INTELLIGENT CONTINUITY OF CARE INFORMATION SYSTEM AND METHOD
专利汇可以提供INTELLIGENT CONTINUITY OF CARE INFORMATION SYSTEM AND METHOD专利检索，专利查询，专利分析的服务。并且An intelligent continuity of care information system comprises a repository of patient data including clinical and social information associated with a plurality of patients updated and received from a plurality of clinical and social service organizations and data sources, at least one predictive model using clinical and social factors derived from the patient data to extract both explicitly encoded information and implicit information about the patient's clinical and social information, and a user interface operable to present a selected view of the patient data and analysis associated with the at least one particular patient to a user via a computing device, each view being composed of a selected collection of a plurality of widgets each presenting a focused subset of patient data and analysis.，下面是INTELLIGENT CONTINUITY OF CARE INFORMATION SYSTEM AND METHOD专利的具体信息内容。
What is claimed is:
This non-provisional patent application claims the benefit of U.S. Provisional Patent Application No. 61/891,054 filed on Oct. 15, 2013, which is incorporated herein by reference in its entirety. This application is related to the following non-provisional patent applications, all of which are incorporated herein by reference: Ser. No. 13/613,980 filed on Sep. 13, 2012, entitled “Clinical Predictive and Monitoring System and Method”; Ser. No. 14/018,514 filed on Sep. 5, 2013, entitled “Clinical Dashboard User Interface System and Method”; and Ser. No. 14/326,863 filed on Jul. 9, 2014, entitled “Patient Care Surveillance System and Method”.
The present disclosure relates to a computer system, and more particularly to an intelligent continuity of care information system and method.
Historically, coordinating the care of indigent and vulnerable patients has been extraordinarily difficult. These individuals suffer disproportionately from job loss, substance abuse, homelessness, and low health literacy—conditions which subsequently lead to poor health outcomes. In addition to medical treatment, indigent patients often need access to community-based social sector organizations that provide a distinct and complementary set of services, such as housing, transportation, and employment assistance. In many cases, social services are just as vital as healthcare services to achieving long-term health goals.
Care providers require access to updated, relevant, and complete patient information to effectively coordinate care between health and social services. That information, however, typically resides in separate data systems which do not interact with each other. Aligning the efforts of healthcare and social service organizations represents a massive logistical feat rarely achieved for the individual patient, and despite the best efforts of individual care providers and organizations to meet patients' complex needs, many still fall through the gaps of a patchwork safety net.
Strengthening coordination between health care and social services is a significant challenge for communities throughout the United States. Despite the difficulties, solutions to these nuanced problems must be pursued as the future of health care depends on the increased efficiency that can be achieved through improved coordination of care. Failures of care coordination have been estimated to increase healthcare costs by $25-$45 billion annually (“Health Policy Brief: Care Transitions,” Health Affairs, Sep. 13, 2012). Systems that facilitate care transitions have the potential to reduce healthcare expenses while improving patient health. The present disclosure describes an intelligent system and method of collecting and presenting information to facilitate the continuity of patient care.
The data received by the patient care and management system 11 may include electronic medical records (EMR) that include both clinical and non-clinical data. The EMR clinical data may be received from entities such as hospitals, clinics, pharmacies, laboratories, and health information exchanges, including: vital signs and other physiological data; data associated with comprehensive or focused history and physical exams by a physician, nurse, or allied health professional; medical history; prior allergy and adverse medical reactions; family medical history; prior surgical history; emergency room records; medication administration records; culture results; dictated clinical notes and records; gynecological and obstetric history; mental status examination; vaccination records; radiological imaging exams; invasive visualization procedures; psychiatric treatment history; prior histological specimens; laboratory data; genetic information; physician's notes; networked devices and monitors (such as blood pressure devices and glucose meters); pharmaceutical and supplement intake information; and focused genotype testing.
The EMR non-clinical data may include, for example, social, behavioral, lifestyle, and economic data; type and nature of employment; job history; medical insurance information; hospital utilization patterns; exercise information; addictive substance use; occupational chemical exposure; frequency of physician or health system contact; location and frequency of habitation changes; predictive screening health questionnaires such as the patient health questionnaire (PHQ); personality tests; census and demographic data; neighborhood environments; diet; gender; marital status; education; proximity and number of family or care-giving assistants; address; housing status; social media data; and educational level. The non-clinical patient data may further include data entered by the patients, such as data entered or uploaded to a patient portal.
Additional sources or devices of EMR data may provide, for example, lab results, medication assignments and changes, EKG results, radiology notes, daily weight readings, and daily blood sugar testing results. These data sources 13 may be from different areas of the hospital, clinics, patient care facilities, patient home monitoring devices, among other available clinical or healthcare sources.
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The patient care and management system 11 may further receive data from health information exchanges (HIE) 16. HIEs are organizations that mobilize healthcare information electronically across organizations within a region, community or hospital system. HIEs are increasingly developed to share clinical and non-clinical patient data between healthcare entities within cities, states, regions, or within umbrella health systems. Data may arise from numerous sources such as hospitals, clinics, consumers, payers, physicians, labs, outpatient pharmacies, ambulatory centers, nursing homes, and state or public health agencies.
A subset of HIEs connect healthcare entities to community organizations that do not specifically provide health services, such as non-governmental charitable organizations, social service agencies, and city agencies. The patient care and management system 11 may receive data from these social services organizations and social-to-health information exchanges 17, which may include, for example, information on daily living skills, availability of transportation to medical appointments, employment assistance, training, substance abuse rehabilitation, counseling or detoxification, rent and utilities assistance, homeless status and receipt of services, medical follow-up, mental health services, meals and nutrition, food pantry services, housing assistance, temporary shelter, home health visits, domestic violence, appointment adherence, discharge instructions, prescriptions, medication instructions, neighborhood status, and ability to track referrals and appointments.
Another source of data include social media or social network services, such as FACEBOOK, GOOGLE+, TWITTER, and other websites can provide information such as the number of family members, relationship status, identification of individuals who may help care for a patient, and health and lifestyle preferences that may influence health outcomes. These social media data may be received from the websites, with the individual's permission, and some data may come directly from a user's computing devices (mobile phones, tablet computers, laptops, etc.) as the user enters status updates, for example.
These non-clinical or social patient data may potentially provide a much more realistic and accurate depiction of the patient's overall holistic healthcare environment. Augmented with such non-clinical patient data, the analysis and predictive modeling to identify patients at high-risk of readmission or disease recurrence become much more robust and accurate.
The patient care and management system 11 is further adapted to receive and display user preference and system configuration data from a plurality of user interface computing devices (e.g., fitness monitoring bracelets/watches, mobile devices, tablet computers, laptop computers, desktop computers, servers, etc.) 18 in a wired or wireless manner. These user interface devices 18 are equipped to display a plurality of clinical/social/patient views of the intelligent continuity of care information system 10 to present data and reports in an organized and intelligent manner that can be easily adapted to the user's role or responsibilities. The graphical user interface are further adapted to receive the user's (healthcare personnel, social services, and patient) input of personal preferences and configurations, etc. The plurality of user interface computing devices 18 may also be data sources 13 to the intelligent continuity of care information system 10 and the patient care and management system 11.
For example, a clinician (physicians, nurses, physician assistants, and other healthcare personnel) may use the clinical view to immediately display a list of patients that have the highest congestive heart failure risk scores, e.g., top n numbers or top x %. The clinical view may also provide information on a particular patient's allergies, health issues or red flags related to a patient's care, medical prescriptions, most prominent problems, relevant historic lab results, etc. A patient may access the patient view to obtain information about his/her medical history calendar appointments, medication prescriptions, preventative health regimen, etc. A social case worker may access a social view that provides information on a patient's allergies, demographic data, height and weight, insurance coverage, upcoming appointments, most prominent problems, referrals, etc. The data may be transmitted, presented, and displayed to the clinician/user in the form of web pages, web-based messages, text files, video messages, multimedia messages, text messages, e-mail messages, and in a variety of other suitable ways and formats.
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The patient care and management system 11 further receives user input and data from data sources 13 including a number of additional data generating devices 22, including RFID devices that are worn, associated with, or affixed to patients, hospital personnel, hospital equipment, hospital instruments, medical devices, supplies, and medication. A plurality of RFID sensors are distributed in the hospital rooms, hallways, equipment rooms, supply closets, etc. that are configured to detect the presence of RFID tags so that movement, usage, and location can be easily determined and monitored. Further, a plurality of stationary and mobile video cameras is distributed in the hospital to enable patient monitoring and to identify biological changes in the patient. The additional data generating devices and sources 22 may also include biometric sensors that are located in hospital rooms or other selected locations.
The data extraction process 24 extracts clinical and non-clinical data from the plurality of data sources 13 in real-time or in historical batch files either directly or through the Internet, using various technologies and protocols. Preferably in real-time, the data cleansing process 26 “cleans” or pre-processes the data, putting structured data in a standardized format and preparing unstructured text for natural language processing (NLP) to be performed in the disease/risk logic module 30 described below. The system may also receive “clean” data and convert them into desired formats (e.g., text date field converted to numeric for calculation purposes).
The data manipulation process 28 may analyze the representation of a particular data feed against a meta-data dictionary and determine if a particular data feed should be re-configured or replaced by alternative data feeds. For example, a given hospital EMR may store the concept of “maximum creatinine” in different ways. The data manipulation process 28 may make inferences in order to determine which particular data feed from the EMR would best represent the concept of “creatinine” as defined in the meta-data dictionary and whether a feed would need particular re-configuration to arrive at the maximum value (e.g., select highest value).
The data integration logic module 22 then passes the pre-processed data to a disease/risk logic module 30. The disease/risk logic module 30 is operable to calculate a risk score associated with an identified disease or condition for each patient and to identify those patients who should receive targeted intervention and care. The disease/risk logic module 30 includes a de-identification/re-identification process 32 that is adapted to remove all protected health information according to HIPAA standards before the data is transmitted over the Internet. It is also adapted to re-identify the data. Protected health information that may be removed and added back may include, for example, name, phone number, facsimile number, email address, social security number, medical record number, health plan beneficiary number, account number, certificate or license number, vehicle number, device number, URL, all geographical subdivisions smaller than a state, including street address, city, county, precinct, zip code, and their equivalent geocodes (except for the initial three digits of a zip code, if according to the current publicly available data from the Census Bureau), Internet Protocol number, biometric data, and any other unique identifying number, characteristic, or code.
The disease/risk logic module 30 further includes a disease identification process 34. The disease identification process 34 is adapted to identify one or more diseases or conditions of interest for each patient. The disease identification process 34 considers data such as lab orders, lab values, clinical text and narrative notes, and other clinical and historical information to determine the probability that a patient has a particular disease. Additionally, during disease identification, natural language processing is conducted on unstructured clinical and non-clinical data to determine the disease or diseases that the physician believes are prevalent. This process 34 may be performed iteratively over the course of many days to establish a higher confidence in the disease identification as the physician becomes more confident in the diagnosis. New or updated patient data may not support a previously identified disease, and the system would automatically remove the patient from that disease list. The natural language processing combines a rule-based model and a statistically-based learning model.
The disease identification process 34 utilizes a hybrid model of natural language processing, which combines a rule-based model and a statistically-based learning model. During natural language processing, raw unstructured data, for example, physicians' notes and reports, first go through a process called tokenization. The tokenization process divides the text into basic units of information in the form of single words or short phrases by using defined separators such as punctuation marks, spaces, or capitalizations. Using the rule-based model, these basic units of information are identified in a meta-data dictionary and assessed according to predefined rules that determine meaning. Using the statistical-based learning model, the disease identification process 34 quantifies the relationship and frequency of word and phrase patterns and then processes them using statistical algorithms. Using machine learning, the statistical-based learning model develops inferences based on repeated patterns and relationships. The disease identification process 34 performs a number of complex natural language processing functions including text pre-processing, lexical analysis, syntactic parsing, semantic analysis, handling multi-word expression, word sense disambiguation, and other functions.
For example, if a physician's notes include the following: “55 yo m c h/o dm, cri. now with adib rvr, chfexac, and rle cellulitis going to 10W, tele.” The data integration logic 22 is operable to translate these notes as: “Fifty-five-year-old male with history of diabetes mellitus, chronic renal insufficiency now with atrial fibrillation with rapid ventricular response, congestive heart failure exacerbation and right lower extremity cellulitis going to 10 West and on continuous cardiac monitoring.”
Continuing with the prior example, the disease identification process 34 is adapted to further ascertain the following: 1) the patient is being admitted specifically for atrial fibrillation and congestive heart failure; 2) the atrial fibrillation is severe because rapid ventricular rate is present; 3) the cellulitis is on the right lower extremity; 4) the patient is on continuous cardiac monitoring or telemetry; and 5) the patient appears to have diabetes and chronic renal insufficiency.
The disease/risk logic module 30 further comprises a predictive model process 36 that is adapted to predict the risk of particular disease, condition, or adverse clinical and non-clinical event of interest according to one or more predictive models. For example, if the hospital desires to determine the level of risk for future readmission for all patients currently admitted with heart failure, the heart failure predictive model may be selected for processing patient data. However, if the hospital desires to determine the risk levels for all internal medicine patients for any cause, an all-cause readmissions predictive model may be used to process the patient data. As another example, if the hospital desires to identify those patients at risk for short-term and long-term diabetic complications, the diabetes predictive model may be used to target those patients. Other predictive models may include HIV readmission, diabetes identification, risk for cardio-pulmonary arrest, kidney disease progression, acute coronary syndrome, pneumonia, cirrhosis, all-cause disease-independent readmission, colon cancer pathway adherence, risk of hunger, loss of housing, and others.
Continuing to use the prior example, the predictive model for congestive heart failure may take into account a set of risk factors or variables, including the worst values for laboratory and vital sign variables such as: albumin, total bilirubin, creatine kinase, creatinine, sodium, blood urea nitrogen, partial pressure of carbon dioxide, white blood cell count, troponin-I, glucose, internationalized normalized ratio, brain natriuretic peptide, pH, temperature, pulse, diastolic blood pressure, and systolic blood pressure. Further, non-clinical factors are also considered, for example, the number of home address changes in the prior year, risky health behaviors (e.g., use of illicit drugs or substance), number of emergency room visits in the prior year, history of depression or anxiety, and other factors. The predictive model specifies how to categorize and weight each variable or risk factor, and the method of calculating the predicted probably of readmission or risk score. In this manner, the patient care and management system 11 is able to stratify, in real-time, the risk of each patient that arrives at a hospital or another healthcare facility. Therefore, those patients at the highest risks are automatically identified so that targeted intervention and care may be instituted. One output from the disease/risk logic module 30 includes the risk scores of all the patients for a particular disease or condition. In addition, the module 30 may rank the patients according to the risk scores, and provide the identities of those patients at the top of the list. For example, the hospital may desire to identify the top 20 patients most at risk for congestive heart failure readmission, and the top 5% of patients most at risk for cardio-pulmonary arrest in the next 24 hours. Other diseases and conditions that may be identified using predictive modeling include, for example, HIV readmission, diabetes identification, kidney disease progression, colorectal cancer continuum screening, meningitis management, acid-base management, anticoagulation management, etc.
The disease/risk logic module 30 may further include a natural language generation module 38. The natural language generation module 38 is adapted to receive the output from the predictive model 36 such as the risk score and risk variables for a patient, and “translate” the data to present, in the form of natural language, the evidence that the patient is at high-risk for that disease or condition. This module 30 thus provides the intervention coordination team with additional information that supports why the patient has been identified as high-risk for the particular disease or condition. In this manner, the intervention coordination team may better formulate the targeted inpatient and outpatient intervention and treatment plan to address the patient's specific situation.
The natural language generation module 38 also provides summary information about a patient, such as demographic information, medical history, primary reason for the visit, etc. This summary statement provides a quick snapshot of relevant information about the patient in narrative form.
The disease/risk logic module 30 further includes an artificial intelligence (AI) model tuning process 40. The artificial intelligence model tuning process 38 utilizes adaptive self-learning capabilities using machine learning technologies. The capacity for self-reconfiguration enables the patient care and management system 11 to be sufficiently flexible and adaptable to detect and incorporate trends or differences in the underlying patient data or population that may affect the predictive accuracy of a given algorithm. The artificial intelligence model tuning process 40 may periodically retrain a selected predictive model for improved accurate outcome to allow for selection of the most accurate statistical methodology, variable count, variable selection, interaction terms, weights, and intercept for a local health system or clinic. The artificial intelligence model tuning process 40 may automatically modify or improve a predictive model in three exemplary ways. First, it may adjust the predictive weights of clinical and non-clinical variables without human supervision. Second, it may adjust the threshold values of specific variables without human supervision. Third, the artificial intelligence model tuning process 40 may, without human supervision, evaluate new variables present in the data feed but not used in the predictive model, which may result in improved accuracy. The artificial intelligence model tuning process 40 may compare the actual observed outcome of the event to the predicted outcome then separately analyze the variables within the model that contributed to the incorrect outcome. It may then re-weigh the variables that contributed to this incorrect outcome, so that in the next reiteration those variables are less likely to contribute to a false prediction. In this manner, the artificial intelligence model tuning process 40 is adapted to reconfigure or adjust the predictive model based on the specific clinical setting or population in which it is applied. Further, no manual reconfiguration or modification of the predictive model is necessary. The artificial intelligence model tuning process 40 may also be useful to scale the predictive model to different health systems, populations, and geographical areas in a rapid timeframe.
As an example of how the artificial intelligence model tuning process 40 functions, the sodium variable coefficients may be periodically reassessed to determine or recognize that the relative weight of an abnormal sodium laboratory result on a new population should be changed from 0.1 to 0.12. Over time, the artificial intelligence model tuning process 38 examines whether thresholds for sodium should be updated. It may determine that in order for the threshold level for an abnormal sodium laboratory result to be predictive for readmission, it should be changed from, for example, 140 to 136 mg/dL. Finally, the artificial intelligence model tuning process 40 is adapted to examine whether the predictor set (the list of variables and variable interactions) should be updated to reflect a change in patient population and clinical practice. For example, the sodium variable may be replaced by the NT-por-BNP protein variable, which was not previously considered by the predictive model.
The disease/risk logic module 30 may further include a data analytics module 41 that analyzes the data processed by the disease/risk logic module 30 and performs certain data processing procedures related to the presentation of the data by the widgets 54 (
The results from the disease/risk logic module 30 are provided to the hospital personnel, such as the intervention coordination team, other caretakers, and the patient, by a data presentation and system configuration logic module 42. The data presentation logic module 42 includes an intelligent continuity of care interface system 10 that is adapted to provide various focused and organized views into data and information available on the patient care and management system 11. A user (e.g., hospital personnel, administrator, intervention coordination team, social worker, patient, and family) is able to find the specific data they seek through clinical/social/patient views characterized by simple and clear visual navigation cues, icons, windows, and devices.
The data presentation and system configuration logic module 40 further includes a messaging interface 46 that is adapted to generate output messaging code in forms such as HL7 messaging, text messaging, e-mail messaging, multimedia messaging, web pages, web portals, REST, XML, computer generated speech, constructed document forms containing graphical, numeric, and text summary of the risk assessment, reminders, and recommended actions. The interventions generated or recommended by the patient care and management system 11 may include: risk score report to the primary physician to highlight risk of readmission for their patients; score report via new data field input into the EMR for use by population surveillance of entire population in hospital, covered entity, accountable care population, or other level of organization within a healthcare providing network; comparison of aggregate risk of readmissions for a single hospital or among hospitals to allow risk-standardized comparisons of hospital readmission rates; automated incorporation of score into discharge summary template, continuity of care document (within providers in the inpatient setting or to outside physician consultants and primary care physicians), HL7 message to facility communication of readmission risk transition to nonhospital physicians; and communicate subcomponents of the aggregate social-environmental score, clinical score and global risk score. These scores would highlight potential strategies to reduce readmissions including: generating optimized medication lists; allowing pharmacies to identify those medication on formulary to reduce out-of-pocket cost and improve outpatient compliance with the pharmacy treatment plan; flagging nutritional education needs; identifying transportation needs; assessing housing instability to identify need for nursing home placement, transitional housing, Section 8 HHS housing assistance; identifying poor self-regulatory behavior for additional follow-up phone calls; identifying poor social network scores leading to recommendation for additional in home RN assessment; flagging high substance abuse score for consultation of rehabilitation counseling for patients with substance abuse issues.
This output may be transmitted wirelessly or via LAN, WAN, the Internet, and delivered to healthcare facilities' electronic medical record stores, user electronic devices (e.g., pager, text messaging program, mobile telephone, tablet computer, mobile computer, laptop computer, desktop computer, and server), health information exchanges, and other data stores, databases, devices, and users. The patient care and management system 11 may automatically generate, transmit, and present information such as high-risk patient lists with risk scores, natural language generated text, reports, recommended actions, alerts, Continuity of Care Documents, flags, appointment reminders, and questionnaires.
The data presentation and system configuration logic module 40 further includes a system configuration interface 48. Local clinical preferences, knowledge, and approaches may be directly provided as input to the predictive models through the system configuration interface 48. This system configuration interface 48 allows the institution or health system to set or reset variable thresholds, predictive weights, and other parameters in the predictive model directly.
The exemplary intelligent continuity of care information system 10 is adapted to provide a real-time electronic summary or view of a patient's entire medical and social history, no matter how large, complex, or distributed the information may be. In a preferred embodiment, the intelligent continuity of care information system 10 utilizes analyses and data provided by the patient care and management system 11 that uses electronic predictive models, natural language processing, artificial intelligence, and other sophisticated algorithms and analytics tools to processes non-standardized, repetitious and unstructured data. The patient care and management system 11 is described in U.S. patent application Ser. No. 13/613,980, incorporated herein by reference in its entirety.
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The system 10 further provides the ability to generate templates for multiple customized clinical views, social views and patient views on organization, department, role, disease/condition, and individual levels. For example, a hospital may define an emergency department physician template, an emergency department nurse template, a cardiology physician template, an emergency department patient template, a cardiology patient template, etc. Each template defines a collection of widgets that provides relevant and critical information for the intended user. Further, each user may personalize the collection of widgets. For example, emergency department physician X may prefer to organize information displayed on the screen in a certain order, and she is able to configure the widgets defined in the emergency department physician template according to her personal preferences and needs. Another clinical personnel, nurse Y in cardiology, may configure her personalized clinical view to suit her own preferences and needs. Additionally, clinical views may be created to tailor to specific diseases or conditions. For example, a clinical view may focus on information specific to a patient with diabetes, heart condition, or hypertension. A social service organization may choose to omit a certain widget and instead select a subset of widgets from among all available social view widgets for case intake personnel at the organization, for example. The case managers at the same organization may customize and organize the social widgets to suit the demands of their jobs. Further, a patient may also choose and organize the widgets so that her view of the data is customized and tailored to her needs, and she may also permit access by a family member to have limited access by eliminating some of the widgets in his customized view.
The following are brief descriptions of selected exemplary widgets and the type of information that is provided by each widget.
Allergies Widget—Provides a patient's allergies displayed with reaction symptoms and severity to help detect and prevent allergic reactions. The allergy information is extracted from the patient's Electronic Medical Record (EMR) as well as from clues found in unstructured text such as physician's notes or patient input/comments. This widget is preferably defined to be accessible from clinical, social, and patient views.
Chart Check Issues Widget—During patient care transitions, clinical events that should be tracked or monitored may sometimes be missed by the receiving care team. By analyzing physician notes, action items or follow-up labs can be visually flagged and displayed for the receiving care team during patient care transition. This widget is preferably defined to be accessible from the clinical view.
Demographic Information Widget—A patient's demographic information helps inform decisions, and is often used when assessing eligibility and enrolling individuals for services. The demographic information is extracted from the patient's Electronic Medical Record (EMR) as well as from clues found in unstructured text such as physician's notes or patient input/comments. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Documents On File Widget—Provides access to a list of stored documents that are often used for assessing eligibility and enrolling individuals for services. This view enables access to images of documents that are available from source systems across collaborating organizations. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Height and Weight Widget—Provides records of height and weight that enable the patient care team to track and flag significant fluctuations and take action if necessary. The height and weight information are typically not available for social service settings, thus their availability may provide the case worker additional insights on how to better take care of the patient. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Insurance Coverage and Assistance Widget—Provides insurance coverage, assistance, and benefits information often used for assessing eligibility and enrolling individuals for services. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Prior Encounters Widget—Provides information on the patient's prior encounters with medical, community, and social organizations which may be helpful to inform what other needs an individual may have, and whether they are getting the necessary services to meet those needs. The number of encounters presented may be tailored or limited to different views and different types of user roles in each view. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Upcoming Appointments Widget—Provides information on the patient's upcoming appointments with medical, community, and social organizations which may be helpful to inform what other needs an individual may have, and whether they are getting the necessary services to meet those needs. The number of encounters presented may be tailored or limited to different views and different types of user roles in each view. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Medication Reconciliation Widget—Provides information about medications to help the patient adhere to the medication regimen and help providers make clinical decisions. This widget may provide information such as names of current and discontinued medications, medication possession ratio (the percentage of time the patient has had access to the medication), cost, flagged for review due to a recent change in the patient's status, image of the medication, and patient education materials. This information is populated by the patient care and management system 11 using new analytics and data extraction methods. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Most Prominent Problems Widget—Provides a list of the most prominent (e.g., severe, urgent, chronic, most relevant) medical issues or conditions for the patient. This widget eliminates the problem of redundancies and irrelevant information that most EMR records have. This information is extracted from structured and unstructured data fields in the EMR. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Complete Problem List Widget—Provides a complete list of the patient's medical issues without redundancies and irrelevant information. This information is extracted from structured and unstructured data fields in the EMR. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Patient Summary Widget—Provides a summary of the patient's medical history, including the most recent discharge summary. Through natural language processing and generation, the clinical continuity of care information system displays a succinct text summary of the patient's demographics, reason for visit, and relevant medical and utilization history generated by the clinical predictive and monitoring system. This avoids the time and resource-intensive process of sifting through large volumes of disparate and disorganized patient history records during limited clinical time. This widget is preferably defined to be accessible from the clinical and social views.
Predictive Analytics Widget—Provides an identification of a patient's risk for adverse events. The patient care and management system 11 aggregates and analyzes available patient clinical and social factors, and uses advanced algorithms to calculate a patient's risk for adverse events, which can then be displayed to facilitate delivery of targeted interventions to prevent the adverse event. This widget is preferably defined to be accessible from the clinical view.
Referrals Widget—Provides a record of past referrals to social service programs or organizations. This information is extracted from clues found in unstructured text such as physician's or nurse's notes. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Relevant Historic Abnormal Results Widget—Provides any relevant historic abnormal lab results that would be helpful to inform clinical decisions. The algorithms may adapt to criteria including but not limited to: a defined time period, outside of a range that is typical for other patients with similar medical history and similar settings, association with certain disease conditions, and the patient's medical history. The patient care and management system 11 also augments the algorithms by using clues found in unstructured text. This widget is preferably defined to be accessible from the clinical view.
Relevant Recent Abnormal Results Widget—Provides any relevant recent abnormal lab results that would be helpful to inform clinical decisions. The algorithms may adapt to criteria including but not limited to: a defined time period, outside of a range that is typical for other patients with similar medical history and similar settings, association with certain disease conditions, and the patient's medical history. The patient care and management system 11 also augments the algorithms by using clues found in unstructured text. This widget is preferably defined to be accessible from the clinical view.
Relevant Unresolved Orders and Labs Widget—Provides reminders to complete any unresolved orders and labs. The algorithms may adapt to criteria including but not limited to: a defined time period, outside of a range that is typical for other patients with similar medical history and similar settings, association with certain disease conditions, and the patient's medical history. The patient care and management system 11 also augments the algorithms by using clues found in unstructured text. This widget is preferably defined to be accessible from the clinical view.
Current Health Issues Widget—Provides the patient with information on health issues currently experienced by the patient. The patient care and management system 11 populates this information for display from the EMR and clues found in unstructured text. This widget is preferably defined to be accessible from the clinical and patient views.
Preventive Health Widget—Provides the patient with information on preventive health activities and due dates. The patient care and management system 11 populates this information for display from the EMR and clues found in unstructured text. This widget is preferably defined to be accessible from the clinical and patient views.
Recent Test Results Widget—Provides information to the patient about his/her recent lab results. The patient care and management system 11 populates this information for display from the EMR and clues found in unstructured text. This widget is preferably defined to be accessible from the clinical and patient views.
Diabetes Complications Widget—Provides information about the patient's diabetes complications to help inform clinical decisions. The patient care and management system 11 populates this information for display from the EMR and clues found in unstructured text. This widget is preferably defined to be accessible from the clinical view and from a focused diabetes view.
Previous Glycemic Control Record Widget—Provides information about the patient's previous glycemic control record to help inform clinical decisions. The patient care and management system 11 populates this information for display from the EMR and clues found in unstructured text. This widget is preferably defined to be accessible from the clinical view and from a focused diabetes view.
Diagnostic Information Widget—Provides information about the patient's diabetes diagnostic information to help inform clinical decisions. The patient care and management system 11 populates this information for display from the EMR and clues found in unstructured text. This widget is preferably defined to be accessible from the clinical view and from a focused diabetes view.
Relevant Results Widget—Provides relevant lab results to help inform clinical decisions. The patient care and management system 11 populates this information for display from EMR and clues found in unstructured text. This widget is preferably defined to be accessible from the clinical view and from a focused diabetes view.
Previous BP Records Widget—Provides the patient's blood pressure records to help inform clinical decisions. The patient care and management system 11 populates this information for display from the EMR and clues found in unstructured text. This widget is preferably defined to be accessible from the clinical view and from a focused hypertension view.
Processing and Translating Clinical Notes Widget—Provides a simplified version of clinical or physician notes to help the patient understand information from medical encounters. In other words, medical jargon, abbreviations, and phrases are translated to layman terms to facilitate understanding. The system also detects and corrects inconsistencies and errors. The patient care and management system 11 uses natural language processing to extract and display a simplified summary of the patient's clinical notes. This widget is preferably defined to be accessible from the clinical and patient views.
Tailored Patient Care Plans With Patient Engagement Incentives Widget—Provides patient care plans that have been tailored to the specific patient to help the patient adhere to healthy behaviors and track progress toward goals. Prescriptive analytics considers the patient's medical and social data, including but not limited to missed appointments, medication adherence, functional status, social support, and comorbidities to generate recommendations and goals for a tailored patient care plan. As milestone goals are achieved (e.g., exercise and nutrition goals), patients may receive incentives (e.g. unlock new features, earn points to redeem health education materials, health apps, or health devices). This widget is preferably defined to be accessible from the patient view.
Patient Care Preferences Widget—Provides patient care plans that factor in the patient's preferences, such as location, religious practices, cultural beliefs, preferred rounding time, end of life care, etc. The patient can record their care preferences in a patient interface or view. Care providers can view these preferences in devising the patient care plan. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Interpreting Patient Questions and Concerns Widget—Patient can enter questions in a patient interface or view, and the questions are analyzed to identify resources that address topics or issues relevant to those questions. For example, if the patient's question is parsed and that it is recognized to contain a medical term, then definitions, FAQ, web pages, and other resources that are relevant to the medical term are identified and presented to the patient. The patient's questions are logged and can be accessed by healthcare and social service providers so that they may track and have follow-up discussions with the patient if necessary. The analytic logic of the patient care and management system 11 may flag or issue alerts to be displayed or transmitted to healthcare providers or social services providers if a concern requiring urgent attention is raised by analyzing the patient's questions. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Integration with Patient Devices Widget—Patients who are using mobile health monitoring devices and apps. (e.g., jawbone, fitbit, etc.) to measure and track certain physical or activity information, nutritional intake, and other activities can permit the integration of these devices with the intelligent continuity of care information system 10. The analytic logic of the patient care and management system 11 may further utilize this information to calculate risk scores for certain diseases or adverse events, for example. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Patient Assessments Widget—Using this view and interface, a patient may view, correct, and enter an assessment of their own medical history, social history, behaviors, and family history for review and discussion during an encounter with a healthcare provider or social service provider. Predictive analysis can be used to prepare initial assessments for review by the patient, to recommend questions for discussion during an encounter, and to identify educational materials based on the assessment results. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Patient Calendar Widget—The patient can use this view and interface to keep track of and adhere to appointments, self-management activities, medication regimen, medication refills, and healthy behaviors. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Tailored Patient Education Modules Widget—Patient education materials and resources are selected and tailored according to the patient's health conditions and to information such as questions, concerns, or assessment results that a patient has entered. Patient education materials can help patients to better understand and manage their medical conditions. This widget is preferably defined to be accessible from the clinical, social, and patient views.
Vitals Widget—Clinical users and the patient can view a patient's relevant vital measurements in a simple summary view (e.g., current and previous blood pressure and heart rate measurements). This widget is preferably defined to be accessible from the clinical and patient views.
The following is a description of a number of exemplary use cases for the intelligent continuity of care information system and method 10. In the interest of brevity and clarity, some procedures are not repeated in the description below. For example, it is assumed that each of the users (clinicians, social service providers, and patients) in the use cases below has proper authorization to access the intelligent continuity of care information system 10, and that each session to access the information is preceded with entry of proper credentials such as user name and password. User authentication may be handled in the intelligent continuity of care information system 10, in the patient care and management system 11, or in the home systems from which a user accesses the data in the system 10. Further, the patient has also provided consent to the access of his/her clinical and non-clinical information to clinical and social personnel. Consent management may be handled in the intelligent continuity of care information system 10, in the patient care and management system 11, or in the home systems from which a user accesses the data in the system 10.
A client enrolled at a senior center needs transportation services to attend his medical appointments at a clinic. He asks his case worker at the center for assistance. The case worker is provided access to the client's summary record. She reads the information provided by the Demographic Information widget and learns that the client's transportation is “unstable.” Looking at the information provided by the Referrals widget, she learns that he has received transportation assistance from a city initiative to provide bus passes to seniors. The Upcoming Appointments Widget further provides information about the appointment date, time, and location for the patient. The case worker calls the transportation service and arranges for her client to receive a bus pass in order to attend the appointment listed in the intelligent continuity of care information system portal. The positive result is that the client is able to attend his medical appointment.
A patient presents to the emergency department for nausea/vomiting and abdominal pain. He admits he has been on a drinking binge and is subsequently diagnosed with alcoholic hepatitis. Incidentally, he states that he is a recovering heroin addict and states that he needs to continue his methadone taper. He is very nervous about opioid withdrawal symptoms. The provider queries the intelligent continuity of care information system 10 using a hospital computer. The patient's record is presented for viewing by the provider. The provider quickly reads information provided by the patient's Patient Summary to determine the likely reason why he was admitted to the emergency department, noting the patient's alcoholism. The provider is able to see in the information provided in the Prior Encounters Widget that the patient has a recurring visit to a methadone clinic, indicating that the patient is enrolled in that clinic. The provider may access the Medication Reconciliation Widget and confirm the patient's current and accurate methadone dose. The provider also looks for any medication allergies as provided by the Allergies Widget before finalizing a treatment plan. The positive result is that the intelligent continuity of care information system 10 facilitated effective clinical decisions and more efficient care delivery to the patient.
A patient with a history of alcoholism is admitted to the hospital after being sent by ambulance from an outpatient rehab facility. He requires four days in the MICU for severe alcohol withdrawal and another three days in the hospital for deconditioning. He affirms his desire to return to rehab, but at discharge the hospital calls the patient's previous facility and no slots are available. The hospital's social worker queries the intelligent continuity of care information system 10, accesses the patient's Patient Summary Widget, and clicks on the link to the patient's most recent discharge summary to learn about any special instructions for follow up visits or issues to monitor. She also accesses the information in the patient's Most Prominent Problems Widget, and she determines that the patient is at risk of recidivism, withdrawal, and repeat hospitalization for alcohol abuse. She decides to find another alcohol rehabilitation facility that is located closer to the patient's home with the hope of making these appointments easier for the patient to attend. She refers the patient to the facility, and the updated referral information is displayed in the Referrals Widget. She also calls the facility directly and, after learning that they have space, arranges for transportation for the patient from the hospital to the facility. The positive result is that the patient is able to avoid disruption of rehab services, which reduces risk of an adverse event.
A patient with a known history of drug use and who is enrolled in a shelter's transitional housing and rehabilitation program returns to the shelter from the emergency department. He turns in his medications to the staff, who note that this is his fifth emergency department visit in the last eight weeks. They also note that each time, the client visits a different emergency department and returns with a prescription for narcotic analgesics. They are not sure if the client truly has pain, and strongly suspect that the client is exhibiting drug seeking behavior, which is setting back his drug rehab goals. They would like to notify medical providers caring for the patient. The case worker logs into the intelligent continuity of care information system 10, and accesses the Patient Summary and Prior Encounters Widgets, which show that the patient had four emergency department visits in the last eight weeks. She accesses the Medication Reconciliation Widget to learn of the current and discontinued medications that the patient has been prescribed. The records show that the patient has been prescribed narcotic analgesics. Through the information exchange portal, the case worker may query the client's other medical providers about whether the prescribed medications are truly necessary. She also informs them that the client is suspected of drug-seeking behavior. Finally, she adds the information as a note to the encounter and flags the widget red for attention. The positive result is that the intelligent continuity of care information system 10 allows the care provider to recognize and confirm a patient's risk factor for an adverse event, and also alert other providers of this risk.
A case worker is processing paperwork for a client seeking service at a social service agency for the first time. The client does not have his standard documents and does not know what coverage he and his family are enrolled in. The case worker also wants to know what other services the client is currently enrolled in. Having knowledge of current enrollments can inform identification of needs, inform development of a care plan for the patient, help the case worker coordinate care with other partner care providers, and prevent duplication of services. The case worker logs into the intelligent continuity of care information system 10, and accesses information provided by the patient's Patient Summary Widget and the Insurance Coverage and Assistance Widget. She is able to retrieve the patient's insurance information. She also views information provided by the Documents on File Widget, and retrieves the patient's birth certificate, driver's license, and last pay check stub on file. The patient brings in the most recent pay check stub needed for enrollment, which the case worker scans and is stored into a data store 50, which makes it accessible by the Documents on File Widget. To determine if the client has been using other services, the case worker reads information provided by the Referrals Widget and Prior Encounters Widget. The positive result is that the care provider is able to access information, which helps to efficiently enroll the client into necessary service programs and get the care needed promptly.
A patient John comes to the senior center almost every day, but has not shown up for the past few days. His case worker is concerned and calls him at home, but no one picks up the phone. Five days later, John returns to the center. It turns out he had been hospitalized with a severe asthma attack for the past few days because he had been mistakenly taking discontinued medication. The intelligent continuity of care information system 10 provides an alternative to the above scenario in which the center's staff was left unaware of their client's whereabouts. In the alternative, John's case worker logs into the intelligent continuity of care information system 10 and accesses the patient's summary records. When accessing John's information, she receives a notification through the IEP that John has been admitted to the hospital. She is able to look up the admission information and can view the discharge plan as it is completed. This allows system users to track client encounters, increasing efficiency and reducing loss to follow-up. Because of customized settings that allow senior center case workers to view medication records, the case worker is also able to view which discontinued medications John had been taking and to help him properly discard those medications. She is able to set an alert to notify her when John's medications are updated.
A patient Jane regularly receives provisions from the Dallas Food Pantry. She likes to select bread, potato chips, and cookies from the shelves of the pantry. However, Jane has uncontrolled diabetes and her doctor has warned that if she does not change her dietary habits, her vision will continue to worsen as a result of her diabetes. Previously, workers at the Dallas Food Pantry did not know that Jane is a diabetic and had not offered healthier food options to her that would help her manager her diet. The food case worker at the pantry can log into the intelligent continuity of care information system 10 and accesses the patient's Patient Summary as well as the Most Prominent Problems Widget. The food case worker can see that diabetes is a problem for Jane, Jane's BMI information in the Height and Weight Widget, and the recommendation in the Discharge Summary linked to the Patient's Summary that indicates weight loss is needed to reduce the severity of her diabetes and concurrent hypertension. If the food pantry has a program to identify foods that meet Jane's dietary guidelines, having Jane's health information helps Jane have access to those healthier food options. In this way, Jane's care provider at the hospital and her case manager at the food pantry are consistent in addressing Jane's health needs. Finally, Jane may have access to the patient view of her own profile. Jane can access customized features to help her manage her diabetes and hypertension. She may access the Tailored Patient Care Plans With Patient Engagement Incentives Widget that helps her adhere to healthier behaviors, and Tailored Patient Education Modules Widget to access informative materials that help her to have a better understanding of her condition.
Eligibility programs, such as Medicaid, may have renewal requirements once a year or more/less often. The Documents on File and Insurance Coverage and Assistance Widgets show expiration dates for certain types of paperwork. Alerts can be triggered to notify case managers when certain patient's eligibility is close to expiration or almost due for renewal. Sometimes clients may lose eligibility and may need additional social service assistance in these instances. A client may use the intelligent continuity of care information system 10 to coordinate services during any eligibility lapses. Because the intelligent continuity of care information system maintains records of patient needs and utilized services through the Most Prominent Problems, Medication Reconciliation, and Referrals Widgets, it serves as a way to continue service delivery while eligibility issues are being resolved.
Patients may need to fill out medical forms for service on-boarding. Patients often struggle with completing these forms accurately, due to barriers such as access to information, language, and literacy. Case workers may use the intelligent continuity of care information system 10 to access relevant client data and assist clients with completing these forms. Relevant information may be accessed by viewing information provided by a number of widgets: Medication Reconciliation, Insurance Coverage and Assistance, Documents on File, and Most Prominent Problems Widgets. If services are needed or alerts are triggered, case workers can help clients to enroll in needed services.
If a social services agency needs to call the ER or 911 on behalf of a patient, certain agency staff may gain access to necessary information to obtain the data needed to facilitate addressing the client's emergency. The intelligent continuity of care information system 10 may enable social service case workers, or paramedics at a social service agency, to view medically relevant information in a medical emergency. This information would include information provided by the Allergies, Medication Reconciliation, and Most Prominent Problems Widgets.
A homeless patient with a history of mental illness is admitted to the hospital and is found to have cancer. He leaves the hospital against medical advice to return to a shelter after being hospitalized for two weeks. The patient has unstable moods and is intermittently uncooperative. It was unclear to clinical providers if the patient's lack of cooperation was due to denial, his personality disorder, or lack of understanding/insight. The patient also reported that he had been in prison about four months prior to admission and had been transferred to a nursing home but was unable to articulate why. The intelligent continuity of care information system 10 allows the provider team to view social and medical records collected at a social service agency. In this case, the care provider logs into the intelligent continuity of care information system 10 and accesses the patient's Demographic Information Widget. He also reads in the Referrals and Prior Encounters Widgets that the patient has received care from the shelter. The provider also reads the patient's information provided by the Medication Reconciliation, Most Prominent Problems, Relevant Recent Abnormal Results, Relevant Unresolved Orders and Labs, and Prior Encounters Widgets. With this information, the provider is able to piece together the patient's medical history in real time without waiting for the full medical history from the patient's previous provider. Therefore, a better understanding of the patient's mental and physical condition is helpful to the provider in formulating a treatment plan.
A patient seeks services at a clinic, claiming that he received inadequate care from his previous care provider. The case worker wants to know the patient's other clinics in order to coordinate a care plan or discharge plan with other partner care providers, and prevent duplication of services. The care provider logs into the intelligent continuity of care information system 10 and accesses the information provided by the Referrals and Prior Encounters Widgets and learns that the patient has been actively receiving services from three other care providers. She also reads the Unresolved Orders/Labs and Abnormal Results Widgets and notices that he has several outstanding lab orders, several of which are follow-up labs to address previous abnormal findings. She contacts the previous care provider through the information exchange portal to confirm her findings. The previous care provider explains that the patient never attended the lab appointments, despite many attempts to contact the patient. Together, the former and current care providers develop a care plan to ensure that the patient attends his appointments and receives the proper care and treatment.
A patient that frequently uses clinical or social services may need additional attention, monitoring, or may have unidentified, unmet needs. The hospital care provider logs into the intelligent continuity of care information system 10 and accesses the information provided by the Predictive Analytics Widget, which indicates that the patient is at high risk of readmission. He reads about the patient's reliance on clinical and social support in the Prior Encounters Widget. He also reads medical information in the Medication Reconciliation, Referrals, and Most Prominent Problems Widgets. The care provider further uses this information to collaborate with a local social service center to develop a care plan for the patient. Using the information exchange portal and intelligent continuity of care information system 10, he also sets up alerts on the patient's record so that he receives a notification if the patient is readmitted to the hospital.
John arrives at the Parkland ER due to a severe asthma attack. This is his first encounter at a Parkland facility. The ER provider accesses the patient's prior records via the health information exchange, but finds a disorganized volume of 7 years of medical records from other facilities. However, he has very little time to process all of the information, He is searching for any allergies or possible factors that may have triggered John's asthma attack, but the information is buried in the medical history. When scanning the records, he also sees a prior stay in the Dallas County Jail, during which his request for a portable home nebulizer for breathing treatments was suspended and had not been resumed since his release from the jail. In this scenario, the intelligent continuity of care information system 10 would present a 1-page summary of the most relevant information over the 7 years to the ER provider at the point of care, including other medical conditions, current medications, allergies, and prior lab results, thus informing clinical decisions and efficient delivery of necessary treatment to the patient. The information in the intelligent continuity of care information system 10 also allows the care management team to help John resume his request for a nebulizer and to coordinate other follow up care with John's other care providers in the community.
Patient John Smith is preparing to be discharged from the hospital. His case manager helps him set up a profile in the intelligent continuity of care information system 10, so that he can access his health information and discharge summary via the Patient Summary Widget in the patient view after he has left the hospital. At home, John is able to track his self-management activities and his progress towards achieving health goals as jointly determined with his care providers. He can also receive reminders about his health events such as upcoming appointments, medications, and referrals as well as track these events using the Calendar Widget. He is able to access his translated clinical notes via the Processing and Translating Clinical Notes Widget and understand them due to the simplified language. He uses a step counter on his mobile phone, which integrates with the intelligent continuity of care information system 10 so that he can view and track his progress toward his next milestone exercise goal as defined in his care plan via the Integration with Patient Devices for Patient-Generated Data Widget. In preparation for his next appointment, John records the questions, concerns, and preferences that he wants to discuss with his care provider via the Interpreting Patient Questions and Concerns, and the Patient Care Preferences Widgets. John also completes health assessments using the Patient Assessments Widget that will help his care provider understand his medical history. Educational information provided by the Tailored Patient Education Modules Widget is made available to John. All of the information and functionalities help him better adhere to his health management activities and manage his chronic health conditions.
This view may further include the Most Prominent Problems Widget which provides a curated problem list that displays the most relevant medical conditions of the patient. The problem list is populated by analyzing and parsing structured and unstructured data fields in the EMR to identify the most prominent medical problems and present a curated list of conditions that are severe, chronic, or most relevant to the viewing provider. Further, additional widgets provide information such as action items that are extracted from unstructured physician notes and analyzed to facilitate care transitions. For patients with certain conditions, such as diabetes and/or hypertension, relevant information about medications, orders, and labs may be aggregated and prioritized according to the disease condition.
Some adverse events, such as diabetic complications or hospital readmissions, may be prevented if interventions are delivered in a timely manner. However, information necessary to detect and prevent an adverse event is usually not available with adequate lead time. By aggregating and analyzing available patient clinical and social factors, advanced algorithms can be used to calculate a patient's risk for adverse events and presented as predictive analysis to care providers to map availability of resources and services that facilitate delivery of targeted interventions to prevent the adverse event. In this example, clinical information can be aggregated and prioritized to diabetes and/or hypertension care.
The features of the present invention which are believed to be novel are set forth below with particularity in the appended claims. However, modifications, variations, and changes to the exemplary embodiments described above will be apparent to those skilled in the art, and the intelligent continuity of care information system and method described herein thus encompasses such modifications, variations, and changes and are not limited to the specific embodiments described herein.
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