There is a huge opportunity to reduce healthcare costs if patients understand the expected costs before receiving services and especially if they can compare providers. One of the many provisions in the Affordable Care Act1 is a requirement that every payer must have a patient cost calculator so that members can get an estimate of their costs before receiving services. Most payers already have some type of patient cost calculator but there are significant variations in the range of estimates and specifics for individual providers.
The best calculators allow the user to compare services (e.g. office visits, MRIs) or episodes (e.g. surgeries that include the surgeon, anesthesia, diagnostic tests and facility charge). For episodes they allow the user to build the total cost by component, adjusting which facility and physician(s) to use. The leading calculators show alternative providers within the service area radius input by the user.
Quality and satisfaction scores are typically integrated with the calculator so that users can make a complete informed decision. This has been the goal of consumer driven health plans and transparency efforts for a long time. There are a few stumbling blocks that are slowing the process and/or making the calculators less effective than they could be:
1) Confidentiality provisions in payer/provider contracts. Historically many contracts between the insurers and the hospital/physician providers had confidentiality provisions. It should be easy to eliminate these but many payers and providers are still resisting the transparency push. Most current calculators are listing some provider cost data as “not available” but they usually list them in such a way that implies they are the highest cost provider. If members are educated on how to use these tools and they are able to reduce their out of pocket costs that will pressure the “confidential” providers to eliminate those regulations. Alternatively, legislation could be passed to eliminate confidentiality provisions.
2) Hard to compare fee schedules and reimbursement arrangements. Most payers still have a variety of fee schedules for their providers so that even if you find out hospital A is 15% more expensive than Hospital B for a CT Scan, it may be that a different procedure could be lower cost at Hospital A. Payers can use the same fee schedule for all providers with varying multiples to allow providers to be high or low cost which will allow the estimates to still be relevant even if other services are performed. Currently most calculators are limited to the most frequent services and episodes, standard fee schedules will allow the calculators to cover every service. See my article on the Transparent Cost Network for more information. http://insight.milliman.com/article.php?cntid=7927
3) Efficiency comparisons for episodes of care. The variation within types of episodes is still very large. It is difficult to account for all the reasons for variation and have enough episodes for each provider to have a good estimate of their efficiency for every type of episode they perform. This is an area that will continue to evolve and improve.
Currently these calculators are not being utilized very frequently. There is a huge opportunity to engage members in the cost of the care they are receiving and educate them on their options. I believe this will have a significant impact on the level of competition among providers and will reduce costs. Alternative benefit design options that leverage reference based pricing can make this information even more effective. That will be the subject of a future article.
Exhibits A and B illustrate service and episode based estimates assuming 20% coinsurance.
Developing health care quality metrics based on administrative claims data has become increasingly common over the past several years. NCQA’s (National Committee for Quality Assurance) HEDIS (Healthcare Effectiveness Data and Information Set) measures have been a standard for health plan quality reporting for over two decades, and more recently, newer programs such as the CMS Pioneer ACO (Accountable Care Organization) program and Oregon CCO (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 EMR (electronic medical record) 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 with 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 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.
As health plans and other organizations prepare for Health Reform – including meeting Health Exchange requirements and preparing and competing for the expansion in government program business – there is a renewed focus on meeting accreditation and quality measurement requirements. These requirements are promulgated by organizations such as CMS and state regulators and administered by the National Committee on Quality Assurance (NCQA), URAC and other auditors that are certified by these government agencies.
For experienced health plans with generously staffed Quality Management departments this is old hat to them. To others – newer health plans, provider-sponsored health plans and community-based organizations – the language of the requirements can be foreign and the work required to meet them can seem ominous.
There tends to be theme songs to some of the key quality regulations and requirements. What data do you have that shows your knowledge of the population? How do you use the data to identify opportunities in the population? And how do you measure that your initiatives have had any impact?
Tools like MedInsight, with its clinical analytic and the risk scoring capabilities can be very effective in addressing the needs of some of these requirements. Most organizations are using these tools to identify preventive care gaps and address those gaps but many are missing opportunities to address other critical accreditation and quality improvement areas.
Some of these opportunities include:
- NCQA Health Plan Quality Improvement Standard 7 – requires the ability to identify members with complex illnesses and comorbidities, establish identification methods and conduct an annual population evaluation to determine the continued validity of those methods.
- NCQA Health Plan Quality Improvement Standard 8 – requires the ability to identify condition populations for disease management and implement a stratification approach for selective intervention.
- CMS Special Needs Plan (SNP) Model of Care (MOC)- requires the ability to define the needs of the population, identify frail and high need members and provide interventions based on analysis of population needs.
MedInsight and the Milliman Advanced Risk Adjuster (MARA) provide the ability to efficiently and consistently identify candidates for case and disease management, identify comorbidities and stratify members for targeted intervention. As quality managers and care management leaders gain access to this information these teams will find strong evidence of their ability to meet these accreditation requirements and a solid source of qualified members who will benefit from their programs.