“Patient-Provider Healthcare Recommender System” in Patent Application Approval Process (USPTO 20190043606)

2019 FEB 25 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventors Roots, Kurt (Madison, WI); Nadler, Jeffrey (Rye Brook, NY), filed on August 1, 2018, was made available online on February 7, 2019, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application is assigned to Teladoc Inc. (Purchase, New York, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Historically, the decision of selecting a medical provider was found to not be consumeristic or rational, instead relying on family, friends, and other factors based on geographic and historical contexts as the drivers for a recommendation. Harris, How do Patients Choose Physicians? Evidence from a National Survey of Enrollees in Employment-Related Health Plans, Health Services Research, 38(2):711-732 (2017). Studies in the 1990s and early 2000s focused on factors like location, race, gender, and specialty. Saha et al., Do Patients Choose Physicians of their Own Race?, Health Affairs, 19(4):76-83 (2000); Phillips et al., Women Patients’ Preferences for Female or Male GPs, Family Practice, 5(6):543-547 (1998). This changed in recent years where a high percentage of patients are aware of and review online provider ratings to make decisions. While these existing intrinsic value decisions are still being applied, a new spectrum of preferences can be used to select a provider. The economics of selection change when new criteria like appearance are applied to the selection process. Puhl et al., The Effect of Physicians’ Body Weight on Patient Attitudes: Implications for Physician Selection, Trust and Adherence to Medical Advice, International Journal of Obesity, 37:1415-1421 (2013). Further, this consumerization impacts a range of provider types, including, but not limited to doctors, therapists, dentists, optometrists, chiropractors, and nurses.

“This new and more advanced selection process is most frequently driven through consumer facing apps and websites like Yelp (yelp.com) and Healthgrades (healthgrades.com) where users can search and rank experiences with providers. This approach fundamentally relies on other users’ rankings, both captured through surveys and written reviews. These services then utilize this information with generalized filtering instruments to provide a listing of providers to the user. Ornstein, On Yelp, Doctors Get Reviewed Like Restaurants--And it Rankles, available at https://www.npr.org/sections/health-shots/2015/08/06/429624187/on-yelp-do- ctors-get-reviewed-like-restaurants-and-it-rankles (2015).

“It is an intuitive, yet novel, progression from non-consumeristic, consumeristic, to sophisticated retail-like personalization of the decision process, wherein providers are accurately presented through advanced artificially intelligent agents to the patient for observation, review, and ultimately selection to deliver care.

“Recommender systems have been widely used in industries like retail to provide personalized suggestions to individuals. Typical recommender systems rely on historical data patterns from the consumer to predict future behavior. For example, those streaming movie X might also like movies Y and Z, which are frequently viewed together by others. Digital form factors provide a seamless experience to represent these suggestions to the consumer for evaluation, selection, and purchase.

“Recommender systems are traditionally classified as content-based or collaborative filtering approaches. Collaborative filtering is generally concerned with the relationship between users and/or items, wherein the similarity of the items is represented by users typically rating more than one item. Within collaborative filtering, there are two commonly used approaches: item-item and user-based. The former recommends similar items based on target user’s prior ratings of similar items, typically while browsing other items. The latter recommends similar items to the target user based on similarities between users and their prior behavior. In summary, collaborative filtering relies on past preferences or rating correlation between users, without reliance on additional user or item data (i.e. metadata). Collaborative filtering suffers from the ‘cold start’ problem and can be problematic in a healthcare setting because it raises the concern of sharing patient rankings and preferences across populations.

“Both of the approaches of collaborative filtering differ from content-based filtering, which generally recommends similar items using attributes from profiles. The items in this approach require profiles of attributes to be generated. For example, attributes or metadata of a t-shirt for which a user expressed interest would store things like size, color, brand, and so forth. The system then matches attributes of that t-shirt to other t-shirts viewed with similar attributes to generate recommendations. This method has two primary advantages in a healthcare setting: (1) the isolation of ratings to an individual user’s profile; and (2) new items can be easily incorporated (no cold start problem). The primary disadvantage of content-based filtering in this context is that new user ratings or information about a user has to be collected. Thus, returning to the example, if a user never expressed interest in the t-shirt, we do not have information about their preferences. Hao, A Comparative Study: Classification vs. User-Based Collaborative Filtering for Clinical Prediction, BMC Medical Research Methodology, 16:172 (2016); Leskovec et al., Mining of Massive Datasets, Stanford University (2014).

“The present invention seeks to solve the aforementioned drawbacks by providing systems and methods that provide proper safeguards for the sensitive nature of healthcare patient information, and impute preferences of patients without prior data regarding the patients’ preferences.

“Similar to on-demand ridesharing drivers, medical professionals can be made available nearly instantly through a frictionless delivery model. However, unlike most retail situations or hailing a ride to the airport, healthcare is very personal and highly regulated. Additionally, selecting the ‘best’ provider for one’s needs can be complicated and is dependent on a state-by-state basis because of provider licensing requirements. This invention personalizes provider selection by advancing the complex and sensitive process of selecting a provider while controlling for geographic complexity.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “The present invention relates generally to a Health Recommender System and related methods that employ a sophisticated system architecture and technology stack that may be encapsulated in a container platform, and an artificially intelligent agent, also referred to herein as a predictive engine, that can optimally match patients with healthcare providers. As used herein, the term ‘patient’ may be used interchangeably with the term ‘member’ and the term ‘provider’ may be used interchangeably with the terms ‘physician,’ ‘care provider,’ or ‘caregiver.’

“The system and methods of the present invention are designed to efficiently collect, quantify, and evaluate data across several dimensions important to the provider and patient. In an embodiment, the data components undergo a variety of preprocessing and statistical transformations in preparation for processing by the predictive engine. The preprocessed data is then passed to the predictive engine, which applies a supervised learning technique to evaluate a patient-provider relationship and compute a likelihood of match scenario. The predictive engine thus produces optimal provider recommendations for a new or repeat patient. These results are indexed in a matrix and made available in real-time.

“The systems and methods of the present invention have several important advantages. One such advantage is the ability to determine if additional expertise or a referral is required for the patient before the consultation or visit occurs. Under a traditional framework, for example, while a patient may experience sinusitis symptoms, they may also be clinically depressed. The secondary complaint may go unnoticed, de-prioritized and/or not communicated to a mental health professional in favor of resolving the chief complaint. Another advantage of the systems and methods of the present invention is their utility in brick-and-mortar clinics, pharmacies, and nursing homes, wherein a pharmacists, nurse, and/or geriatric care provider is optimally determined based on a particular patient’s specific needs. Additionally, the systems and methods of the present invention are particularly well-suited for an on-demand healthcare economy, where everything from virtual clinics to on-demand insurance offerings are becoming commonplace.

“One embodiment of the present invention is directed to a healthcare recommender system for matching healthcare providers with patients that employs: one or more provider records, each provider record corresponding to a provider; one or more patient records, each patient record corresponding to a patient and containing at least one demographic data value of the patient; a multidimensional feature space relating to one patient and defining one or more patient-provider combinations, wherein each patient-provider combination is made up of a set of values that includes at least the demographic data value of the patient and at least one of an aggregate internal review score of the provider and an aggregate external review score of the provider; and a predictive engine capable of machine learning that classifies each patient-provider combination of the feature space into one of two or more classes according to the set of values.

“Another embodiment of the present invention is directed to a computer-implemented method for matching healthcare providers with patients employing the steps: (i) creating a provider record corresponding to a provider; (ii) creating a patient record corresponding to a patient and populating the patient record with at least one demographic data value of the patient; (iii) at least one of: (a) receiving one or more internal data of the provider from one or more historical patients of the provider, and for each internal data creating an internal review record populated with the internal data and a demographic data value of the historical patient. In a preferred embodiment, the internal data is an internal score, which may be generated from a survey completed by the historical patient; and (b) retrieving one or more external data of the provider from one or more third party websites or databases, and for each external data creating an external review record populated with the external data and a demographic data value of the source of the external data. In a preferred embodiment, the external data is a review of the provider authored by a reviewer. In a most preferred embodiment, the external data is a computed numerical sentiment value by natural language processing of the review. In an embodiment, the demographic data value of the reviewer is determined as discussed below where it is not provided; (iv) generating a multidimensional feature space relating to one patient and defining one or more patient-provider combinations, wherein each patient-provider combination is made up of a set of values that at least includes the demographic data value of the patient and at least one of an aggregate internal review score and an aggregate external review score. In a preferred embodiment, the aggregate internal review score is computed from the internal review records having a demographic data value of the historical patient that is the same as the demographic data value of the patient, and the aggregated external review score is computed from the external review records having a demographic data value of the reviewer that is the same as the demographic data value of the patient; and (v) classifying by a predictive engine capable of machine learning each patient-provider combination of the feature space into one of two or more classes according to the set of values.

“Steps (ii), (iii)(a), and (iii)(b) of the above method may be performed in any order.

“A further embodiment of the present invention is directed to a computer-implemented method for matching a patient with one or more healthcare providers employing the steps: (i) receiving a request from the patient to be matched with the providers, wherein the patient has a corresponding patient record that includes at least one demographic data value; (ii) generating a multidimensional feature space relating to the patient and defining one or more patient-provider combinations, wherein each patient-provider combination is made up of a set of values that at least includes the demographic data value of the patient and at least one of an aggregate internal review score of the provider and an aggregate external review score of the provider; (iii) classifying by a predictive engine capable of machine learning each patient-provider combination of the feature space into one of two or more classes according to the set of values. In a preferred embodiment, at least one class represents optimal provider matches; (iv) presenting to the patient one or more providers, preferably those classified in the class representing optimal provider matches; and (v) receiving a selection of one provider from the patient.

“The providers may be presented to the patient in a graphical user interface (GUI) that preferably includes a digital image of the provider and biographical information about the provider such as name, age, gender, geographical location, and medical specialty. After the patient’s selection of the provider is received, the methods of the present invention may further include the step of enabling communication between the patient and the provider via a network, wherein the communication may be text, audio, video, or a combination thereof.

“In embodiments of the present invention, the aggregate internal review score of the provider is derived from one or more internal review records corresponding to the provider, each internal review record including an internal score provided by a historical patient of the provider and a demographic data value of the historical patient, and the aggregate internal review score of the provider is computed from the internal review records having a demographic data value of the historical patient that is the same as the demographic data value of the patient. In preferred embodiments of the present invention, the internal review score is derived from structured surveys completed by historical patients of the provider, i.e., patients that have previously had a consultation or other interaction with the provider.

“In embodiments of the present invention, the aggregate external review score of the provider is derived from one or more external review records corresponding to the provider, each external review record including an external score from a third party website or database and a demographic data value of the source of the external score, and the aggregate external review score of the provider is computed from the external review records having a demographic data value of the source that is the same as the demographic data value of the patient. In a preferred embodiment the external score is a review of the provider authored by a reviewer. In a most preferred embodiment, the external data is a computed numerical sentiment value of the review by natural language processing of the review. In an embodiment, the demographic data value of the reviewer is determined as discussed below where it is not provided.”

The claims supplied by the inventors are:

“1. A healthcare recommender system for matching healthcare providers with patients comprising: one or more provider records, each provider record corresponding to a provider and comprising at least a unique government-issued identifier of the provider; one or more patient records, each patient record corresponding to a patient and comprising at least one demographic data value of the patient; a multidimensional feature space relating to one patient and defining one or more patient-provider combinations, wherein each patient-provider combination comprises a set of values that includes at least the demographic data value of the patient, and at least one of an aggregate internal review score of the provider and an aggregate external review score of the provider, wherein the aggregate internal review score of the provider is derived from one or more internal review records corresponding to the provider, each internal review record including an internal score provided by a historical patient of the provider and a demographic data value of the historical patient, and the aggregate internal review score of the provider is computed from the internal review records having a demographic data value of the historical patient that is the same as the demographic data value of the patient, wherein the aggregate external review score of the provider is derived from one or more external review records corresponding to the provider, each external review record including an external score provided by a reviewer and a demographic data value of the reviewer, and the aggregate external review score of the provider is computed from the external review records having a demographic data value of the reviewer that is the same as the demographic data value of the patient; and a predictive engine capable of machine learning that classifies each patient-provider combination of the feature space into one of two or more classes according to the set of values of the patient-provider combination.

“2. The healthcare recommender system of claim 1 wherein the demographic data value of the patient, historical patient, and reviewer is selected from the group consisting of gender, race, ethnicity, geographical location, age range, and medical condition.

“3. The healthcare recommender system of claim 1 wherein the unique government-issued identifier of the provider is a National Provider Identifier issued by the Centers for Medicare and Medicaid Services.

“4. The healthcare recommender system of claim 1 wherein the aggregate internal review score is computed from at least one structured survey in which the provider is assessed by the historical patient.

“5. The healthcare recommender system of claim 1 wherein the aggregate external review score is a sentiment value computed from a natural language processing of at least one review of the provider authored by the reviewer and from a third party website or database.

“6. The healthcare recommender system of claim 5 wherein a name of the reviewer is analyzed to determine an age and/or gender of the reviewer, and wherein at least one of the age and gender is the demographic data value of the reviewer.

“7. A computer-implemented method for matching healthcare providers with patients comprising the steps: (i) creating a provider record corresponding to a provider and populating the provider record with at least a unique government-issued identifier of the provider; (ii) creating a patient record corresponding to a patient and populating the patient record with at least one demographic data value of the patient; (iii) receiving one or more ratings of the provider from one or more historical patients of the provider, and for each rating creating an internal review record populated with the rating and a demographic data value of the historical patient, wherein the internal review record is correlated to the unique government-issued identifier of the provider; (iv) retrieving one or more reviews of the provider authored by one or more reviewers from one or more third party websites or databases, and for each review: computing a numerical sentiment value by natural language processing of the review and determining at least one demographic data value of the reviewer, and creating an external review record populated with the sentiment value and the demographic data value of the reviewer, wherein the external review record is correlated to the unique government-issued identifier of the provider; (v) generating a multidimensional feature space relating to one patient and defining one or more patient-provider combinations, wherein each patient-provider combination comprises a set of values that at least includes the demographic data value of the patient, an aggregate internal review score computed from the internal review records having a demographic data value of the historical patient that is the same as the demographic data value of the patient, and an aggregate external review score computed from the external review records having a demographic data value of the reviewer that is the same as the demographic data value of the patient; and (vi) classifying by a predictive engine capable of machine learning each patient-provider combination of the feature space into one of two or more classes according to the set of values of the patient-provider combination.

“8. The method of claim 7 wherein the demographic data value of the patient, historical patient, and reviewer is selected from the group consisting of gender, race, ethnicity, geographical location, age range, and medical condition.

“9. The method of claim 7 wherein the unique government-issued identifier of the provider is a National Provider Identifier issued by the Centers for Medicare and Medicaid Services.

“10. A computer-implemented method for matching a patient with one or more healthcare providers comprising the steps: (i) receiving a request from the patient to be matched with the providers, wherein the patient has a corresponding patient record comprising at least one demographic data value; (ii) generating a multidimensional feature space relating to the patient and defining one or more patient-provider combinations, wherein each patient-provider combination comprises a set of values that at least includes the demographic data value of the patient and at least one of an aggregate internal review score of the provider and an aggregate external review score of the provider, wherein the aggregate internal review score of the provider is derived from one or more internal review records corresponding to the provider, each internal review record including an internal score provided by a historical patient of the provider and a demographic data value of the historical patient, and the aggregate internal review score of the provider is computed from the internal review records having a demographic data value of the historical patient that is the same as the demographic data value of the patient, wherein the aggregate external review score of the provider is derived from one or more external review records corresponding to the provider, each external review record including an external score provided by a reviewer and a demographic data value of the reviewer, and the aggregate external review score of the provider is computed from the external review records having a demographic data value of the reviewer that is the same as the demographic data value of the patient; (iii) classifying by a predictive engine capable of machine learning each patient-provider combination of the feature space into one of two or more classes according to the set of values of the patient-provider combination, wherein at least one class represents optimal provider matches; (iv) presenting to the patient one or more providers classified in the class representing optimal provider matches; and (v) receiving a selection of one provider from the patient.

“11. The method of claim 10 wherein the demographic data value of the patient, historical patient, and reviewer is selected from the group consisting of gender, race, ethnicity, geographical location, age range, and medical condition.

“12. The method of claim 10 wherein the unique government-issued identifier of the provider is a National Provider Identifier issued by the Centers for Medicare and Medicaid Services.

“13. The method of claim 10 wherein the aggregate internal review score is computed from at least one structured survey in which the provider is assessed by the historical patient.

“14. The method of claim 10 wherein the aggregate external review score is a sentiment value computed from a natural language processing of at least one review of the provider authored by the reviewer and from a third party website or database.

“15. The method of claim 14 wherein a name of the reviewer is analyzed to determine an age and/or gender of the reviewer, and wherein at least one of the age and gender is the demographic data value of the reviewer.”

URL and more information on this patent application, see: Roots, Kurt; Nadler, Jeffrey. Patient-Provider Healthcare Recommender System. Filed August 1, 2018 and posted February 7, 2019. 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=%2220190043606%22.PGNR.&OS=DN/20190043606&RS=DN/20190043606

(Our reports deliver fact-based news of research and discoveries from around the world.)