Patent Application Titled “Systems and Methods for Matching Patients to Best Fit Providers of Chronic Disease Prevention Programs” Published Online…

Patent Application Titled "Systems and Methods for Matching Patients to Best Fit Providers of Chronic Disease Prevention Programs" Published Online (USPTO 20180039762)

By a News Reporter-Staff News Editor at Diabetes Week -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventor Schmidt, Brenda (Phoenix, AZ), filed on October 13, 2017, was made available online on February 15, 2018 (see also Patent Application).

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: "The evolving U.S. health care system presents opportunities for improving population health. As described in the January, 2014 article entitled 'Twin Pillars of Transformation: Delivery System Redesign and Paying for Prevention', available at www.healthyamericans.org, population health offers better care for patients, better health for the population, and lower healthcare costs by reversing the escalating epidemic of chronic diseases such as obesity, diabetes, and cardiovascular disease. A key component of population health involves linking clinical care with community-based prevention programs and related social services. The Journal for Public Health Management and Practice, 'Population-Based Health Principles in Medical and Public Health Practice' (http://journals.lww.com/jphmp/Abstract/2001/07030/Populalion Based Health Principles in Medical and.12.aspx), notes that traditional medical education, research, and practice have focused on the care of the individual. Shifting the emphasis to embracing population-based health principles can have a greater effect on long term health and wellness, particularly in the prevention of chronic disease.

"Reversing the epidemic of chronic disease requires increased access to evidence-based prevention programs such as the National Diabetes Prevention Program (National DPP). The National DPP is a year-long community-based program delivered in group-based settings as well as virtually (on line) and supported by a trained lifestyle coach. The program helps patients modify their eating and physical activity habits and sustain lifestyle changes, coupled with a modest (e.g., 5%-7%) weight loss goal. The National DPP has been shown to reduce the risk of developing T2DM by 58% for prediabetic adults over 25 years of age, and by 71% for adults over 60.

"More than 700 community-based organizations (CBOs) and digital/virtual program providers have been granted pending or full recognition status as National DPP providers by the Centers for Disease Control and Prevention (CDC) (http://www.cdc.gov/diabetes/prevention/recognition). However, disparate community-based and virtual DPP providers are not supported through a coordinated approach to patient identification, referrals, program delivery, and payment. At present, healthcare providers supply their eligible patients with a list of organizations offering the National DPP, relying on the patient to follow up directly with a provider organization. Unfortunately, this type of 'opt-in' approach tends to result in significantly lower enrollment, in part because prevention programs offered by community-based organizations are typically not covered by most health insurance plans. More recently, insurers have begun to adopt the National DPP as a covered benefit for their members. However, most National DPP providers lack the infrastructure to submit medical claims for their services, and it would be cumbersome and costly for health plans to independently contract with each community-based or virtual National DPP provider.

"It is also known that patient behavior is a key metric in the success of chronic disease prevention programs. Consequently, chronic disease prevention programs should deliver against patients' needs, preferences and expectations. 'One size fits all' programs and delivery methods have limited success because all patients are not alike--even when they share a common health condition. See, for example, the discussion of demographic segmentation, psychographic segmentation, and behavioral segmentation at http://www.examstutor.com/business/resources/studyroom/marketing/market_a- nalysis/7_demographic_segmentation.php et seq.; and the Commonwealth Fund 'Quality Matters' at http://www.commonwealthfund.org/publications/newsletters/quality-matters/- 2015/june. The entire contents of the forgoing articles are incorporated herein by this reference. Presently known prevention program delivery models lack sufficient understanding of consumer preferences and how to effectively influence their choices.

"Systems and methods are thus needed which overcome these limitations. Various desirable features and characteristics will also become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and this background section."

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventor's summary information for this patent application: "Various embodiments of the present invention relate to systems and methods for: i) determining an 'ideal' participant profile for each of a plurality of programs and/or program providers based on the quality of participant engagement and successful outcomes; ii) segmenting heterogeneous patient populations into homogeneous sub-groups to identify participants who match the ideal profile; and iii) enrolling the identified participant in the best fit program. Accommodating individual patient needs and preferences in this way enables a more personalized approach to care, allowing health plans to engage with their members through prevention, thereby mitigating the higher long term costs of chronic disease treatment.

"Presently known machine learning technologies include Watson.TM. available from IBM and Azure.TM. available from Microsoft. It is possible to use current technologies to identify likely candidates for program intervention from within a patient population based on, for example, each patient's likelihood of incurring health care costs, presence and acuity of chronic health conditions, biometric data, and readiness to change. Intervention strategies undertaken by health plans, whether nurse practitioners in a care management call center, pharmacists coaching individuals on the use of medications, formulary management of primary and specialty care drugs or alternative benefits plans, are each appropriate for only certain individuals within the population.

"The present invention, on the other hand, uses predictive analytics and machine learning to pair a particular candidate with a specific one of several programs having analogous content, based on a prediction that the candidate will do well in the optimally selected program. Health plans have historically lacked the capabilities to precisely select the individuals appropriate for varied intervention methodologies, using a one-size fits all approach.

"Stated another way, prior art approaches identify which patients should be treated by a particular intervention; whereas the present invention identifies best fit programs from a wide variety of treatment providers for patients already identified as candidates for treatment.

"In an embodiment, segmentation involves analytic techniques to break down a heterogeneous population into smaller, homogeneous groups composed of individuals with similar needs, preferences, attitudes and behaviors. These segments are then analyzed with variables from a broader behavioral database (e.g., medical claims data). Unique, segment-specific variables may be isolated and extrapolated across a database population to flag each patient according to his or her segment. The emergent segments may then be profiled against the 'ideal' patient profile based on engagement and outcome data from each program provider.

"A series of algorithms and/or branched logic may be used to determine one or more 'best match' programs for each participant, allowing the participant to explore options based on his or her expressed preferences. Successful application of these insights can positively drive program engagement and influence health and wellness behaviors, and support ongoing retention and successful program completion.

"In an embodiment, a diverse network of program providers each deliver similar evidence-based programs having variations in how the program is delivered, which may be used to quantify participant preferences (which may be programmatically weighted). Using predictive analytics, the system determines the profile of the 'ideal participant' for each program provider based on the foregoing variables and individual participant characteristics. This profile represents a hypothetical participant most likely to be successful in each program based on delivery methodology.

"After matching participant-specific data to various ideal participant profiles, the system programmatically (e.g., algorithmically) selects the program provider best suited to the participant. In this context, the participant-specific data may include, inter alia, patient contact information (including zip code), demographics, socio-economic factors, psychographics, health information, health care utilization, claims data, electronic medical record data, prescription history, and purchasing data (collectively referred to herein as the 'Patient Data').

"It should be noted that the present invention, while described in the context of Diabetes Prevention, it is not so limited. Those skilled in the art will appreciate that the systems and methods described herein may contemplate any prevention or treatment program, as well as chronic disease management, telemedicine, medication and dosage adherence, social services, behavioral health, and the like.

"Various other embodiments, aspects and features are described in greater detail below."

For more information, see this patent application: Schmidt, Brenda. Systems and Methods for Matching Patients to Best Fit Providers of Chronic Disease Prevention Programs. Filed October 13, 2017 and posted February 15, 2018. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220180039762%22.PGNR.&OS=DN/20180039762&RS=DN/20180039762

Keywords for this news article include: Cyborgs, Epidemiology, Public Health, Machine Learning, Patent Application, Health and Medicine, Risk and Prevention, Obesity and Diabetes, Predictive Analytics, Emerging Technologies, Nutritional and Metabolic Diseases and Conditions.

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