Facilitating smooth transitions to value-based care – data aggregation

“To them though, not to us, we were just a catalyst for their imagination.” – Donald Sutherland

With the upcoming mandates towards value-based healthcare reimbursements, patient care delivery and purchasing models, healthcare organizations will have to assume accountability towards improving overall patient health outcomes through cost-effective healthcare practices. The transition to such a mode of healthcare practice will require all the consumers of healthcare services – patients, providers, and payors – to collectively ensure that our healthcare dollars are used wisely and for the right purposes.

Here at Holon Solutions, we believe technology will play a vital role in enabling this transition towards value-based care, breaking data silos, empowering all the consumers of healthcare to make the right decisions at the right time. I recently got to strategize with Renee Broadbent, Senior Vice President of Population Health at Holon Solutions, around the role of technology for the core pillars of effective Population Health Management (KLAS)[1]:

  1. Data Aggregation
  2. Data Analysis
  3. Care Management,
  4. Administrative and Financial Reporting
  5. Patient Engagement
  6. Clinical Management

Because this is a complex topic, we are going to break it down by pillar in a series of discussions and blogs. In this blog, we will focus on pillar #1: Data Aggregation.

Renee Broadbent:

Prior to joining Holon Solutions, I worked at a large academic medical center, which was the center of a very complex clinically integrated network (CIN)  The network had multiple hospitals, clinical practices, federally qualified health centers (FQHCs), skilled nursing facilities (SNFs) and other organizations that needed to integrate with us to support their value-based care programs.  As part of the leadership team that was guiding the organization through the value-based care transition, I was charged with achieving the first pillar of the population health framework: data aggregation.  Data aggregation facilitates the other components of population health management; I like to think of it as the foundation of the house.

Aggregating data from many data sources into a common format, a location that is useful, and in a timely manner together are extremely challenging.  While we use words like “interoperability”, “standardized formats”, “regulatory requirement” the reality is that data remains siloed and not easily exchanged.  In this setting, every day of my life was a struggle to make that happen.  The outcome for the lack of data, was inconsistent and incomplete information and relying on many more resources and manual efforts so data could be effectively utilized to support quality metrics and medical expense – with inevitable time lags.

Saurabh Mathur:

This challenge is multi-faceted.  Let’s lay out some facts to first understand the scope of the problem here:

Fact #1: Patients have multiple providers for their care.  Most patients have several doctors (primary care and specialists) to address their care needs.

Fact #2: Data is in multiple systems – serving multiple needs.  With patients having multiple care providers, those providers are on numerous Electronic Medical Record systems which typically are designed to accommodate the clinical information for that specific provider’s needs.  Aside from EMRs, many other systems play a role, such as lab information systems, health information exchanges, billing systems, third-party apps, etc.  Data is everywhere.

Fact #3: Multiple patient identifiers per patient.  With the lack of a universal health data identifier for a patient, such as a social security number, each of these systems assigns a patient with a unique identifier.  This results in one patient having multiple identification numbers.  This, along with inadequate identity attribution across the disparate systems, proper matching of patients to their data is a challenge.

Effective partnerships and evaluating the right technology solution are key. We recommend a partnership with a data analytics company that has a comprehensive data warehouse and with an internal governance structure so that the data is reliable.  A sound data analytics strategy provides for improvements in clinical, operational and financial outcomes and successful population health and accountable care.

Data analytics will only be as good as the input data. Obtaining the much-needed chart data from practice EMRs is core to effective population health. Often times, we hear customers and industry leaders complain about the difficulties in managing EMR integrations, and the quality of data from those EMR systems. The problem is more than just data related. A broader set of issues lie within the relevance, quality, and efficacy of obtaining patient chart data. Along with the increasing levels of maturity of effectively utilizing their EMRs, providers have fallen victim to the challenge of managing recorded observations, by multiple providers, in multiple locations within the system. Some of those recordings are not reported out of the EMRs for analysis, causing a state of confusion amongst the providers where they think that they have done what is necessary for the patient’s care, but analytics outcomes continue to show gaps in care.  There are no standard data exchange mechanisms implemented across the care continuum, therefore it makes it challenging for the analytics systems to consume the data in a consistent format and to generate the intended outcomes in a timely fashion.

Focusing on these problems of management and data aggregation, known as the “chart chase” by many, Holon’s CollaborNet™ Connect app was designed to automate the retrieval of clinical data from different EMR systems and supply the data to the designated analytics system, mapped to the appropriate formats. CollaborNet Connect enables a measurable way to gauge provider engagement and performance in a timely way and assists in gap closures.  Holon can do this better, faster and cheaper than traditional point-to-point interfaces through our sensor-based methods.  Paired with Holon’s CollaborNet™ Insights app, which automatically surfaces contextual patient information within clinician’s workflow, equates to a win-win.   As providers navigate through their EMR, critical patient-in-context information is presented.  With providers now engaged with useful, timely, contextual information, value-based care can be activated.  Here are a few results we’ve seen:

  • Care variations decline as providers throughout the network receive the same insights – driven by the rich data aggregated from across the network – not just a handful of systems.
  • Clinical engagement improves as providers and their care team now have access to timely information they need to deliver the best treatment options, based on rich analytics.
  • User experience is elevated: Holon saved them time per visit by surfacing interactive insights to and from the point of care.

Data is the foundation for any population health management program.  Holon enables our value-based care partners to solve the pillar of data aggregation.  No more third-party “point-to-point” interfaces that take a tremendous amount of time to implement, come with substantial costs from all vendors involved and are prone to frequent disruptions or downtime when a vendor’s version updates.  Holon offers a first-to-market seamless integration, vendor agnostic approach, reliant on our proprietary methods with our point of care platform.  Contact us and let us know how we can help you Liberate the Data.

[1] Framework Source: KLAS Population Health Management 2017, Part 1 – Validating the adoption of PHM Functionality