Patent Application
20170372238
The present invention relates to Accountable Care Organizations (ACOs), and more particularly, to a systematic data-driven approach for building an ACO.
In recent years, there has been an increasing emphasis on enabling value-driven healthcare, aimed at improving outcomes, lowering costs, and increasing overall access to care for patients. A prominent example of value-driven delivery systems is the formation of patient-centric Accountable Care Organizations (ACOs).
ACOs are groups of physicians, facilities, and other healthcare providers who come together to provide coordinated care to their patients. Providers belonging to these naturally-occurring preexisting networks may be more ready to be accountable for managing the health of a population by sharing the risks and benefits of being part of a shared savings program.
Thus, effective techniques for the identification of naturally-occurring ACOs are needed in transitioning a healthcare care system from fee-for-service to valued-based reimbursement.
The present invention provides a systematic data-driven approach for building an Accountable Care Organization (ACO). In one aspect of the invention, a method for forming an ACO is provided. The method includes: determining groups of healthcare providers that have x number of patients in common; detecting communities in the groups using a recursive community detection process; ranking contractual organizations of the healthcare providers based on how well the contractual organizations represent the communities; and making recommendations for the contractual organizations to include in the ACO based on the ranking.
In another aspect of the invention, a system for forming an ACO is provided. The system includes a recommender engine that is configured to: determine groups of healthcare providers that have x number of patients in common; detect communities in the groups using a recursive community detection process; rank contractual organizations of the healthcare providers based on how well the contractual organizations represent the communities; and make recommendations for the contractual organizations to include in the ACO based on the ranking.
A more complete understanding of the present invention, as well as further features and advantages of the present invention, will be obtained by reference to the following detailed description and drawings.
As provided above, the identification of naturally-occurring accountable care organizations (ACOs) is an important step in transitioning a healthcare care system from fee-for-service to valued-based reimbursement. Provided herein is a systematic data-driven approach that discovers provider communities with promising features of high performing ACOs, e.g., high in-community utilization in both visit frequency and costs.
The definitions of some terms/abbreviations used throughout the present description are now provided. NPI refers to a national provider identifier (e.g., physicians, specialists, etc.). With the present techniques, NPIs are not used per se since any healthcare provider identification is sufficient. Each “contractual organization” (or simply “organization”), also referred to herein as a “site,” is composed of one or more NPIs. Each ACO is composed of one or more contractual organizations which share responsibility for outcomes of a set of patients (“shared savings” and “value-based payment” are synonymous terms which can be used to refer to the contracts that can be formed in accordance with the present techniques). To use a simple example, in the typical healthcare context a patient is treated by a physician. The physician, an NPI, can be part of at least one contractual organization (e.g., a physician can work in private practice, as well as for a hospital, etc. and thus may be part of more than one organization). The contractual organization can in turn be part of at least one ACO. Thus, the ACO is essentially a list of healthcare providers, each having an association with at least one organization/site. A goal of the present techniques is to produce a ranked list of organizations/sites to add to an ACO as an output of the present process. This list of organizations/sites can then be used by entities, such as insurance companies, to help them determine which organizations/sites (i.e., based on the ranking) the entities should write contracts with in the interest of value-based healthcare.
Namely, as will be described in detail below, the present process starts with insurance claims data, and observes their effects on the ACO with visualization. For instance, it can first be determined which healthcare providers belong to pre-existing informal networks of shared patients (e.g., a graph of NPIs can be constructed from health insurance claims data based on shared patients in order to apply community detection methods). Next, organization-level relationships can be found based on individual NPI relationships. Provider communities of a specified appropriate size with a specified amount of shared patient care can be created. The resulting ACOs improve coordination of care by promoting shared responsibility for the outcomes of a set of patients.
A detailed description of the present techniques is now provided by way of reference to
Referring to
One can visualize this collaboration relationship as shown in
Namely, referring back to methodology 100 of
According to an exemplary embodiment, some additional constraints are imposed on the community detection process. For instance, one constraint might be that each community size has to be at least x number (i.e., each community has to contain at least x number of healthcare providers). In that manner, one can derive communities of a meaningful size rather than simply groups of nominally few individuals. Another relevant constraint might be a certain ratio of specialists to primary healthcare providers. A primary care provider is often the first contact for a patient, and the provider who will provide continued care for the patient. During their treatment, primary care providers might refer the patient to a specialist for various specialized care. The desired ratio of specialists to primary healthcare providers can vary depending on the situation and objectives of the patient. For instance, for many patients a low ratio of specialists to primary healthcare providers might be preferable since it means that the patient will have more primary physicians to pick from. On the other hand, for a patient with special requirements, a higher ratio of specialists to primary healthcare providers might be preferable to have a greater range of specialists. According to an exemplary embodiment, this ratio of specialists to primary healthcare providers is applied as a range specification (e.g., a ratio of specialists to primary healthcare providers of from X value to Y value).
Yet another relevant constraint might be in-community utilization. In-community utilization looks at, of all the services a patient has received, what percentage of the care happened in the patient's assigned community. A high in-community utilization means that the community has been designed well, meeting all the needs of the patient. Again, this constraint can be applied as a range specification (e.g., a percentage of in-community care of from X % to Y %).
One can visualize this recursive partitioning step using the community detection algorithm as shown in
Up to this point in the process, the analysis has been at the provider level, meaning that (as described in detail above) it has focused on the healthcare providers themselves and the patients they share in common. However, a given healthcare provider can practice at multiple sites, also referred to herein as contractual organizations (see above). For instance, a physician can be in private practice, but can also treat patients at a hospital and/or at a clinic. Thus, a more comprehensive understanding of value-based healthcare can be achieved by next evaluating the groups (communities) that were created in step 104 (via the community detection process) at the contractual organization level. For instance, an NPI-to-contractual organization database (specifying which healthcare providers are associated with each contractual organization/site—for example a list of physicians who practice at a given hospital, clinic, etc.) can be used to link the healthcare providers in the groups/communities from step 104 to one or more contractual organizations. See, e.g.,
The ultimate goal will be to determine, based on this NPI to organization association, which contractual organizations to include in the ACO (i.e., this will be done at the organization-level) so as to best represent the detected communities from step 104. As provided above, the ACO is essentially a list of healthcare providers. Further, since the process begins (as described above) with claims data—which has only healthcare provider-level information—then up through step 104 the process represents the resulting ACO as a list of healthcare providers in the detected communities. Another way to look at it is that the ACO of healthcare providers based just on the raw claims data represents an initial/raw ACO of the healthcare providers. After obtaining the raw ACO as a list of healthcare providers, the next task will be to distill it, as accurately as possible, the final ACO (at the organization/site level). Thus, since the goal is to write contracts at the site/organization-level, the ACO must be approximated at this stage (e.g., based on the list of healthcare providers in the communities detected in step 104—i.e., the raw ACO), and one way of measuring the performance of the approximation is by looking at its F-score.
For instance, if one were to evaluate contractual organizations for inclusion in the ACO based simply on the provider communities (determined in step 104) and those providers' associations with contractual organizations, then some contractual organization might be over-represented while others are under-represented. This is because, as provided above, healthcare providers can practice at multiple sites. To use a simple example to illustrate this concept, if one healthcare provider in the group (Physician A) practices at multiple sites (e.g., at multiple hospitals and/or clinics in addition to a private practice) and another healthcare provider in the group (Physician B) has only a private practice and does not go to any hospitals, then the sites/organizations contracted with Physician A might be over-represented, while those contracted with Physician B might be under-represented.
Thus, referring back to
In general, an F-score is used to measure accuracy. Here a modified F-score is used to rank order the contractual organizations to include in an ACO. Basically, a score is computed for each of the contractual organizations being added to the ACO. The scores can then be used to rank the contractual organizations and the contractual organizations having the top x scores are included in the ACO. The modified F-score used herein is based on participation percentage (participation %) and cover percentage (cover %) scores. For instance,
F-score=(1+μ2)*((participation %*cover %)/(μ2*participation %+cover %)),
wherein,
Thus, when evaluating a given one of the contractual organizations for inclusion in the ACO, the participation % looks at the number of healthcare providers the contractual organization has in common with the ACO as a function of the number of healthcare providers in the contractual organization, while the cover % looks at it as a function of the number of healthcare providers in the ACO. Since the ACO is essentially a list of providers, then the healthcare providers in the ACO can be based on the list of healthcare providers in the communities detected in step 104. Then what the participation % and cover % are measuring is how well the site-level “approximation” represents the original ACO provider list. The parameter μ is tunable. By way of example only, the parameter μ can be changed by the end user (e.g., end user entity such as the insurance company). The higher the value of parameter μ the more emphasis is placed on achieving high coverage. Namely, as shown in the equations above, the parameter μ can be used to tune participation %/cover % based, e.g., on an end-user's preferences.
One can visualize this organization-level analysis as shown in
Of course, not every contractual organization should be included in the ACO. Advantageously, the present techniques provide a data-driven approach to meaningfully evaluate organizations for inclusion in the ACO. For instance, based on the modified F-score computed for each contractual organization, the contractual organizations can be ranked. Then the organizations with the top y scores can be included in the ACO. Namely, the end-user can look at the F-score line in graph 206 and pick the first time a threshold is achieved. This process enables use of a single number (score) to understand the effect of including a contractual organization in the ACO.
Referring back to
By way of example only, the end user might be an insurance company that employs the recommendations to build ACO networks of organizations. The insurance company can leverage the present techniques to better understand the effect of including various contractual organizations in the network. Namely, as provided above, the participation percentage, cover percentage, and modified F-score can be computed for each candidate organization's inclusion in the ACO. For instance, adding or removing a given organization/site from the ACO will change the overall F-score.
Also provided herein is a recommender system 300 for building ACOs. See
The recommender engine 302 can also provide the user with visualizations corresponding to the steps of the recommendation process, as is depicted, e.g., in
The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
Turning now to
Apparatus 400 includes a computer system 410 and removable media 450. Computer system 410 includes a processor device 420, a network interface 425, a memory 430, a media interface 435 and an optional display 440. Network interface 425 allows computer system 410 to connect to a network, while media interface 435 allows computer system 410 to interact with media, such as a hard drive or removable media 450.
Processor device 420 can be configured to implement the methods, steps, and functions disclosed herein. The memory 430 could be distributed or local and the processor device 420 could be distributed or singular. The memory 430 could be implemented as an electrical, magnetic or optical memory, or any combination of these or other types of storage devices. Moreover, the term “memory” should be construed broadly enough to encompass any information able to be read from, or written to, an address in the addressable space accessed by processor device 420. With this definition, information on a network, accessible through network interface 425, is still within memory 430 because the processor device 420 can retrieve the information from the network. It should be noted that each distributed processor that makes up processor device 420 generally contains its own addressable memory space. It should also be noted that some or all of computer system 410 can be incorporated into an application-specific or general-use integrated circuit.
Optional display 440 is any type of display suitable for interacting with a human user of apparatus 400. Generally, display 440 is a computer monitor or other similar display.
Although illustrative embodiments of the present invention have been described herein, it is to be understood that the invention is not limited to those precise embodiments, and that various other changes and modifications may be made by one skilled in the art without departing from the scope of the invention.
