come in the way of a successful PHM implementation. This is not just another blog about the challenges with PHM; it will go in-depth about how it can be done with the willingness to inculcate data-driven initiatives in any given network.

The real issue is that the correct PHM solution cannot be achieved through Electronic Medical Records alone. The reason? An EMR implementation and data repository are just preliminary requirements when it comes it health IT. The process of PHM requires heavy and advanced data analytics. Several roadblocks still lie ahead as many stakeholders in healthcare have divergent opinions on increased investment in health IT and an apparent resistance to change.

To explain the problem of EMRs more clearly, let’s make up a metaphor of a student who keeps adding to his knowledge base day in and day out but, fails to keep a report to measure his progress. Now, without the tools to analyze or measure his improvement, he cannot make a blind guess about his performance, potential or scope of improvement. Similar is the case with health systems that have installed EMRs are constantly digesting patient data, without realizing the actual rewards that be achieved by analyzing this data.

Not undermining the importance of EMRs, but think of it this way- they are just the bricks, however, in order to make a building, you also need mortar.

Why only EMRs are not enough?

EMRs have remained unchanged ever since their inception that is to say the database structure and technology they are built on has not evolved. On the other hand, healthcare has witnessed numerous technological breakthroughs in the past decade. As antiquated systems, EMRs fail to truly support interoperability, analytics and improving care outcomes. Moreover, being mere repositories of medical data, the EMRs cannot fulfill the many analytical requirements of 21st-century healthcare. They are incapable of presenting data in a way that gives useful insights.

Therefore, EMRs are what you might call the ‘necessary evil.’ As data repositories, they are extremely useful to store and translate information, but beyond that, hospitals and health systems have to look outside of them for smarter and innovative technological solutions, which include analytics and insights that support clinical decisions.

Considering the student example as mentioned above, suffice it to say that without being able to measure the impact one is making with the backing of data, it does not matter whether the data one has is on paper or it is paperless. Thus, having an EMR is merely a regulatory measure. A necessary one indeed, but a limited one nonetheless.

Therefore, any ACO trying to generate savings without taking up the responsibility of improving quality is an exercise in vain. Moreover, said ACO cannot expect to improve its quality without knowing exactly what and how to improve.

In any given healthcare network, there are at least a million patients and to keep track of everyone’s health can drain the network of its resources. However, with data-driven population health management, it is possible to easily identify the high-risk population, patient and disease registries and point out the ongoing trends.

What more is needed? EMR installation is only the first step.

First Step: The majority of hospital networks have fulfilled the requirement of EMRs to store data electronically as the first step to digitization. However, it does not give us a meaningful exchange of data as EMRs are not interoperable because they were created as siloed and disparate systems of record. Even so, it gives us a single data repository where all patient information of a hospital or health system is stored in a consolidated manner.

Second Step: The succeeding step for fully utilizing the information at hand is to understand the data. It means integrating data to gain a wholesome picture of the patient’s medical history to provide better care. It also means analyzing the data to perform prescriptive and predictive analysis for more accurate diagnoses and plan preventive care as and when needed. Emphasizing data analytics is especially required when analyzing the trends in the population of a health network.

Third Step: An essential step is identifying the patients that are at-risk, needing immediate care intervention. Also, it is crucial to focus on those factors that are making the network lag behind in terms of quality of care and performance of physicians. On top of this, data can track down cost-drivers that increase the financial burden of hospitals and health systems.

Fourth Step: The ultimate step for initiating successful PHM has to be best practice implementation for improved outcomes. For example, reducing the time taken during post-acute care (PAC) of patients. High readmission rates and expensive skilled nursing facilities (SNF) lead to heavy losses on behalf of the hospitals. With data-driven care coordination, PAC can be efficiently utilized for maintaining the 30-day readmission rate. Analytics on patient data can help in identifying frequent flyers and measure the impact of value-based activities. More than anything else, data analytics can measure and tell the impact that was created by PHM in a health network.

The Road Ahead

The deeper the foundations, the stronger the fortress.

Post the boom of EMRs, the next steps that healthcare chooses will define the future that is to come. The best way to understand what we are going to achieve is to see how we have fared in the past. A sturdier healthcare with foundations that can overcome any challenge will be the face of tomorrow’s healthcare. Data Analytics is playing a major role in the top-most leading industries of the world, then why should healthcare stay behind? With growing focus on value-based care, improving quality of care and maintaining costs becomes a cakewalk with the right tools.

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