IntellimedIntellimed

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.

By Shelly Cutrer

The Role of Claims Data in Evolving Telehealth in Healthcare

In a 2017 study published by Health Affairs, commercial claims data on over 300,000 patients from three years (2011-2013) was analyzed to explore patterns of utilization and spending for acute respiratory illnesses.

The study found that while direct-to-consumer telehealth may increase access by making care more available and convenient, it may also increase utilization and healthcare spending.

According to the American Telehealth Association telemedicine offers these four primary benefits:

  • Improving Access: Telehealth brings care to patients in remote areas. It expands the reach for providers to offer care beyond their facilities.
  • Cost Efficiencies: Keeps costs down through better chronic disease management, shared healthcare staffing, reduced travel times and fewer/shorter hospital stays.
  • Improved Quality: Telehealth has come a long way and the quality of telehealth care often equals that on in-person care in many situations.
  • Answers Patients’ Need: Consumers like telemedicine, and it provides both access and answers when and where they need them.

Given these goals and the recent Health Affairs study showing telehealth may not actually reduce costs, how can claims data be of value in both improving access and lowering costs through telehealth?

Patient Outreach & Engagement: Claims data can be used to analyze utilization, physician patterns, geographic trends and more. This can be valuable in creating and informing patient outreach and engagement programs to encourage patients to take a more active role in their healthcare, including proper usage of telehealth programs. While patient engagement is still in its beginning stages, early evidence shows it has huge potential to lower the cost burden on the healthcare system.

Support Value-Based Healthcare: Regardless of what happens with healthcare legislation, the train has left the station when it comes to value-based care. Value-based care focuses on managing rising costs, reducing inefficiencies and redundancies in the system and rewarding providers and healthcare systems on quality over quantity. Claims data has great potential to be leveraged to inform when and where telehealth services should be utilized to support value-based care initiatives.

Big Data: Big data must not only include clinical data, but also claims data along with lab and other data to be truly meaningful for strategic decision making. As hospitals and healthcare systems become more and more advanced in data analytics, big data will be better positioned to inform the proper usage of telehealth to both achieve cost savings and improve access to care.

Given the aging population, physician shortages in many areas and the growing need to manage chronic diseases, telehealth has a lasting role to play in healthcare. However, utilizing it effectively to meet the goals for telehealth and the emerging value-based care environment will be critical and data – including claims data – will be needed.

At Intellimed, we offer claims data analytics solutions that can help inform strategic decisions for telehealth as well as many other areas. To learn more about our solutions or to schedule a demo, please contact us.

Big data and healthcare.

By Ed Willard

Why Utilizing Insurance Claims Data is Necessary for Any Healthcare Strategy Team

Why Utilizing Insurance Claims Data is Necessary for Any Healthcare Strategy Team

Using insurance claims data for strategic healthcare decision making and understanding market dynamics is relatively new to the healthcare market, and it is becoming a necessary part of any strategic planning process. While using claims data in this way can be very valuable, there are some principles to keep in mind to ensure you obtain the most benefit from the data (and avoid the mistakes many organizations have made when pursuing claims data).

How Do You Ensure the Claims Data You Acquire is Actionable?  
Only robust, transparent and detailed claims data is valuable in organizational strategy. The following factors are critical with regard to claims data:

  • Coverage: When exploring non-institutional claims data, the most important factor to consider is whether the data has enough market coverage – at a minimum, it will have 65 to 85 percent coverage. Without at least this level of coverage, you won’t be able to get a holistic view of the market, understand your competitors’ activities or use the data to analyze market dynamics.
  • Transparency: A lack of comparable, transparent healthcare data is an ongoing obstacle for most organizations and extends to claims data as well. When it comes to transparency, claims data should be cleaned and updated frequently as well as managed for duplicates. Additionally, stay away from data providers that don’t offer transparency in types of insurance companies, shared patients, etc. The more transparent the data, the more accurately you can understand the market and, in turn, craft stronger strategic objectives and action plans.
  • Detail: The level of detail in claims data is very important as well, notably for more complex decisions such as increasing market share among specific insurance companies or understanding physician outpatient activity by procedure, by specific payer and by location.

Is Claims Data Alone Enough?

While claims data is a critical part of data-driven decision making, we at INTELLIMED, a healthcare data analytics company, are the first to acknowledge that claims data alone will not provide all of the data needs for strategic decision making or deliver a full picture of the healthcare ecosystem of a city, state or region. Claims data definitely offers a large portion of what is needed, but not all.

By combining claims data, available state discharge data, and demographic data with a healthcare organization’s own data — including information from its electronic medical record (EMR) — claims data can be used to understand what is happening within an organization and within the external environment. The EMR in particular, with its rich information around patient encounters and clinical data, can yield a more detailed view of a patient’s progress through the encounter and his or her status at discharge, while the claims data will provide a holistic view of the patient’s interaction with the healthcare system.

What Can Be Done with Claims Data?

Every healthcare encounter creates a claim for payment from physicians, hospitals, pharmacies and other healthcare providers. There are two ways that claims are submitted and the data collected:

  • UB-04 is the standard billing form used by institutional providers for claim billing. Although it was developed by the Centers for Medicare and Medicaid (CMS), it has become the standard form used by all insurance carriers.
  • CMS-1500 insurance claim form is used for fast professional health care claims submission. The CMS-1500 form is the standard claim form used by a non-institutional providers or suppliers to bill Medicare and commercial carriers. Durable medical equipment providers also use this form to bill regional carriers.

Among the more common uses of external claims data is accessing outpatient market data to understand the connections between doctors, patients and payers beyond the inpatient setting. Other purposes include utilizing the data for physician relations and marketing, including increasing physician market share, facility loyalty and other physician patterns. Using data strategically for both patient- and physician-focused marketing campaigns can yield a positive return on investment.

Additionally, claims data can be mined for important information that has an impact on decisions in many areas, including competitors; service line expansions, decreases or closures; purchase of independent physician practices and clinics; and marketing and pricing strategy, including:

  • Which hospitals have the highest and lowest prices by service line.
  • How far consumers travel for services.
  • Which health plans provide the best discounts and pay the highest by service.
  • Emergency department and outpatient usage among commercial and non-commercial consumers.
  • Utilization patterns of the commercial and non-commercial population.
  • Payer mix by geography, specialty, and procedure, among other factors.

While all healthcare organizations have access to their own internal claims data, there is no publicly available source for competitor claims data, therefore it is essential organizations find trusted partners who have extensive claims data to support strategic decision making. Internal data, along with state discharge data, simply is not enough in today’s increasingly competitive marketplace

A New Data Paradigm
The changes in the healthcare system at all levels triggered by the Affordable Care Act (ACA) have put new emphasis on using claims data to facilitate cost savings at a system-level and for aligning with value-based purchasing initiatives. Claims data can also help to determine whether established clinical and quality safety guidelines are being met. In addition, to achieve the three goals of population health management and analytics: improved outcomes, increased patient safety, and decreased costs – which many organizations have prioritized – combining claims data with clinical data is absolutely essential.

ACA, coupled with the trend of an increasingly active healthcare consumer, has shifted the way healthcare organizations view market share. In fact, developing market share has drastically changed in the last few years. No longer are the days of a volume-based approach focused solely on patients in beds and emergency department usage.

The focus on delivering patient-centered care – one of the “Aims for Improvement” in the Institute of Medicine’s 2001 report, Crossing the Quality Chasm: A New Health System for the 21st Century – is shaping a new paradigm around market share and using data for strategic decision making. Factors such a facility convenience, online reputation, facility and physician ratings, and other variables are all influencing consumer choice. Organizations must market as much to the consumer as to the physician. Many healthcare organizations are looking to the best practices of retail marketing to reshape their consumer interactions.

Using data – including robust, transparent and detailed claims data – will allow healthcare organizations to be aware of the elements that have an impact on their market, a critical factor in organizational strategy and decision making. Such an approach will allow healthcare organizations to evolve with the new landscape and set the course for where they wish to be in the near as well as more distant future.

Ed Willard serves as INTELLIMED’s Executive Director of Business Development and is a member of the INTELLIMED leadership team. In his free time, he enjoys soccer and is involved in several local soccer organizations.

Healthcare big data

By Bill Goodwin

Three Tips to Prevent Big Data From Causing Big Problems

Three Tips to Prevent Big Data From Causing Big Problems

If you are like most leaders in business, you hear the words “Big Data” being used in promotions, internal meetings, vendor presentations and more. Big data – to an increasing extent – has become synonymous with “we can help you find the answers you need and improve profits.”

And, there is some truth in this statement. According to the International Institute for Analytics, businesses that use data will see $430 billion in productivity benefits over their competition not using data. Forrester predicts that real-time streaming insights into big data will be the hallmarks of data winners going forward. Without a doubt, data can help us make better more informed decisions. However, it is possible to over-rely on big data as a panacea for answers to complex healthcare business decisions.

Countless times I have been in meetings with vendors, internal personnel and clients where healthcare big data is mentioned in some form or another as being the solution to helping (better yet telling) them what to do. The competitive pressure in all markets today forces individuals to make decisions faster and more accurately, so the appeal of fresh insights from new clinical data analysis becomes extremely appealing.

In many ways, tapping into healthcare big data analytics can help, but all of us should be extremely careful about placing too much stock in there always being clear, action-oriented and effective go-forward strategies to be found in big data. In fact, I have been in front of many prospective clients over the last few years and they mention, albeit sometimes reluctantly, that they have been burned by previous companies who offer data-driven tools designed to provide answers they had previously been unable to find. So not only has big data in hospitals been marketed as the solution, it has also started to develop a reputation as being overrated.

Big data, more accurately described, is a general, all-inclusive term for a variety of complex data collection, processing and analysis generation that traditional applications are unable to handle. There is no question the accumulation and analysis of new data can be helpful to every organization. However, take ten organizations in the same industry that have the same big data inputs and I guarantee all will come out with different conclusions on what they should do next. Seems logical, yes, but how do you ensure your organization is not one of the ones that makes a critical misstep?

While big data can certainly provide critical insights for healthcare decision makers, we must approach big data cautiously and through a measured perspective. Here are three important considerations with regard to big data to leverage immediately:

  • Be Aware of Personal and Internal Bias
    All analyses have some level of personal or organizational bias. Whether it is how the analyses are set up or how the information is interpreted, it is essential to a) know there will always be bias and b) try to factor that out in the final interpretation. In other words, while there is no way to fully eliminate bias, you can be aware of what it is and modify your interpretation accordingly. Most leaders are aware that there is bias in all analyses, yet some continue to make decisions without taking bias into consideration.
  • Understand Correlation Versus Causation
    I have seen too many leaders make detrimental decisions when they mistake correlation with causation. High positive or high negative correlation does not always mean causation, whether one is looking at two variables or a multi-variable analysis. Be sure to factor this in before any major decisions are made. Dig deeper, look at the problem or opportunity from more angles and get other viewpoints before settling in on the final decision.
  • Tap Into Your Intuition
    In this new data-driven society, we are becoming so data dependent that using intuition is becoming a thing of the past. In fact, I would argue the art of intuition has been lost in many organizations. We have all had situations where the “data” told us to turn left, but our gut told us to turn right. And, how many times did all of us turn left due to an analyses only to find out right was the better direction? Intuition is essential to any decision-making process and simply cannot be excluded. If you don’t have a good feel for the decision you need to make, rely on data more. If your intuition is telling you what decision you should make, pay more attention to it, regardless of what the analysis suggests.

Some data experts predict that we have already begun to move away from the era of big data in favor of “fast data” and “actionable data,” noting that most businesses don’t use a fraction of the data they have access to and should focus on asking the right questions to make the best use of data, big or otherwise. Certainly these data analytics changes will enable us to continue to enhance our insights and subsequent decisions.

With the continual advancement of how we access and analyze big data, it’s hard to argue that it’s not a necessary component of healthcare decision making – but it’s not the only factor. Regardless of the direction that big data goes, coupling the knowledge gained from data with our experience and intuition – and knowing when to favor one over the other – will become increasingly important in our complex healthcare landscape.

Bill Goodwin is CEO of INTELLIMED, a leading healthcare analytics company.

8 Ways Claims Data Supports Population Health
The Role of Claims Data in Evolving Telehealth in Healthcare
Big data and healthcare.
Why Utilizing Insurance Claims Data is Necessary for Any Healthcare Strategy Team
Healthcare big data
Three Tips to Prevent Big Data From Causing Big Problems