The present invention generally relates to patient care plans. More specifically, the present invention relates to systems, methods, and apparatus for enhancing and improving transitions in patient care according to a patient care plan.
Today's healthcare involves electronic data and records management. Information systems in healthcare include, for example, healthcare information systems (HIS), radiology information systems (RIS), clinical information systems (CIS), and cardiovascular information systems (CVIS), and storage systems, such as picture archiving and communication systems (PACS), library information systems (LIS), and electronic medical records (EMR). Information stored may include patient medical histories, imaging data, test results, diagnosis information, management information, and/or scheduling information, for example. The content for a particular information system may be centrally stored or divided at a plurality of locations. Healthcare practitioners may desire to access patient information or other information at various points in a healthcare workflow. Availability of data also provides opportunities for healthcare analytics.
Nearly all Americans are cared for by business models that profit from patients' sickness rather than wellness. This has trapped care in high cost business models. Few patients are searching to “hire” healthcare providers that can do everything for everyone else. Generally, after diagnosis most patients want the medical problem fixed as effectively, affordable and conveniently as possible. Variation is a critical element in health care systems today. Quality problems are reflected in a wide variation in the use of health care services, underuse of some services, overuse of other services, and misuse of services, and an unacceptable level of errors.
In particular, professional uncertainty and scarce use of medical evidence seem to be the key elements in many problems dealing with healthcare variations. According to an investigation by Hearst Corporation, a staggering number of Americans will die (the estimated number was 200,000 in 2009) needlessly from preventable mistakes and infections every year. Even if it is difficult to establish a direct relationship between variations and errors, reducing variations by standardizing clinical processes is an effective tool to minimize the probability of medical errors. According to the Oxfords Journal, variation problems are especially critical today because the pressure to reduce healthcare costs without reducing quality in patient care has increased.
Certain examples provide systems, methods, and apparatus for patient care and care transition support.
An example system includes a processor and a memory to store and execute instructions to provide a strategy development and simulation tool to analyze a patient care plan and transitions of care within the care plan to develop and analyze a strategy for the transitions of care within the care plan. The example system provides a discharge planning tool including predictive analytics to provide scenario-based planning and visualization to develop the care plan for patient discharge. The example system provides visual analytics to track and display progress of the patient against the care plan for the patient. The example system provides an outcome tracker to measure care plan efficacy to provide feedback for the patient care plan and future care plans.
Certain examples provide a tangible computer-readable storage medium including a set of instructions to be executed by a processor, the instructions, when executed, implementing a system. The example system includes a strategy development and simulation tool to analyze a patient care plan and transitions of care within the care plan to develop and analyze a strategy for the transitions of care within the care plan. The example system includes a discharge planning tool including predictive analytics to provide scenario-based planning and visualization to develop the care plan for patient discharge. The example system includes visual analytics to track and display progress of the patient against the care plan for the patient. The example system includes an outcome tracker to measure care plan efficacy to provide feedback for the patient care plan and future care plans.
Certain examples provide a method including analyzing, using a processor, a patient care plan and transitions of care within the care plan to develop and analyze a strategy for the transitions of care within the care plan. The example method includes providing scenario-based planning and visualization using predictive analytics to develop the care plan for patient discharge. The example method includes tracking and displaying progress of the patient against the care plan for the patient using visual analytics. The example method includes measuring care plan efficacy using an outcome tracker to provide feedback for the patient care plan and future care plans.
The foregoing summary, as well as the following detailed description of certain embodiments of the present invention, will be better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, certain embodiments are shown in the drawings. It should be understood, however, that the present invention is not limited to the arrangements and instrumentality shown in the attached drawings.
Potentially preventable hospital readmissions are a $30 billion annual problem in the U.S. alone. Poorly executed care transitions in general lead to quality problems and in worst cases patient deaths. Hospitals with excessive 30-day readmissions will incur penalties against Medicare payments in 2013. Certain examples provide intelligent care transitions leveraging cloud computing, predictive analytics, data intensive computing (e.g., big data) and patient controlled social graphs. A composite solution differentiates by taking a closed-loop, system-wide, proactive and novel approach to creating intelligent care transitions and collaborations with retrospective and predictive analytics guiding every step along the way.
On a daily basis, patients with continuous, complex care needs make hundreds of thousands of transitions across different sites of care. Poorly executed transitions often result in potentially preventable hospital readmissions and in worst cases result in patient death. Re-hospitalizations cost the federal health insurance system billions of dollars, and certain example help to reduce hospital readmissions.
Factors contributing to the hospital readmissions include:
an inadequate relay of medical- and care-related information by hospital discharge planners to patients, caregivers, and/or post-acute care providers;
poor patient compliance;
inadequate follow-up care from post-acute and long-term care providers; insufficient use of supportive capacity of family caregivers;
deterioration of a patient's clinical condition; and
medical errors in a hospital that may occur during an initial admission and result in illness, injury, or harm to a patient.
Transitional care is more complex than simple exchange of information. Although it is important for clinicians and care providers to have access to the patient's medical record, the record is not useful unless users take the initiative to read the information in the record and act accordingly.
Many contemporary issues in healthcare such as hospital readmissions, chronic care and bundled episodes of care suffer from a number of breakdowns including ineffective protocols, poor collaboration, a lack of visibility into the care plan, patient progress and adherence as well as ineffective patient education and engagement, for example.
Preventing breakdowns before they occur often involves a variety of challenges. In certain examples, breakdowns in care transitions can be predicted before they occur to some level of accuracy. Hospital readmission risk prediction models can incorporate clinically actionable data, for example, that can be used to triage patients to different types of interventions.
The creation of clinical pathways has become a popular response to these concerns. Clinical pathways (also known as critical pathways, care maps, integrated care pathways, etc.) are integrated management plans that display goals for patients, and provide the sequence and timing of actions necessary to achieve such goals with optimal efficiency. Clinical pathways stress the improvement of clinical processes in order to improve clinical effectiveness and efficiency. A clinical pathway is a multidisciplinary management tool based on evidence-based practice for a specific group of patients with a predictable clinical course, in which the different tasks (e.g., interventions) by professionals involved in patient care are defined, improved/optimized and sequenced by hour (e.g., for emergency department (ED)), day (e.g., acute care) or visit (e.g., homecare). Outcomes are tied to specific interventions, for example.
One or more indicators can be analyzed to determine that it may be useful to commit resources to establish and implement a clinical pathway for a particular condition. Example indicators can include prevalent pathology within the care setting, pathology with a significant risk for patients, pathology with a high cost for the hospital, predictable clinical course, pathology well defined and that permits a homogeneous care, existence of recommendations of good practices or experts opinions, unexplained variability of care, possibility of obtaining professional agreement, multidisciplinary implementation, motivation by professionals to work on a specific condition, etc.
Thus, clinical paths are clinical management tools used by health care workers to define the best process in their organization, using the best procedures and timing, to treat patients with specific diagnoses or conditions according to evidence-based medicine (EBM). As a consequence, the introduction of clinical pathways could be an effective strategy for health care organizations to reduce or at least to control their processes and clinical performance variations.
However, there are a number of challenges with implementing standardized clinical pathways in healthcare organizations. Building and developing clinical pathways may require business re-engineering techniques, involvement of multidisciplinary teams, pre and post analysis models to evaluate the effect of applying standardized pathways to process and outcome indicators.
To help ensure implementation success, patient satisfaction must also be measured along with adoption obstacles faced by care providers. In the past, finding the proper balance between clinician autonomy and standardization has proven difficult. Many doctors still consider clinical pathways as “cookbook medicine”, even though they could change the pathway for a patient at any time. Critics of clinical pathways argue that by discouraging idiosyncrasies in clinical methods, standards introduce disincentives for individual innovations in care and healthy competition among practitioners. Instead of revolutionizing care, evidence based medicine therefore threatens to bring about stagnation and bland uniformity, derogatorily characterized as “cookbook medicine.”
Furthermore, if clinicians are not involved in the definition and continuous improvement of clinical guidelines, there is a real danger that the clinical pathways could be considered an administrative attempt to reduce costs, and therefore it would most likely fail. The implementation tasks may seem daunting at first without expert assistance.
Although the following discloses example methods, systems, articles of manufacture, and apparatus including, among other components, software executed on hardware, it should be noted that such methods and apparatus are merely illustrative and should not be considered as limiting. For example, it is contemplated that any or all of these hardware and software components could be embodied exclusively in hardware, exclusively in software, exclusively in firmware, or in any combination of hardware, software, and/or firmware. Accordingly, while the following describes example methods, systems, articles of manufacture, and apparatus, the examples provided are not the only way to implement such methods, systems, articles of manufacture, and apparatus.
When any of the appended claims are read to cover a purely software and/or firmware implementation, in an embodiment, at least one of the elements is hereby expressly defined to include a tangible medium such as a memory, DVD, CD, Blu-ray, etc., storing the software and/or firmware.
Certain examples connect consumers (e.g., patients) to advancements in healthcare, such as in molecular medicine and clinical research relevant to their predisposed diseases (e.g., genetically, hereditarily, environmentally, etc., pre-disposed or inclined to suffer from). Furthermore, certain examples provide systems, apparatus, and methods including guidance for a user to seek professional intervention. Certain examples provide a knowledge exchange clearinghouse.
In certain examples, models that are used for risk standardization and readmission risk models intended for clinical use can provide data prior to discharge, discriminate high- from low-risk patients, and can be adapted to the settings and populations in which they are to be used. For example, marginally housed patients or those struggling with substance abuse might require unique discharge services. In certain examples, there is not a one-size fits all intervention strategy.
Certain examples provide better care plans and flexible intervention strategies. Root causes of breakdowns in care transitions vary greatly across the patient population. Patient adherence can be a factor. Examples of barriers to patient adherence are:
1.) Logistical barriers—cost, adverse effects, poor access to medicine, etc.
2.) Perceptual barriers—poor understanding of therapy, lack of belief in therapy, unable to see benefits of drug therapy, etc.
3.) Physical and mental barriers—cognitive/memory deficits, visual deficits, mental deficits, physical deficits
4.) Social barriers—poor communication with healthcare providers, patient dissatisfaction with care, language deficits, poor literacy, cultural/religious beliefs, lack of social/family support, disruption of daily routine and attitude
In certain examples, overcoming these barriers involves individualized care transition strategies and patient specific care plans. However, creating such plans can overburden hospital discharge planners. Medicare regulations in the United States require participating hospitals to have a discharge planning process that applies to all patients. Hospital discharge planning can include instructions hospitals provide to patients, caregivers, outpatient physicians, and other post-acute providers. Discharge planning can also include counselling for patients and caregivers to ensure the smooth and timely transition of a patient from the inpatient setting to a home, post-acute care setting or long-term care setting. Despite these requirements, discharge planning is often incomplete and necessary information is not provided by hospitals to physicians and post-acute providers in a timely manner. For example, timely and comprehensive delivery of discharge information from hospitals to post-acute and long-term-care providers can be a first step in breaking the cycle of unnecessary readmissions. Encouraging better collaboration among providers and enhancing accountability for patient outcomes and treatment costs can be another step.
Caregivers (e.g., family and friends who give care without compensation) play a significant role in the hospital discharge of Medicare beneficiaries. Caregivers help patients comply with their care plans, including taking and accompanying patients to follow-up physician visits and diagnostic test appointments, as well as reminding patients to take their prescribed medications and understanding or interpreting worsening medical symptoms. Training of caregivers enhances the quality of the assistance that they provide to patients thus could help reduce readmissions. Training, coaching, counselling, and education can be provided to caregivers throughout the discharge process (e.g., by hospital discharge planners by transitional care teams, etc.).
Certain examples provide “Intelligent Care Transitions” (ICT) using a system-wide approach to solving problems in care transitions and hospital readmissions. The ICT solution leverages emerging technologies including cloud computing, predictive analytics, “data intensive computing” (e.g., Big Data), social graphs, etc., and includes a set of novel components orchestrated in a closed loop system that operates in a self-reinforcing virtuous cycle.
The Care Transition Strategy Development and Simulation Tool 1 facilitates patient stratification, testing of what-if scenarios, development and simulation of care transition strategies, etc. Example strategies are (but are not limited to) specific care transition interventions, family care giver education, home based primary care, home telehealth, programs for stimulating patient compliance, etc. Examples of the care transition strategy development and simulation tool 1 are illustrated in
For example, as shown in
The example strategy and simulation tool interface 400 provided in
The Scenario and Discharge Planning Tool(s) 2 are driven by predictive analytics are used to inform discharge planning with a number of variables such as diagnosis, demographics and socio-economic variables. A scenario-based planning and visualization technique (e.g., best case, worst case and likely scenarios) assists in developing effective and adaptive care plans. Examples of the scenario and discharge planning tool(s) 2 are illustrated in
For example, as shown in
As shown the example of
The example of
On-Demand Dynamic Care Circles 3 connects care providers and patients in a manner to help enable superior care transitions and collaboration. As shown in the example of
Scenario-based visual analytics 4 shows progress against a care plan, for example. Adherence is visualized with updated scenario-based visualization that clearly shows when care is on course or off course, for example.
As shown in the example scenario visualizer 1000 of
In certain examples, an outcome tracker measures care plan efficacy for application to similar scenarios and patients to continually reinforce effective plans and interventions as well as to incorporate deviations from plan with positive outcomes, for example.
The following is a detailed scenario presented for purposes of illustration only. This scenario involving care of a cancer patient is but one of many examples that will be clear to one of ordinary skill in the art after reading and understanding the description above. However, for brevity, the following example is provided.
Example Scenario—Cancer Patient Care
The example scenario is for a cancer patient who lives in a rural area of the country. The patient is a 29 year old male and father of two young sons who has just been diagnosed with Acute Lymphocytic Leukemia (ALL). To begin his treatment, he must travel one hour by flight to the nearest cancer hospital. Prior to his arrival at the hospital, a Chief Quality Officer at the hospital has just completed some patient stratification and analysis of Leukemia patients. Using the stratification tool 300 (see
Armed with this knowledge, the Chief Quality Officer can design an intervention strategy to improve the situation for rural cancer patients (see
When the ALS cancer patient is admitted at the hospital to begin a first phase of the treatments he brings a mobile computer to keep in touch with his family and friends using, for example, Facebook™ and Skype™. He is pleased to discover the “On-Demand Care Circles” social application 800 (see
As he completes the first phase of the treatment, a discharge nurse walks the patient through the discharge planning process. The predictive analytics built into the discharge planning tool 500 (see
After coming home, the patient logs into the “Care Circles” application 800 and is pleased to discover that the application is aware of his discharge status, and the care circles have automatically expanded to include his home care team and the primary doctor. He grants the home care team members access to his electronic medical record and activates the collaboration features. A message from his primary doctor welcomes him home and suggests some dates for him to come in for a visit. He accepts the appointment request. When arriving for his first appointment, the doctor tells him that he talked with the discharge nurse to clarify the care plan (see
A few months later after completing phase II of the treatment back at the cancer hospital, the patient and the discharge nurse review his progress using the “scenario visualizer” 1000 (see
After being admitted at the second (e.g., regional) cancer hospital, the patient logs into the “Care Circles” application 800 and is again pleased to discover the new faces that had been added to his cancer team (see
Thus, certain examples can help navigate through a plurality of care transitions including: 1) prior to arrival for treatment, 2) enhanced discharge planning process, 3) primary care provider follow-up, 4) transfer to specialty hospital, 5) discharge from specialty procedure, 6) post-discharge follow-up and intervention, etc.
The above scenario is a complex case. There are numerous other scenarios which can be described and where the systems and associated methods described above and illustrated in the drawings play an important role in improving the patient outcome.
System Architecture
An example system architecture and components 1200 to implement, provide, and support the tools, interfaces, methods, and other solutions described above is shown in
The example system 1200 includes one or more connections to and/or indications of care participants 1201 (e.g., hospice nurse, specialist, patient access, caregiver, psychiatrist, primary care provider, home health care provider, nurse, doctor, nutritional consultation, long-term care provider, etc.). Care participant(s) 1201 (e.g., the patient and/or care provider) can access functionality provided by the system 1200 via a software-as-a-service (SaaS) implementation over a cloud or other computer network, for example. While SaaS is described herein for purposes of illustration, all or part of the system 1200 can also be provided via platform as a service (PaaS), infrastructure as a service (IaaS), etc. Other personnel, such as a care quality analyst 1202, discharge planner 1203, etc., can be connected to and/or otherwise utilize the system 1200 as well, for example.
One or more intelligent care transitions applications 1210 can be provided as part of the system 1200 via SaaS, for example. Additionally provider analysis applications 1220 such as hospital applications, accountable care organization (ACO) applications, integrated delivery network (IDN) applications, etc., can be provided as part of the system 1200 via SaaS, for example. Intelligent care transition applications/components 1210 can include one or more of care plan progress, care journal, alerts/reminders, pluggable applications, patient health information/monitor, social collaboration, scenario visualizer, social economics, on-demand social graph (e.g., care circle), etc., as described above, for example. Other care provider applications/components 1220 can include one or more of patient stratification, strategy development, simulation, patient discharge planner, scenario analysis, etc., as described above, for example.
Applications 1210, 1220 and/or other functionality can leverage a supporting infrastructure 1230. The supporting infrastructure 1230 includes one or more of retrospective analytics, a portal container, a workflow engine, data ingestions, one or more data and/or plan models, visual analytics, provider registry, rules engine, security controls, predictive analytics engine, outcome monitor, news feed, task engine, etc.
Retrospective analytics provides a historic cohort study and a type of medical research to look back at events that have already happened, for example. The predictive analytics engine provides machine learning to analyze current and historical events to predict future events such as potentially preventable hospital readmissions, for example. The portal container provides a Web portal to support pluggable user interface components, for example. Visual analytics tools provide analytical reasoning facilitated by interactive visual interfaces, for example. Visual analytics tools can be used to identify alternative futures and their warning signs, for example. The outcome monitor provides a surveillance engine to track a patient's treatment progress and deviations against a care plan, for example. Secure messages provides a secure collaboration infrastructure to support peer consults, patient collaboration, telemedicine and second opinions, for example. The provider registry includes a registry of healthcare providers that can be added to the patient's care circle, for example. The news feed engine delivers a social news feed into the care circle application, for example. The workflow engine provides a process orchestration engine to automate care transition processes and care plan activities, for example. The rules engine executes business rules in a runtime production environment, for example. For example, the rules engine executes business rules when interventions are mandated. The task engine manages human workflows, tasks, and escalations, for example.
The supporting infrastructure 120 includes integration with one or more partners, standards, etc. 1240, such as one or more integrated healthcare organizations (IHOs), IDNs, ACOs, health information exchanges (HIEs), personal health records (PHRs), electronic medical records (EMRs), social networks (e.g., Facebook™, twitter™ YouTube™, flickr™, digg™, Technorati™, LinkedIn™, del.icio.us™, myspace™, RSS, etc. Integration can be facilitated via one or more of Integrating the Healthcare Enterprise (IHE) profile including Exchange of Personal Health Record Content (XPHR), Cross-enterprise Document Sharing (XDS), Cross-enterprise Document Workflow (XDW), Patient Identifier Cross Referencing (PIX), Document Metadata Subscription (DSUB), etc.
The supporting infrastructure 1230 shown in the example of
Reimbursement trends such as the Medicare 30-day excessive readmission penalty, value-based purchasing rewards to high quality providers and other payment innovations alter traditional business models and place a premium on cost effectiveness and care coordination. Healthcare is experiencing a marked shift from a fee for service model that incents individual episodes of care to an integrated model with various forms of payment bundling all the way up to capitation. A shift can be made towards a model that incents and provides the capability to coordinate care and care transitions. Certain examples help healthcare organizations to measure the effectiveness of interventions and understand expected outcomes with an eye towards modeling financial risk. There are many economic buyers participating in this shift including Accountable Care Organizations, Integrated Healthcare Organizations, Integrated Delivery Networks, Group Purchasing Organizations, Payers, Self-Insured Employers, and Governments.
Thus, certain examples provide a system-wide approach to the problem, focuses on the clinical workflows and the root causes of care transition failures. Certain examples empower patients, caregivers, care providers and envisions integrated care models where the providers are encouraged to collaborate to enhance accountability for patient outcomes and treatment costs.
Certain examples allow for a variety of prediction models whose effectiveness is continuously evaluated through a closed loop feedback model. Certain examples provide predictive analytics throughout care transitions in a care plan or pathway. Certain examples provide an integrated scenario analysis tool including discharge planning Certain examples provide an “on-demand” social graph (care circles) adapted and optimized for care plan collaboration. Certain examples provide use of “behavioral economics” concepts to influence care providers and nudge a patient into adherence. Certain examples utilize integrated patient stratification, strategy planning and simulation. Certain examples provide visual analytics, such as a “scenario visualizer”.
In certain examples, in place of or in addition to a cloud or network-based solution, “smart cards” can be used to allow patients to carry data from provider to provider. Password-protected, web-based medical records can be provided to make the information available on a need-to-know basis. Patients can be equipped with hand-held personal data assistants, smart phones, etc., to convey information across care settings.
A flowchart representative of example machine readable instructions for implementing the example systems and methods described herein is shown in
As mentioned above, the example processes of
At 1350, the actor(s) 1325 and/or 1335 may provide care circle intelligence to a patient outcome tracker 1345, which can provide decision support based on the data provided and/or other retrieved data compared to care plan, threshold, comparison data, etc. At 1360, the patient outcome tracker triggers one or more alerts to actor(s) 1325 and/or 1335. At 1370, the patient outcome tracker escalates and/or confirms an action, care plan, etc., with the actor 1325.
The patient outcome tracker 1345 can also provide predictive and visual analytics 1355. At 1380, the analytics 1355 can help provide foresight and increased peripheral vision to actor(s) 1325 and/or 1335. At 1390, the analytics 1355 stimulate adherence to a care plan by the patient 1305.
The processor platform 1400 of the instant example includes a processor 1412. For example, the processor 1412 can be implemented by one or more microprocessors or controllers from any desired family or manufacturer. The processor 1412 includes a local memory 1413 (e.g., a cache) and is in communication with a main memory including a volatile memory 1414 and a non-volatile memory 1416 via a bus 1418. The volatile memory 1414 may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory 1416 may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory 1414, 1416 is controlled by a memory controller.
The processor platform 1400 also includes an interface circuit 1420. The interface circuit 1420 may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface.
One or more input devices 1422 are connected to the interface circuit 1420. The input device(s) 1422 permit a user to enter data and commands into the processor 1412. The input device(s) can be implemented by, for example, a keyboard, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system.
One or more output devices 1424 are also connected to the interface circuit 1420. The output devices 1424 can be implemented, for example, by display devices (e.g., a liquid crystal display, a cathode ray tube display (CRT), etc.). The interface circuit 1420, thus, typically includes a graphics driver card.
The interface circuit 1420 also includes a communication device such as a modem or network interface card to facilitate exchange of data with external computers via a network 1426 (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.).
The processor platform 1400 also includes one or more mass storage devices 1428 for storing software and data. Examples of such mass storage devices 1428 include floppy disk drives, hard drive disks, compact disk drives and digital versatile disk (DVD) drives. The mass storage device 1428 may implement a local storage device.
The coded instructions 1432 may be stored in the mass storage device 1428, in the volatile memory 1414, in the non-volatile memory 1416, and/or on a removable storage medium such as a CD or DVD.
Although certain example methods, systems, apparatus, and articles of manufacture have been described herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, systems and articles of manufacture fairly falling within the scope of the claims of this patent.