By Kim Carlson

8 Ways Claims Data Supports Population Health

8 Ways Claims Data Supports Population Health

Effective population health initiatives implemented by hospitals, large physician groups, payers, self-funded employers, among others require data analytics to be successful. The right data can inform population health strategy, goals and outcomes. While healthcare claims data is not the only data required for population health, it is a big factor in driving improvements in population health programs.

Here are eight ways we believe healthcare claims data can inform population health initiatives:

  1. Managing Overall Costs: Claims data can shed light on the disparate prices doctors and hospitals charge for the same procedures. The data can show total spending within an institution by procedure as well. Claims data can reveal which service lines are performing well and which are struggling with cost-containment.
  2. Physician Performance: Claims data can help to determine the performance of individual physicians through analysis of the services provided by diagnostic code. Data can reveal if physicians are following nationally recognized medical protocols. An example is diabetes care: According to the Pew Charitable Trusts, claims data can reveal whether a doctor followed nationally recommended protocols for treating patients diagnosed with diabetes. How many received quarterly exams? Did they receive an eye exam? How many were admitted to a hospital?
  3. Empowered Consumers: Some states through all-payer claims databases (APCDs) are making claims data available to healthcare consumers, with the idea that when consumers can compare prices across physicians and hospitals, they will make better and more informed decisions regarding both quality and cost.
  4. Improving Quality and Outcomes: When combined with clinical data, healthcare claims data can provide a very broad view at both the patient-level and population-level of interactions across the continuum of care within a healthcare system.
  5. Reduce Hospital Readmissions: Claims data can help to reduce costly hospital readmissions by uncovering areas by service line and/or at the physician-level where readmissions are occurring most frequently.
  6. Patient Engagement: Patient engagement is a key to successful population health. Claims data can help reveal when to reach out to patients as well as whether patients are filling prescriptions or following-up with recommended lab tests. In the past, technology lagged when using claims data to reveal patient patterns. However, newer analytics allow for as little as 15 days to reveal patterns such a prescription refills or follow-up tests, providing healthcare clinical teams a reasonable window to follow-up with patient outreach.
  7. Strengthen Coordination of Care: Claims data, notably when coupled with clinical data, can inform the actions of care teams that can include physicians, care managers, health coaches, caregivers and even the patients. Creating data transparency through patient portals and other tools that aggregate data into usable information allows for care plans to be adjusted to the patient’s needs.
  8. Amp Up Reporting: The best reporting reveals where there are opportunities to improve and where health systems have effectively made changes. Claims data when coupled with clinical and other data can reveal these insights. Such insights can improve population health initiatives that help to contains costs and improve healthcare quality resulting in healthier populations and healthcare systems.
measuring hospital competition

By Kim Carlson

Measuring Hospital Competition: Vital in Value-Based Healthcare

Measuring Hospital Competition: Vital in Value-Based Healthcare

For the last two decades, competition has been front-and-center in our market-driven system. Hospitals must increasingly make measuring hospital competition a priority to make the right decisions around their markets. However, measuring the competition is complex and having the right data matters as much as pairing it with meaningful methodology and analytics.

Changes in healthcare toward a value-based care model are forcing hospitals to take a broader view of data and competitive analysis. Indeed, clinical, operational and financial measures around quality intersect, making analysis challenging.

Measuring the competition is critical for making decisions around:

  • Service lines expansions, additions and closures
  • Mergers and acquisitions
  • Integrating new service lines post-acquisition
  • Physician outreach, engagement and retention

It’s no secret that with value-based care, hospitals and health systems that provide high-quality care and stellar outcomes will rise above the rest and grow their market share. Market intelligence is critical to achieve the goals of value-based care with its quality, efficiency and outcomes measures.

Furthermore, as we continue to see both horizontal and vertical consolidation within the healthcare market, the number of healthcare systems may continue to become smaller within a geographic region, changing the nature of how competition is measured.

According to data reported on the U.S. hospital industry in a 2013 JAMA article (n = 4973):

  • 60% of hospitals are part of a health system
  • There were 3.2 hospitals within a system on average
  • In 2011, there were 432 merger and acquisition deals
  • 49% of hospitals owned physician practices
  • 41% of physician practices were physician-owned

And… things continue to change, as those of us in healthcare know!

Approaches to Hospital Competitive Measurement

The Herfindahl-Hirschman Index

The HHI is a commonly-used measure of market concentration calculated by squaring the market share of each entity competing in a market and summing the resulting numbers. The Healthy Marketplace Index (HMI), created by the Health Care Cost Institute (HCCI) uses the HHI Index in its methodology. The HMI was developed as a series of metrics to assess the economic performance of health care markets, both across markets and within markets over time. The metrics are related to three aspects of the economic environments of healthcare markets, including price, productivity and competition.

A Relational Approach

An interesting approach to measuring hospital competition was put forth in 2002 by Min-Woong Sohn in 2002 called “A Relational Approach.” The approach conceptualizes competition as an attribute of a relationship between two hospitals and measures competition at the level of each pair of hospitals, the smallest unit at which competition can be measured. This approach can be used to identify the strongest competitor in a market and to estimate the strength of competition received from that competitor. This methodology can produce a ranked list of competitor’s strength and competition and how much competitive pressure a hospital receives from its local competitors.

Data Sources for Measuring Hospital Competition

The primary data sources for measuring hospital competition are:

  • Discharge data
  • Claims data
  • Emergency room data
  • Outpatient data
  • Demographic data

At INTELLIMED, we have provided highest quality claims data for over 30 years to U.S. hospitals. We are known for our stellar customer service and speed of data and analytics. We hope you enjoyed this article. Learn more about our healthcare data analytics services.


Kim Carlson is Regional Vice President of Business Development at INTELLIMED.

precision health, population health management, healthcare data analytics

By Kim Carlson

Precision Health Will Require Both High Touch and High Tech

Precision Health Will Require Both High Touch and High Tech

Stanford Medicine Precision Health has a bold vision for medicine:

Over the past century, the focus in medicine – and academic medicine – has been on the diagnosis and treatment of acute diseases. Although shining our brightest light on treating the most complex conditions has resulted in many medical advances, patient care has often been fragmented and has lacked specificity.

We have within our grasp the ability to completely change this approach. From cancer to cardiac diseases, from neurological diseases to inborn errors of metabolism in infants, from food allergies to heart transplantation – our advances in diagnostic methodologies and therapies will lead to the most precise molecular diagnoses and to treatments that are individually tailored based upon these diagnoses.

So how do we move toward this bold vision for medicine? What are the critical steps to make precision health a reality?

Precision health will require both high touch and high tech.

High Touch: Patient-Centered Care

Many believe that true precision health is not about tailored therapies to cure diseases once they have occurred, but rather to prevent them from occurring in the first place. This focus requires radical patient-centered care and a high touch approach that includes:

  • Reviving the patient-provider relationship model from a bygone era, where the connection and communication between the patient and doctor was a critical aspect of care and healing.
  • Changing the way hospitals are designed and run to better accommodate patients’ needs and create a healing supportive environment.
  • Accommodating patients’ desires to be engaged in their care and customizing care to patients’ needs, values and choices.
  • The inclusion of family and friends as part of the care team.
  • Freely sharing information among the care team, including providers, patients, caregivers and care partners.
  • Acknowledging and leveraging the role the Internet plays in changing the way patients learn about and engage with health and medical information.

High Tech: Data & Genomics

Initiatives like the $130 million Precision Medicine Initiative Cohort Program – part of former President Obama’s precision health initiative being conducted by the National Institute of Health – are changing the way data is collected and used to advance the goals of precision health.

The goal of the initiative is to study the health records of more than 1 million people to learn which individuals respond to certain types of drugs, are at risk for a certain disease, maintain health and fitness, age and die. The anonymous data from all 1 million individuals will be made available to any interested researcher who wants to study one of the largest medical research cohorts ever.

Genomic data combined with other forms of data, including clinical and claims data, will play a major role in precision health. An executive report by IBM entitled Precision Health and Wellness: The Next Step for Population Health Management states, “from an analysis of the projected state of healthcare by 2020, we believe that population health management will converge with precision medicine, which incorporates genomic data to personalize optimal treatments for individual patients, to create an entirely new paradigm in healthcare services.”

In fact, 60% of respondents surveyed for the IBM report said that genomic data tops their needs by 2020, underscoring this predicted intersection between population and precision health.

Leveraging multiple data sources to create smart data that can deliver personalized care to patients will require new collaborations which we are already seeing form between physicians, engineers, computer scientists and business leaders, among others.

Additionally, the continued advancement of private and public health information exchanges will continue to bring together patient data from disparate sources to provide a more holistic view of the patient.

Next Steps

I’m sure we can all agree that healthcare is undergoing a radical transformation. It’s impossible to say where we’ll end up, but it’s clear that healthcare data will be part of our journey. Bringing the patient back into the care model will also be critical if we are to realize the promise of precision health, as data alone will not get us there.

Kim Carlson is Regional Vice President of Business Development at INTELLIMED, a healthcare data analytics company. 

8 Ways Claims Data Supports Population Health
measuring hospital competition
Measuring Hospital Competition: Vital in Value-Based Healthcare
precision health, population health management, healthcare data analytics
Precision Health Will Require Both High Touch and High Tech