Healthcare Town Hall

Developing healthcare quality metrics based on administrative claims data has become increasingly common over the past several years. The National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) measures have been a standard for health plan quality reporting for over two decades, and more recently, newer programs such as the Centers for Medicare & Medicaid Services Pioneer ACO (accountable care organization) program and Oregon Coordinated Care Organization program have included claims based quality measures as requirements for program participation.

Most claims-based measures are process based, evaluating if appropriate services are provided for specified groups of patients, or identifying potential over-utilization of services, but claims data are not the sole source of quality measurement. Survey data are often used for patient satisfaction and operational measures, and there is increasing use of lab results and electronic medical record (EMR) data to expand the clinical components of quality that can be measured – a topic for another posting.

Despite the expansion of claims-based quality measures, some still question the merit of these measures. Those citing concerns point out known limitations associated with analyzing claims data including:

• Potential errors or inconsistencies in coding.
• Availability of required data sources may be constrained if components of benefits are administered by multiple sources.
• Lack of complete clinical information.
• No diagnostic coding for blood pressure, laboratory results or pathology results.
• Clinical information is limited to conditions for which the patient was treated and submitted a claim. A noncompliant diabetic may have no claim history of the disease.
• Timeliness of data is impacted by claim lag.

However, the advantages of analyzing claims data greatly outweigh the limitations noted above. The advantages include:

• Data are commonly available and relatively inexpensive to analyze.
• Data are available for very large populations, allowing for more robust sample sizes.
• Coding accuracy has improved dramatically over the past 20 years, and
• For some types of measures, claims may produce a more accurate picture than even chart reviews.

An example of this last point would be measures focusing on patient compliance with medications. A physician may regularly write refill prescriptions for a patient’s hypertension medication, and those refills may be well documented in the patient’s chart, but those data provide no real evidence that the patient filled those prescriptions. Tracking actual claims for prescription refills is a much better measure. Granted, submitting a claim for a hypertension medication does not prove that the patient actually took the medication at the appropriate frequency, but a regular, on-going refill pattern is a better proxy of medication adherence than chart review information.

Days supplied is commonly available on claims data making it easy to calculate “possession ratios” to monitor patient compliance from pharmacy claims. A simplistic way (additional conditions can be added to the calculation) to measure possession ratios is demonstrated in table 1. For patients continuously enrolled during a 180 day period and previously diagnosed with hypertension, the possession ratio for each patient is the sum of all days supplied on their prescriptions during the study period, divided by 180 days.

Although claims data are not perfect for clinical reporting, they will continue to be a valuable and important source of data for quality reporting for a selected set of metrics.

This article first appeared at Milliman MedInsight.

Electronic Health Records Doug Bates, Drug Utilization, healthcare analytics, MedInsight, qualified reporting

Changes in reimbursement frameworks have increased the need for doctors to access meaningful, timely physician profile reporting.

There are many variations in what gets presented in physician profile reporting as well as how and when it is delivered. In July 2012, the American Medical Association launched their Physician Reporting Guidelines in an effort to provide a physician perspective in profile reporting. The guidelines called for the need to be easy to understand, easy to access and provide an opportunity for review of detailed data. While the AMA reporting guidelines also called for standardized reporting, that remains a significant work in progress.

Technology and data availability continue to improve and have given rise to sharing information through provider or physician portals. There is a great deal of work on the front-end to gather data, select measures, and validate all this data before release. Today, we scratch the surface of physician portals and share some examples and considerations in the design of the portal.

The critical elements in the design and implementation of a physician portal require early inclusion of physicians in the planning and design process. In accordance with the AMA Reporting Guidelines, it should also address ease of use, availability of actionable information and drill to their member detail.

Dashboards are a popular method for sharing information through a portal. They provide for an accessible, visual, and portable look for physicians. This type of display can begin at a higher level such as the medical group and then allow for drill to individual providers and then to their members. Depending on the tool and data sources, the provider portal can include claims and EMR data reporting. Retrospective data is very valuable in providing insights into opportunities for change. The examples below are claims based and allow for review of the retrospective performance as well as provide transparency and insight into prospective issues, using risk assessment results and other mainstream methodologies.

Lastly, providing the option to submit feedback is a critical element for provider portals. Through the portal, the physician can be given access to submit requests to update or revise the data, and related results, based on additional information. The workflow of this process should be a significant consideration in the planning process to define standard reasons.

There is a great deal of planning involved in determining the specific metrics and the data visualization option but with early physician engagement and thorough data governance rules, the physician portal is a valuable tool in broadening the engagement of physicians.

This article first appeared at Milliman MedInsight.

Electronic Health Records Barb Ward, MedInsight, provider profiling, qualified reporting

In 2010 we highlighted an electronic health records (EHR) analysis conducted by Software Advice. The company has recently updated their EHR market share statistics.

According to the report, 115,918 eligible professionals (EPs)—physicians and other health professionals—have successfully qualified for Stage 1 Meaningful Use EHR incentives according to the criteria of the Centers for Medicare and Medicaid Services (CMS).

Also, the Office of the National Coordinator for Health Information Technology (ONC-HIT) lists 623 software vendors that provide solutions to EPs that can be eligible for incentives under the American Recovery and Reinvestment Act. However, the report shows only 387 of those vendors were found in the CMS data.

The entire report can be accessed here.

For Milliman’s perspective on EHR, click here.

Electronic Health Records EHR

With the interest in establishing all payor claims databases (APCDs) continuing to grow (currently APCDs exist, are being developed, or are being contemplated in over 25 states), the need to standardize the data collection component of APCDs is now much greater. In this context, the term standardization means that states and substate entities collecting APCD data would do so in the same manner (i.e., identical file structures, data elements, data type, positioning, lengths, and code sets). Also, because individual states and substate entities will need to collect some data elements that are unique, standardization must also include a uniform process, which involves both data submitters and data collectors, for modifying the accepted file structure.

While the states of Maine, Massachusetts, Minnesota, New Hampshire, Tennessee, and Vermont have very similar data collection requirements, differences do exist, and Maryland, Utah, and Oregon, even with many of the data elements collected identical to those collected by the six states listed above, have considerably different data file formats. This nonuniform approach to developing APCDs has resulted in increased costs to all stakeholders: states and other substate entities using “one-off” data collection systems cannot easily adopt or leverage the advancements made by those employing a more standardized format, resulting in increased costs. Because different extracts must be created for each data collection entity, costs for payors submitting data, especially for payors operating in multiple states, are significantly higher than the ideal. The ability of data users (including federal and state government agencies) to share analyses and applications across states is more complicated, resulting in additional analytic costs to normalize the data.

Read more…

Electronic Health Records Al Prysunka, APCD, EHR, MedInsight

Many organizations struggle with the business decision of whether to build their own healthcare data warehouse and decision support solution or to license a solution from an organization that specializes in healthcare data analytics.

Most healthcare payor organizations have some form of an operational data store (ODS) that serves as a storage site for their claims adjudication system data. The temptation to expand the ODS data sources to include non-claims adjudication system data sources and layer data tools on top is strong. But is this the right course of action? Should the ODS be transformed into a full healthcare business intelligence (BI) solution? If not, what role should a third-party BI solution play?

In my opinion, the answer is a bit of both—an ODS for operational reporting and a third-party business intelligence tool for cutting edge business analytics. I postulate that the following structure optimizes the strengths of both models:

1. The payor organization builds and maintains an ODS that has frequent (or real-time) updates of claims adjudication system data. The data is subjected to little or no transformation or enhancement.
2. The ODS has a limited number of “operational” reports written against it. The defining metric on whether a report should be written against the ODS is the “currency” of the data. If you need a near real-time list of open claims, this is a report that should populate from the ODS.
3. Layer on top of the ODS a “best in class” healthcare decision support system. This system is typically characterized by periodic data updates, typically monthly, and a number of advanced data analytic enhancements. Enhancements include methods such as risk scores, service classification grouping, episodes of care, quality metrics, completion factors, attribution methods, benchmarks, etc.
4. Included as part of the decision support system are user tools such as dashboards, online analytical processing (OLAP) cubes, standard reports, and user portals. These different interfaces provide for access to a wide variety of users in the organization.
5. The decision support system also allows for a wide variant of different data sources to be combined together in a standardized format. By combining data from pharmacy benefit managers, third-party carve-outs such as vision or mental health, lab results, wellness programs, or even administrative data, powerful new analysis can be accomplished.

Where do you draw the analytic line between your ODS and BI solution?

Electronic Health Records, Quality of Care Brian Studebaker, business intelligence, EHR, healthcare analytics, MedInsight, operational data store

All Payer Claims Databases (APCDs) currently exist in 10 states, with another 12 in the development stage. In addition, there are a number of non-state government claims databases that exist throughout the country. As APCDs are being created, the next step in the progression of obtaining more comprehensive, timely, and accurate healthcare data is to link the APCDs with other databases. The three databases most commonly discussed include two historical databases (hospital administrative and vital statistics) and data from the newly created health information exchanges (HIEs), which will be the focus of this discussion.

HIEs have the potential to enhance existing APCDs with clinical information for quality and outcomes reporting. Because of their content and purpose, HIEs and APCDs will be distinctly separate initiatives as they are developed. If both are integrated, data will exist for comparative effective research, population health applications, and to improve risk adjustment, clinical studies, and outcomes research.

With the rollout of electronic health records with the Health Information Technology for Economic and Clinical Health Act (HITECH) and the establishment of federal grants to expand health information technology (e.g., American Recovery and Reinvestment Act State Grants to Promote Health Information Technology), which greatly augmented existing private initiatives, HIEs now can be found in some form in every state in the United States. Simultaneously, a number of federal initiatives associated with, or driven by, the Patient Protection and Affordable Care Act (e.g., creation of accountable care organizations, patient-centered medical homes, and the Multi-Payer Advanced Primary Care Practice Demonstration pilots and Performance Measurement pilots funded by the Centers for Medicare and Medicaid Services) have elevated the discussion regarding the integration of administrative data derived from APCDs with clinical data generated by HIEs to a new level.

However, while the linkage of claims data with clinical data has taken on a greater significance, overlapping technical and legal impediments exist that may prevent the successful merger of these two robust data sources.

Read more…

Electronic Health Records Al Prysunka, APCD, Healthcare Reform, MedInsight

As payment reform continues to progress and medical inflation continues to escalate, many payers and providers are improving their analytics around the hospital contracting process. Milliman MedInsight would like to make you aware of a Milliman tool that can help healthcare organizations better evaluate and manage commercial contracts. Case mix and severity adjustment of each hospital’s results is critical to answering the age old assertion that “our patients are sicker.” Having a set of reports that allows you to aggregate inpatient and outpatient data to evaluate trends, relative contractual yields, operating costs, percent of Medicare, and LOS performance is critical to getting the best contracts.

Will Fox, a principal in Milliman’s Seattle office recently presented a webinar entitled “Managing hospital contract negotiations using case mix adjusted benchmarks.” During this webinar, Mr. Fox discussed the Hospital Evaluation and Comparison System (HECS™), which combines the power of the Milliman RBRVS™ for Hospitals and a wide variety of benchmarks that can inform and improve your contracting results.

If you missed the webinar, you can access a recording of the Managing Hospital Contract Negotiations Using Case Mix Adjusted Benchmarks session. To listen to the webinar, please click on the “Register” button, enter the requested information, and click “Complete Registration”. The webinar will then automatically stream to your computer.

You can also read this informative white paper, Milliman RBRVS for Hospitals.

This article was first published at Milliman MedInsight.

Cost, Electronic Health Records benchmarking, EHR vs cost, Healthcare Reform, MedInsight, Will Fox

The U.S. Department of Health and Human Services (HHS) announced the next steps in the president’s effort to help doctors and hospitals use electronic health records. Here is an excerpt from the HHS’ news release:

Today, HHS’ Centers for Medicare & Medicaid Services and HHS’ Office of the National Coordinator for Health IT released final requirements for stage 2 that hospitals and health care providers must meet in order to qualify for incentives during the second stage of the program, and criteria that electronic health records must meet to achieve certification.

The requirements announced today:

• Make clear that stage two of the program will begin as early as 2014. No providers will be required to follow the Stage 2 requirements outlined today before 2014.
• Outline the certification criteria for the certification of EHR technology, so eligible professionals and hospitals may be assured that the systems they use will work, help them meaningfully use health information technology, and qualify for incentive payments.
• Modify the certification program to cut red tape and make the certification process more efficient.
• Allow current “2011 Edition Certified EHR Technology” to be used until 2014.

For Milliman’s perspective on electronic health records, click here.

Electronic Health Records EHR, Electronic Health Records

The National Bureau of Economic Research published a new paper (payment may be required) examining the “relationship between the adoption of electronic medical records (EMR) and hospital operating costs at thousands of US hospitals between 1996 and 2009.”

Here is an excerpt from the study’s abstract:

We find evidence consistent with this approach, namely, that: (1) EMR adoption is initially associated with a rise in costs; (2) EMR adoption at hospitals in favorable conditions – such as urban locations – leads to a decrease in costs after three years; and (3) Hospitals in unfavorable conditions experience a sharp increase in costs even after six years.

“Unfavorable conditions” include greater distance from information technology hubs, meaning that rural hospitals are less likely to see a short-term return on their EMR/EHR investment.

Today’s most competitive healthcare organizations are data-driven. Brian Studebaker and Roger Connolly provide perspective into innovative business intelligence (BI)—the process of turning data into actionable information… Read their paper on getting a return on investment out of business intelligence here.

Click on electronic health records for more insights.

Electronic Health Records Brian Studebaker, business intelligence, EHR, Roger Connolly, ROI

We’d like to welcome a new blog to the Milliman family: MedInsight, our healthcare analytics platform, just launched a blog today. This is a great venue for anyone interested in the art and science of healthcare analytics.

You’ll find the new blog here. We have a content partnership with the new blog and will be featuring highlights on Healthcare Town Hall from time to time.

Electronic Health Records MedInsight