Patent Application
20100042431
1. Field of the Invention
The present invention relates generally to the fields of medical and network management. More particularly, it concerns methods and systems for evaluating the quality and cost efficiency of healthcare services.
2. Description of Related Art
A. Quality and Cost of Healthcare
Quality healthcare can be defined by the extent to which patients get the care they need in a manner that most effectively protects or restores their health. This means having timely access to care, getting treatment that medical evidence has found to be effective and getting appropriate preventive care. Choosing a high-quality health plan and a qualified physician plays a significant role in determining whether patients will get high-quality care. Measuring and reporting on healthcare quality is extremely important, because it gives consumers and employers the ability to make informed choices and pursue the best available care. Still, healthcare quality assessment is about more than just informing buyers and consumers about their options. It's also about giving feedback to health plans, medical groups, and physicians that they can use to address quality issues and improve quality of service and cost efficiency over time.
Cost-effectiveness analysis is a form of economic analysis that compares the relative expenditures (costs) and outcomes (effects) of two or more courses of action. Cost-effective analysis is important for healthcare providers to understand how much to charge for the services and for consumers and buyers to understand how much to pay for the services. Cost effectiveness in healthcare may involve measurement of the extent to which an intervention or a service achieves health improvements per unit of cost. This can be measured in terms of various outcomes such as cases of disease prevented, years of life saved, or quality-adjusted life years saved.
In today's healthcare market, the needs for evaluating quality and cost efficiency of healthcare providers are not satisfied in many fields of medicine due to the growing complexity in medical treatment options and outcomes.
For example, oncology is one healthcare field that may benefit from an integrated system of cost and quality assessment for improving care and the customer experience. Certain healthcare expenditure data shows that oncology costs represent 12% of overall commercial medical expenses. According to the National Institute of Health (NIH), these costs are growing at 13% annually, roughly double the overall rate of medical costs. Overall annual cancer direct medical costs in the U.S. were $78.2 billon in 2006. However, only four cancer categories (lung, breast, colorectal, and prostate) represent over 50% of total oncology costs. Certain AIS studies have suggested that over 400 new drugs, representing a significant portion of all drugs currently in development, are focused on cancer care. Nonetheless, when evaluated using a comprehensive multi-year outcomes database, concordance with clinical guideline rates vary across different types of cancer care, patients and institutions. Furthermore, a typical cancer patient is often ill-equipped to choose among doctors and hospitals because of the scarcity of information about their varying quality cancer care and compliance with national guidelines.
Differentials in quality of care also pose serious problems for healthcare systems and health insurance companies, especially in fields such as oncology that typically generate high costs. Studies suggest that significant numbers of patients miss out on cancer treatments that could prevent recurrence, prolong survival, or save lives. Such treatments may include appropriate chemotherapy (recurrence costs $30,000) or limited screening (colonoscopy costs $500-1000 while cost of colon cancer early stage is $30,000 and of colon cancer late stage is $120,000) in colon cancer, and under-treatment with radiation or under-use of anti-estrogen drug therapy in breast cancer. Additionally, over-treatment which wastes resources and money and needlessly subjects patients to the pain and risks of surgery or radiation, such as over-treatment with radical surgery (one surgery averages $12,150) or radiation (average radiation cost $57,357 for 6 weeks of treatment (Konski, 2006)) and under-use of experienced surgeons (Vickers et al., 2007) in prostate cancer, and inappropriate usage of Herceptin drug therapy in breast cancer (annual cost of treatment is $40,000) may occur. (Grady, 2007). Therefore, there remains a need for a more robust and reliable assessment of quality and cost of healthcare services.
B. Administrative Data
Administrative data is often used to evaluate the quality of healthcare. This data is typically derived from administering healthcare services, enrolling members into health insurance plans, and reimbursing for services. The primary producers of administrative data are the federal government, state governments, and private healthcare insurers. Administrative data is readily available, inexpensive to acquire, computer readable, and typically encompass large populations. Many hospital report cards and physician profiles are derived from administrative data.
Gaps in clinical information and the billing context typically compromise the ability to derive valid quality appraisals from administrative data. One example of typical administrative data is shown in Table 1 below. This particular data provides a limited view of quality of care for breast cancer patients. Currently, the type of cancer and the type of treatments are known, but only a limited view of general treatment rules can be created from typical administrative data, while questions such as “was the treatment in accordance with clinical guidelines?” still remain.
Similarly, general aggregate cost and occurrence data derived from administrative data as shown Table 2 below is of only limited use.
The absence of clinical markers and contextual data limits the ability to create rules for assessing treatment plans against clinical guidelines. Similarly, these limits may reduce the effectiveness of cost measurement of care. There remains a vital need for methods and systems for evaluating the quality and cost of care by integrating administrative data and clinical data.
A system for evaluating quality and cost efficiency of a healthcare service to a patient is presented. In one embodiment, the system may include a merged database comprising administrative data and clinical data, a cost-of-care efficiency engine coupled to the merged database, the cost efficiency engine configured to analyze the merged clinical data and administrative data to determine a measurement of cost efficiency, a quality engine coupled to the merged database, the quality engine configured to analyze the merged clinical data and administrative data to determine a compliance value, wherein the compliance value indicates a level of compliance with a clinical guideline, and a reporting application coupled to the cost efficiency engine and the quality engine, the reporting application configured to generate a report representing at least one of the measurement of cost efficiency and the compliance value.
In a further embodiment, the clinical data is collected by a clinical exchange system and a clinical coding system. The clinical exchange system may include fax, internet based data exchange forms, or EMR interface. In a certain embodiment, the target specialty is oncology, cardiology, or orthopedics. In one specific embodiment, the specialty is oncology. The clinical data may include histology, tumor stage, tumor cell receptor expression or disease progression. In a certain embodiment, the clinical coding system comprises a data dictionary. The data dictionary may include initial diagnosis, disease stage or treatment status. In still another embodiment, the system may further comprise a benchmark module.
A method for evaluating quality of healthcare service to a patient is also presented. In one embodiment, the method includes merging clinical data and administrative data, analyzing the merged clinical data and administrative data to determine a compliance value, wherein the compliance value indicates a level compliance of with a clinical guideline, and generating a report representing the compliance value. In a further embodiment, the clinical guideline may include a treatment guideline or medication adherence. In still another embodiment, the clinical guideline is an NCCN guideline.
A method for evaluating cost efficiency of a healthcare service to a patient is also presented. In one embodiment, this method includes merging clinical data and administrative data, analyzing the merged clinical data and administrative data to determine a measurement of cost efficiency, and generating a report representing the measurement of cost efficiency. In a further embodiment, the clinical data includes initial stage, disease stage, treatment status or prognostic indicators.
Additionally, the methods may include aggregating clinical and administrative data to provide a benchmark. In a certain embodiment, the provider is a physician, a hospital, a clinic or an emergency room. In still another embodiment, the provider is an individual or a healthcare network. The stakeholder may include a consumer, a healthcare provider, a payer or an employer.
It is contemplated that any methods or systems described herein can be implemented with respect to any other methods or systems described herein.
The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and/or the specification may mean “one,” but it is also consistent with the meaning of “one or more” or “at least one.” The term “about” means, in general, the stated value plus or minus 5%. The use of the term “or” in the claims is used to mean “and/or” unless explicitly indicated to refer to alternatives only or the alternative are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and “and/or.”
Other objects, features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description, while indicating specific embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will be apparent to those skilled in the art from this detailed description.
The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The drawings do not limit the scope but simply offer examples. The present embodiments may be better understood by reference to one or more of these drawings in combination with the description of the illustrative embodiments presented herein:
As used in this disclosure, “administrative data” or “health plan administrative data” is used according to the ordinary meaning in the art and should contain records associated with at least one or more medical procedures, prescriptions, diagnosis, medical devices, or the like.
As used in this disclosure, a “clinical exchange system” refers to a system comprising fax forms, web technologies, etc. provided to customers to facilitate the collection of clinical data from providers including physicians and facilities.
As used in this disclosure, a “clinical coding system” refers to a set of coding specifications including descriptions and formats for collecting clinical data, as well as a data dictionary.
As used in this disclosure, a “quality engine” refers to a software system comprising analytic algorithms that utilizes merged clinical and administrative data to measure whether actual healthcare delivery for patients is in concordance with a clinical guideline, e.g., NCCN (the National Comprehensive Cancer Network) guidelines.
As used in this disclosure, a “cost-of-care/efficiency engine” refers to a software system that utilizes merged clinical and administrative data to measure the costs of disease episodes, such as using the Ingenix Episode Treatment Groups(® (ETG® system).
As used in this disclosure, a “reporting application” refers to a software system used by customers to generate reports and analysis based on use of a quality/efficiency engine.
As used in this disclosure, a “benchmark” refers to aggregated results and measures of concordance/efficiency using data from multiple customers to contribute data to the aggregated database.
As used in this disclosure, a “data integration service” refers to a service provided to customers to assist in merging clinical data and administrative data.
As used in this disclosure, “data management” refers to a service provided to update and maintain client databases.
As used in this disclosure, an “application integration service” refers to a supplemental consulting service provided to clients to assist them in linking information derived from the Impact Intelligence Oncology Management solution to other client systems or applications.
As used in this disclosure, a “clinical training/consulting program” refers to a supplemental service provided to customers to establish programs in areas such as provider education, medical management, etc., based on information derived from the IIOM system.
As used in this disclosure, a “clinical guideline” can be any clinical guideline known in the art, which is also called medical guideline, clinical protocol or clinical practice guideline. It refers to a document with the aim of guiding decisions and criteria regarding diagnosis, management, and treatment in specific areas of healthcare.
Clinical guidelines may identify, summarize and evaluate the evidence and current data about prevention, diagnosis, prognosis, therapy including dosage of medications, risk/benefit and cost-effectiveness. In one embodiment, clinical guidelines may also define questions related to clinical practice and identify possible decision options and associated results. Certain guidelines may contain decision or computation algorithms discussed below. Thus, the clinical guidelines may integrate the identified decision points and respective courses of action with the clinical judgment and experience of practitioners. Many such guidelines place the treatment alternatives into classes to help providers decide which treatment to use. Additional objectives of clinical guidelines may include standardization of medical care, increased quality of care, reduction of risk, and achieving the best balance between cost and medical parameters such as effectiveness, specificity, sensitivity, resoluteness, etc.
For example, NCCN clinical guidelines, such as the NCCN Drugs and Biological Compendium™ and NCCN Clinical Practice Guidelines™ in Oncology, are defined as “systematically developed statements to assist practitioner and patient decisions about appropriate healthcare for specific clinical circumstances” (Field, 1990).
As used in this disclosure, an “episode” in healthcare means a block of one or more healthcare services received by an individual during a period of relatively continuous contact with one or more providers of service, in relation to a particular medical problem or situation.
Referring to
A. Module 100: Cancer Patient Identification
The patient identification and provider attribution module 100 identifies patients or healthcare insurance plan members and their primary providers or physicians using administrative data. For example, the patient and provider identification module 100 may generate a report of cancer patients and their providers using existing administrative data. In one embodiment, patients may be identified for different cancer types, such as breast, prostate, lung, and colorectal cancers. Additionally, the report may be narrowed by cancer site, geography, etc., to allow for targeted outreach.
B. Module 200: Clinical Data Collection
After the providers have been identified for specific patients by the patient identification and provider attribution module 100, the clinical data collection module 200 may collect clinical data 220 for these patients from identified corresponding healthcare providers or physicians in healthcare network 210.
In certain embodiments, different types of clinical data may be collected, including: initial diagnosis data such as stage and disease markers and ongoing clinical assessment of the patient's status. For example, clinical data for breast cancer may include histology (i.e., ductal, lobular, adenocarcinoma), tumor stage or TNM status (defined by (T)umor size, (N)odal involvement, and (M)etastatic spread), expression status of tumor cell receptors (such as estrogen and progesterone receptors (ER/PR) or Her-2 neu receptors) and disease progression.
In certain embodiments, initial diagnosis data may include a date of initial diagnosis, site, histology, disease status (e.g., TNM status), status of disease markers such as tumor cell receptors, or grade for certain cancers.
In another embodiment, ongoing clinical assessment data may include a date of assessment and clinical status, date of death, an enrollment status in clinical trials, or a prognostic index. Clinical status may include: (i) disease free; (ii) initial adjuvant treatment ongoing; (iii) recurrence/progression—local; (iv) recurrence/progression—regional; (v) recurrence/progression—metastatic; (vi) end of life care; (vii) death due to cancer; and/or (viii) death due to other or unknown cause. Ongoing clinical assessment data in breast cancer may also include menopausal or pregnancy status.
In further embodiments, the clinical data collection module 200 may include a clinical exchange system 225 and a clinical coding system 235. The clinical coding system 235 may be a set of coding specifications including descriptions and formats for collecting clinical data, as well as a data dictionary. One example of a coding specification is a set of oncology G-codes as exemplified in Table 3. The oncology G-codes are temporary national codes for items or services requiring uniform national coding between one year's update and the next.
In one embodiment, the collected clinical data may be stored or deposited as data dictionaries comprised in the clinical coding system 235. A data dictionary may be used to identify the clinical information to be collected. IIOM's data dictionary, built in collaboration with clinical guidelines, may be included as a component of the quality and efficiency analysis. Sample data dictionaries representing initial diagnosis file or ongoing treatment file for breast cancer are shown in Tables 4-5.
In a further embodiment, the clinical data collection module 200 may create a “comprehensive” solution to reach the targeted providers 210 for requesting clinical data 220 as illustrated in
In a further embodiment of the clinical data collection module 200, financial and non-financial incentives can be used with network physicians to increase participation in data sharing and facilitate the collection of clinical data as show below in Table 6.
C. Module 300: Integrated Clinical and Administrative Database
Following clinical data collection, the clinical data 220 and administrative data 900 may be merged or deposited into a merged database 300 to store the administrative and clinical data. Data in the merged database 300 may be further processed by the cost-of-care/efficiency engine 400 and the quality engine 500. In one embodiment, the merged clinical and administrative data may facilitate a comparison of an actual treatment plan to a clinical guild line. In this embodiment, cost of care may be evaluated using episode of care units, risk assessment technologies and evidence-based rules regarding cost-effectiveness. Furthermore, benchmarks may be developed from the national database across different health plans.
D. Module 400: Cost-of-Care/Efficiency Engine
In one embodiment, the cost-of-care efficiency engine 400 may analyze clinical/administrative data stored in the merged database 300 to measure cost of care at a high level of granularity. Clinical data, including initial stage and ongoing treatment status, may support the computation of risk adjusted costs. Both initial diagnosis and treatment status may impact the choice of treatment and associated costs. Other factors may include tumor cell receptors, other prognostic indicators (e.g., menopausal status), etc.
In certain aspects, the cost-of-care/efficiency engine 400 may generate episode units and analyze cost. For example, the cost efficiency engine 400 may process claim data through a grouping method such as the ETG (episode treatment group) grouper. Episode Risk Groups (ERGs) have been developed to offer a more accurate health risk assessment tool with greater predictive power. Like many existing models, ERGs use demographic variables and diagnoses to predict health risk. One differentiator of ERGs over existing systems is the use of “episodes of care” as markers of risk. By leveraging Episode Treatment Groups (ETGs), the ERG model focuses on the key information describing a patient's underlying medical condition, rather than the individual services provided during the treatment of that condition. In this example, each claim service line may be assigned to an episode of care based on diagnosis, procedure codes, and proximity.
In a further embodiment, ETGs may be split into episode units. Some examples of episode units are shown in
The cost efficiency engine 400 may also assign risk weights to episode units to account for patient characteristics that influence cost, such as age and gender, co-morbidities, and clinical markers (e.g., stage group, histology, tumor cell receptors, and other characteristics). Characteristics of two Sample breast cancer patients are shown in Tables 7-8 below and their episode units are represented in
In a further embodiment, the cost-of-care/efficiency engine 400 may perform cost of care measurements using a combination of administrative data and clinical data. Administrative data alone may provide a limited view of cost of care, such as allowing analysis across all cancers by site exemplified in Table 9. However, when clinical data is integrated with the administrative data, the cost efficiency engine 400 may measure costs of care at a much more granular level than that with administrative data alone. For example, the cost efficiency engine 400 may perform cost analysis on each step of disease progression or episode unit shown in
As described above, cost of care analysis using the cost efficiency engine 400 can incorporate multiple clinical concepts or data, including: initial diagnostic status, initial treatment, remission, recurrence/progression, and end of life care. Clinical concepts or data, in combination with administrative data, may be used to adjust expected costs for a particular patient group. In a further embodiment, the cost efficiency engine 400 may roll up actual and expected costs may be rolled up into usable reports for managing physicians, health planning, or other grouping.
E. Module 500: Quality Engine
A quality engine 500 may further process the data stored in the merged database 300. The quality engine 500 may be implemented as software system comprising analytic software modules configured to measure whether healthcare services delivered to patients are in compliance with a clinical guideline. In one embodiment, administrative data may be used to create general treatment rules, such as whether the patient had a mammogram or whether the person is diagnosed with certain cancer. The quality engine 500 may then assess whether a particular patient's treatment was in compliance with the treatment rules by evaluating the clinical data against a the treatment rules.
In a further embodiment, the quality engine 500 may determine whether the patient received the recommended medicine or treatment for the particular disease that he or she has. For example, the quality engine 500 may measure compliance with NCCN™ Drug and Biologics Compendium, concordance with Selected NCCN™ Treatment Guidelines or other quality engine processes, such as Patient Co-morbidity list or Patient Adherence to Prescribed Drugs (Chronic Drug List).
The NCCN Drugs & Biologics Compendium™ is the latest in a series of evaluative information products intended to optimize the clinical decision-making process with a view toward improving the care available to patients. The Compendium contains authoritative, scientifically derived information designed to support decision-making about the appropriate use of drug and biologic therapy in patients with cancer. The Compendium lists appropriate uses of agents as defined in and derived from the NCCN Clinical Practice Guidelines in Oncology™. As such, the uses listed in the Compendium are based upon the evaluation of evidence from scientific literature, integrated with expert judgment in a consensus-driven process. The Compendium is indexed by drug and biological agent whereas the NCCN Clinical Practice Guidelines in Oncology™ are indexed by disease. The Compendium identifies the pharmacologic characteristics of each drug or biological and includes information on route of administration, as well as the recommended uses in specific diseases. The indicated uses are categorized in a systematic approach that describes the type of evidence available for and the degree of consensus underlying each recommendation.
NCCN Drug and Biologics Compendium has a list of anti-neoplastic therapeutic drug classes that are appropriate for treatment of various cancer diagnosis, including: 29 for breast cancer, 13 for prostate cancer, 8 for colorectal, and 19 for lung cancer, and for 30+ other cancers. In one embodiment, the quality engine 500 may analyze pharmacy claim data for certain types of cancer against the Compendium to measure compliance according to diagnosis code. The algorithm used in quality engine 500 also accounts for co-morbidities.
In exemplary embodiments as shown in
If the drug is not compliant with the Compendium 535, the quality engine 500 may analyze administrative data to identify co-morbidities 540 based on a condition class and various diagnosis code sets. If the use of the drug is not compliant, the quality engine 500 may identify co-morbid conditions that justify use of the drug. If the use is non-compliant and there are no co-morbidities to justify the use, then the use is off-compendium. The analysis in Table 11 shows that a considerable portion of drugs taken by cancer patients are not on the NCCN list and are associated with expensive costs, which may be preventable by applying the quality engine 500, based on analysis of the Integrated Healthcare Information Services (IHCIS) Database of 22.6 million patients (95, 255 with breast cancer, 24, 989 with colon cancer, 8,090 with rectal cancer).
In certain embodiments, the quality engine 500 may also analyze concordance with selected NCCN™ Treatment guidelines. In oncology, NCCN treatment guidelines are widely accepted, endorsed and used by academic and community cancer centers, as well as practicing oncologists. These guidelines have been developed to provide recommendations for managing the major symptoms experienced by patients with cancer and a set of pathways detailing the major early diagnostic steps for breast, lung, colorectal, and prostate cancer (available through world wide web at nccn.org). Each guideline may include an algorithm or decision pathway outlining care management, a manuscript discussing important issues related to the algorithm, and references providing data on which recommendations are based.
Recommended treatments according to NCCN guidelines vary based on the patient's clinical parameters, including: histology (i.e., ductal, lobular, adenocarcinoma), tumor stage or TNM status as described below, tumor cell receptors (varies by cancer, such as estrogen and progesterone receptors (ER/PR) or Her-2 neu receptors for breast cancer), disease progression. Tumor stage is defined by (T)umor size, (N)odal involvement, and (M)etastatic spread. Tumor stage group is a summary of TNM status, which can be used for reporting purposes (e.g., Stage I, Stage II, Stage III, Stage IV). Multiple treatment options may be accepted for a specific tumor stage and other clinical status markers.
For example, certain NCCN guidelines regarding radiation following mastectomy in invasive breast cancer are presented in
F. Module 600: Decision Support Report
In one embodiment, the reporting application 600 may generate customizable or standardized reports. For example, the reporting application 600 may generate medical management reports. The medical management reports may provide information regarding collected clinical data, as well as comparisons between the clinical data and corresponding administrative data. These quality reports may have improved clinical relevance relative to administrative data only as exemplified in Table 14. Moreover, compared with administrative data alone, which provides a limited of view of cost of care by only allowing analysis all cancers by site, reporting based on both clinical data and administrative data using the IIOM system may include more granular units of analysis as shown in Table 15.
G. Module 700: Benchmarks
In certain embodiments, clinical data and associated administrative data may be gathered into an aggregated clinical/administrative database 800 to provide benchmarks 700 to enable comparison between individual data from certain payers or providers and national data, therefore comparing individual provider's performance against peer's nationwide.
In certain aspects, benchmarks 700 are aggregated results and measures of concordance/efficiency using data from multiple customers to contribute data to the aggregated database and could be used as reference for evaluating quality/cost efficiency of healthcare services at various levels, such as at the plan level, at the physician level, by cancer type, by region, or by other relevant business dimensions. Benchmarking could guide several initiatives, including: building outreach programs to providers, incentivizing providers to share clinical data, or assessing performance against certain standards, such as national or regional standards.
H. Role of IIOM System in Disease Management
Analysis of combined database through these analytic engines drive provider and member-specific results. Applications of results include, but are not limited to, premium designation programs, medical management initiative, centers for excellence and provider referrals, healthcare coordination and delivery, provider profiling and education, clinical trial and outcome study provider/patient identification, care and case management programs, medical management programs and information sharing programs, guideline concordance and cost of care analysis including evaluation of drug therapies and treatment gaps, consumer activation, provider activation, employer reporting and analysis and so on.
Embodiments of the present invention can provide a number of advantages. For example, the ability to collect and integrate clinical data with administrative data facilitates improvements in the care received by patients diagnosed with cancer as well as other diseases and also supports a more valid assessment of the cost and quality of the care delivered by the physicians and hospitals treating these patients. Further embodiments of the invention may drive new insights about provider quality and cost-of-care in the area of several costly cancers at a level of granularity and precision that is not currently available. These insights can drive enhancements in the care delivered to oncology patients as well as other patients and the management of the individual providers and networks serving these patients. Provider measurement and education, assurance of appropriate medication usage, and assistance with member outreach and education programs can all be supported by the information and analytics made available by the present systems and/or methods. Further applications and advantages for disease management, oncology management, are shown in
All of the systems and/or methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the systems and methods of this invention have been described in terms of particular embodiments, it will be apparent to those of skill in the art that variations may be applied to the systems and/or methods in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.
The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference:
This application claims priority to U.S. Provisional Application No. 61/089,405 filed Aug. 15, 2008, the entire contents of which is specifically incorporated herein by reference without disclaimer.
| Number | Date | Country | |
|---|---|---|---|
| 61089405 | Aug 2008 | US |
