The following is an excerpt from a complete white paper by Singletrack Analytics on analytics for ACOs. The download the entire whitepaper, click here (pdf).
With the passage of the health reform legislation, many healthcare organizations are rapidly planning to form accountable care organizations before the implementation date of January 1, 2012. The primary focus of these efforts appears to be on attracting primary care physicians into the provider network, creating the network structure, facilitating communication among physicians and many other organizational tasks.
Missing from many of these efforts is a structured approach to designing an analytic infrastructure to obtain, consolidate, and analyze data about the patient population demographics, utilization, risk scores, costs and other factors that will determine the financial success of the organization. These analytics are particularly important in the formative stages of an ACO, when primary care physicians are often recruited without regard to their patient mix, risk scores or costs. Understanding these characteristics about the provider network in its formative stages will provide ACOs with a clearer picture of what to expect when the ACO becomes operational. Many provider organizations had unsuccessful experiences with managed care in the 1990s because of poor analytics, and will be unwilling to repeat the experiment without significantly enhancing their ability to obtain and analyze data.
Challenges for ACOs
Accountable care organizations create significant new challenges for healthcare financial managers. Setting aside the largely unknown regulatory nature of these entities, it’s clear that their organizers must understand details of payment for different provider types, the system to be used by CMS to develop targets for bonus payments, and other similar challenges. The entire financial structure of the ACO will move from being based on services by providers to a whole new population-based system closer to the business model of an insurance company. This new business model will require significantly different data systems, analytics, metrics, processes, knowledge, expertise and a whole host of other new concerns.
Analytics are critical in the formative stages of an ACO, and also in ongoing operations. The initial task is the projection of the financial results of the organization. This is part of the "due diligence" process of any undertaking of this magnitude, and will likely involve the use of imperfect but best available data assembled from several sources to provide the most accurate possible overview of the patient population, provider cost and referral patterns, and other significant information. On an ongoing basis, more accurate data may be available from CMS, which will allow an operational ACO to monitor the important metrics necessary to its financial success.
While Medicare ACOs are the primary driver of many of these initiatives, ACOs may also utilize their infrastructure and provider networks to contract with commercial payers. The issues discussed here are applicable to these arrangements, although the revenue for these contracts may differ from the risk-based targets expected to be used for Medicare.
This article provides a structure for developing financial analytics for ACOs, and the data systems necessary to provide the underlying data for these analytics. It does not address the quality related issues and measurements that will be also essential to the success of these organizations, since those issues have been addressed in detail in other publications.
Competing on analytics
In their often-cited book “Competing on Analytics – The New Science of Winning”, authors Davenport and Harris describe organizations that utilize their enhanced analytical ability to give them a competitive edge in the marketplace. They cover the process necessary to identify critical factors to an organization’s success, quantify those factors, and develop systems to measure and act on the results. They underscore the need to develop an enterprise-wide approach to analytics that creates common definitions of important terms, avoid the silos of information each containing its own version of the truth, create a centralized business intelligence competency center, and promote the senior management commitment necessary to make this process work.
These techniques are particularly applicable to ACOs, which are replete with data, but which comes from many sources with no common champion, skill set or knowledge base. The ability of the ACO’s management to create financial success in these ventures will be highly dependent on its ability to access current, correct and consistent information. Organizations having this ability will be able to use it as a competitive advantage over those organizations lacking centralized data structures and analytical capabilities. Data should be viewed as a strategic asset that is critical to the success of the organization.
Is the deck already stacked against you?
A major contributor to the interest in ACO development is the opportunity to earn payments from CMS if the provider payments for the ACOs population are lower than the targets computed by CMS. Although the methodology for computing the targets has not yet been published, it is expected to use a risk adjustment system similar to those used for Medicare Advantage plans, as described below.
No such system can be unbiased towards all participants, and any system will inevitably favor some participants over others. In the Medicare Physician Group Practice (PGP) demonstration project, cost and utilization targets for participating physician groups were based on the demographics of their patient populations. Some groups beat those targets and received significant payments from CMS for doing so. Interestingly enough, it was unclear whether the "winning" groups succeeded because of their efforts during the demonstration, or if they went into the demonstration having a clear advantage, either because of lower initial costs or because of some characteristic of the target-setting process. In any case, it would be useful for an ACO to know whether the cards were stacked in its favor before deciding to participate.
6 key steps to implement effective analytics within ACOs
A comprehensive plan will be critical to the ability of an ACO to implement an effective analytics strategy. Key steps in such a plan are listed below:
A critical success factor for accountable care organizations will be their ability to obtain, analyze and act on cost and utilization data. Critical decisions regarding PCP selection and management, financial projections, risk score management and IBNR estimation depend on obtaining and analyzing data generated within the ACO. The ability to quickly understand what’s happening within the ACO and react to the new challenges of the integrated care model will be critical to the organizations’ success.
Singletrack Analytics is a healthcare financial and data consulting firm specializing in assisting healthcare providers and purchasers achieve success through better use of data and analytic techniques. For information about Singletrack Analytics, please visit www.singletrackanalytics.com or email at info@singletrackanalytics.com.
With the passage of the health reform legislation, many healthcare organizations are rapidly planning to form accountable care organizations before the implementation date of January 1, 2012. The primary focus of these efforts appears to be on attracting primary care physicians into the provider network, creating the network structure, facilitating communication among physicians and many other organizational tasks.
Missing from many of these efforts is a structured approach to designing an analytic infrastructure to obtain, consolidate, and analyze data about the patient population demographics, utilization, risk scores, costs and other factors that will determine the financial success of the organization. These analytics are particularly important in the formative stages of an ACO, when primary care physicians are often recruited without regard to their patient mix, risk scores or costs. Understanding these characteristics about the provider network in its formative stages will provide ACOs with a clearer picture of what to expect when the ACO becomes operational. Many provider organizations had unsuccessful experiences with managed care in the 1990s because of poor analytics, and will be unwilling to repeat the experiment without significantly enhancing their ability to obtain and analyze data.
Challenges for ACOs
Accountable care organizations create significant new challenges for healthcare financial managers. Setting aside the largely unknown regulatory nature of these entities, it’s clear that their organizers must understand details of payment for different provider types, the system to be used by CMS to develop targets for bonus payments, and other similar challenges. The entire financial structure of the ACO will move from being based on services by providers to a whole new population-based system closer to the business model of an insurance company. This new business model will require significantly different data systems, analytics, metrics, processes, knowledge, expertise and a whole host of other new concerns.
Analytics are critical in the formative stages of an ACO, and also in ongoing operations. The initial task is the projection of the financial results of the organization. This is part of the "due diligence" process of any undertaking of this magnitude, and will likely involve the use of imperfect but best available data assembled from several sources to provide the most accurate possible overview of the patient population, provider cost and referral patterns, and other significant information. On an ongoing basis, more accurate data may be available from CMS, which will allow an operational ACO to monitor the important metrics necessary to its financial success.
While Medicare ACOs are the primary driver of many of these initiatives, ACOs may also utilize their infrastructure and provider networks to contract with commercial payers. The issues discussed here are applicable to these arrangements, although the revenue for these contracts may differ from the risk-based targets expected to be used for Medicare.
This article provides a structure for developing financial analytics for ACOs, and the data systems necessary to provide the underlying data for these analytics. It does not address the quality related issues and measurements that will be also essential to the success of these organizations, since those issues have been addressed in detail in other publications.
Competing on analytics
In their often-cited book “Competing on Analytics – The New Science of Winning”, authors Davenport and Harris describe organizations that utilize their enhanced analytical ability to give them a competitive edge in the marketplace. They cover the process necessary to identify critical factors to an organization’s success, quantify those factors, and develop systems to measure and act on the results. They underscore the need to develop an enterprise-wide approach to analytics that creates common definitions of important terms, avoid the silos of information each containing its own version of the truth, create a centralized business intelligence competency center, and promote the senior management commitment necessary to make this process work.
These techniques are particularly applicable to ACOs, which are replete with data, but which comes from many sources with no common champion, skill set or knowledge base. The ability of the ACO’s management to create financial success in these ventures will be highly dependent on its ability to access current, correct and consistent information. Organizations having this ability will be able to use it as a competitive advantage over those organizations lacking centralized data structures and analytical capabilities. Data should be viewed as a strategic asset that is critical to the success of the organization.
Is the deck already stacked against you?
A major contributor to the interest in ACO development is the opportunity to earn payments from CMS if the provider payments for the ACOs population are lower than the targets computed by CMS. Although the methodology for computing the targets has not yet been published, it is expected to use a risk adjustment system similar to those used for Medicare Advantage plans, as described below.
No such system can be unbiased towards all participants, and any system will inevitably favor some participants over others. In the Medicare Physician Group Practice (PGP) demonstration project, cost and utilization targets for participating physician groups were based on the demographics of their patient populations. Some groups beat those targets and received significant payments from CMS for doing so. Interestingly enough, it was unclear whether the "winning" groups succeeded because of their efforts during the demonstration, or if they went into the demonstration having a clear advantage, either because of lower initial costs or because of some characteristic of the target-setting process. In any case, it would be useful for an ACO to know whether the cards were stacked in its favor before deciding to participate.
6 key steps to implement effective analytics within ACOs
A comprehensive plan will be critical to the ability of an ACO to implement an effective analytics strategy. Key steps in such a plan are listed below:
- Understand all factors affecting success the ACO contract. The major factors include medical costs, risk scoring, out-of-network utilization and others described later in this document.
- Determine the data elements that measure those factors. Data elements describe the pieces of raw data, arising from different IT systems, which will be required to measure these factors. These include consistent patient and provider identifiers, diagnosis and procedure codes, payment amounts for services provided, service and payment dates, and others.
- Identify the sources of those data elements within the extended healthcare system. These data elements are likely to reside in a variety of hospital and physician billing systems, pharmacy claims, cost accounting data, as well as in external processes that may be necessary to compute risk assessment scores, incurred but not reported claims (IBNR) allocations and others.
- Design a process to obtain the data and transform it into a usable form. Once the data sources are identified, processes must be developed extract the data from its source system, transform it into a format usable for analytics, and load it into a centralized data repository from which analytics and reports can be developed. This “ETL” process is critical in maintaining the accuracy and consistency of data.
- Identify the specific metrics that will be necessary to measure success in this area. Once the data has been consolidated into a single location, specific metrics can be defined to quantify and measure the critical success factors identified above. These metrics may use data from multiple sources; for example identifications of common drugs used to treat various diagnoses may require merging medical and pharmacy claims. These metrics may be arranged into a manageable structure (possibly hierarchal) that allow quick review of high level data while facilitating drilling down into problem areas using a systematic and predefined structure.
- Develop the internal management processes to review the data and take action based on the results. The greatest reports in the world won’t help if nobody looks at them. Reports, dashboards and analysis tools must reflect the user’s objectives, and convey the results in a useful way.
A critical success factor for accountable care organizations will be their ability to obtain, analyze and act on cost and utilization data. Critical decisions regarding PCP selection and management, financial projections, risk score management and IBNR estimation depend on obtaining and analyzing data generated within the ACO. The ability to quickly understand what’s happening within the ACO and react to the new challenges of the integrated care model will be critical to the organizations’ success.
Singletrack Analytics is a healthcare financial and data consulting firm specializing in assisting healthcare providers and purchasers achieve success through better use of data and analytic techniques. For information about Singletrack Analytics, please visit www.singletrackanalytics.com or email at info@singletrackanalytics.com.