Description:Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, known details are not described in order to avoid obscuring the description.
References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and such references mean at least one of the embodiments.
Reference to "one embodiment", "an embodiment", “one aspect”, “some aspects”, “an aspect” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others.
The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided.
A recital of one or more synonyms does not exclude the use of other synonyms.
The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only and is not intended to further limit the scope and meaning of the disclosure or of any example term. Likewise, the disclosure is not limited to various embodiments given in this specification. Without intent to limit the scope of the disclosure, examples of instruments, apparatus, methods, and their related results according to the embodiments of the present disclosure are given below. Note that titles or subtitles may be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, technical and scientific terms used herein have the meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions will control.
Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims or can be learned by the practice of the principles set forth herein.
As mentioned above, there is a need for a comprehensive, intelligent healthcare management platform that can streamline care delivery, personalize intervention plans, and facilitate real-time engagement between healthcare providers and patients across the continuum of care.
The present disclosure therefore provides a healthcare management system for proactive monitoring and personalized care coordination.
Referring to Figure 1, a healthcare management system (100) is illustrated in accordance with an embodiment of the present disclosure. The system (100) is modular in architecture and integrates various functional components including a data ingestion module (10), a natural language processing (NLP) module (20), a care plan generation module (30), a care program execution module (40), a communication module (50), a touchpoint management module (60), a real-time patient journey visualizer (70), and a user interface module (80), all operatively connected to perform synchronized, intelligent healthcare management operations. Each of these modules is operatively coupled through a central control logic or data orchestration framework to facilitate seamless information flow and coordinated care delivery.
In accordance with aspects of the present disclosure, the data ingestion module (10) may be configured to collect diverse patient information from various sources including hospital information systems (HIS), electronic medical records (EMR), lab diagnostic systems, insurer databases, and patient-reported platforms. The module (10) may support multiple ingestion mechanisms such as application programming interfaces (APIs), secure file transfers (SFTP), bulk CSV uploads, and manual entries through healthcare worker terminals. The received data includes patient demographics (age, gender, location), clinical conditions (diagnoses, comorbidities), laboratory test results (e.g., blood sugar, lipid profile, renal functions), discharge summaries, medication history, and lifestyle inputs (smoking status, physical activity, sleep patterns). The data ingestion module (10) ensures standardization and normalization of input fields using predefined data models and validation schemas, enabling downstream processing consistency.
In accordance with aspects of the present disclosure, the natural language processing module (20) may be connected to the data ingestion module (10) and is specifically designed to process unstructured medical documents such as physician notes, clinical narratives, discharge summaries, diagnostic imaging reports, and treatment summaries. The NLP module (20) may use rule-based parsers, machine learning models, and medical ontologies (e.g., SNOMED CT, ICD-10, UMLS) to extract structured information such as medical conditions, test values, symptom durations, drug names, dosage frequencies, and clinical impressions. For example, a discharge summary stating "Patient presented with dyspnea and was treated with nebulization and steroids" would be parsed to identify symptoms, interventions, and prescribed medications.
In some aspects of the present disclosure, the NLP module (20) is multilingual and supports regional language processing to accommodate patients across diverse geographies. The output of the NLP module (20) is passed along to the care plan generation module (30) as structured, standardized data for further decision support.
In accordance with aspects of the present disclosure, the care plan generation module (30) may receive the enriched and structured patient data from data ingestion module (10) and NLP module (20), and dynamically generates a personalized care plan tailored to the patient’s medical needs, social circumstances, and risk profile. In an embodiment, risk categories may be inferred from historical care utilization patterns, clinical guidelines, and algorithmic assessments. The care plan may include schedules for follow-up appointments, lab testing intervals, medication compliance checks, specialist referrals, lifestyle coaching, and escalation criteria. The care plan generation module (30) may use configurable rule engines, clinical pathways, and optionally machine learning models to decide the frequency, modality, and content of care interventions. The care plan is adaptive, recalibrated with each new data input—such as an updated lab value, a missed touchpoint, or a newly reported symptom. For instance, if a patient reports fatigue and shows abnormal creatinine levels, the care plan may insert a nephrology consult and advance lab retesting timelines.
In accordance with aspects of the present disclosure, the care program execution module (40) implements the care plan and is comprised of at least three key sub-modules: a chronic care management sub-module (41), a post-discharge care sub-module (42), and an infectious disease control sub-module (43).
The chronic care management sub-module (41), shown in Figure 3, may manage long-term engagement with patients diagnosed with non-communicable conditions such as diabetes, hypertension, asthma, chronic kidney disease, or hypothyroidism. It schedules digital surveys, symptom trackers, lifestyle guidance modules, and health coach calls at regular intervals. This chronic care management sub-module (41) may include condition-specific templates and customizable care paths that adapt to clinical stage, lab markers, and patient-reported behaviors.
The post-discharge care sub-module (42), shown in Figure 4, may address patients discharged after hospitalization or emergency room visits. It monitors them during the high-risk recovery window of 30 to 90 days. The post-discharge care sub-module (42) may push daily symptom check-ins, flags missed responses, schedules follow-up visits, and facilitates prescription refills. A rule-based engine embedded in sub-module (42) escalates any critical data point—such as shortness of breath, elevated BP, or missed follow-ups—to a human care coordinator. In an embodiment, the post-discharge care sub-module (42) may also coordinate with home visit services, teleconsultation portals, or integrated remote patient monitoring (RPM) devices for real-time vital sign tracking.
The infectious disease control sub-module (43), as shown in Figure 5, may be designed to identify members at risk due to geographic or seasonal exposure to communicable diseases such as influenza, COVID-19, dengue, or tuberculosis. A geolocation risk analyzer (44) may be embedded in the infectious disease control sub-module (43) and maps patient residence data to disease prevalence heatmaps, micro-cluster alerts, and epidemiological forecasts. Based on the identified risk, this infectious disease control sub-module (43) adjusts outreach cadence, sends precautionary messages, activates symptom monitoring surveys, and guides patients to testing or vaccination centers. It may also generate regional intervention reports for insurer or government use.
In accordance with aspects of the present disclosure, the communication module (50) may interface with the care execution module (40) and handles all patient interactions via digital channels. It may include a messaging interface (51) for sending WhatsApp prompts, chatbot messages, or SMS updates; a voice calling module (52) for outbound IVR calls or health coaching conversations; and a notification module (53) for delivering push alerts on mobile apps or wearable devices. This module (50) is policy-driven, meaning it chooses the appropriate medium and script based on patient profile, channel availability, and urgency level. For example, an elderly patient may be called for a medication reminder, while a younger patient receives a WhatsApp message for a lab follow-up.
In accordance with aspects of the present disclosure, the touchpoint management module (60) orchestrates the sequence of healthcare interactions across all sub-modules. It maintains a real-time care calendar for each patient, schedules appointments and alerts, logs response status, and triggers next-best actions based on interaction outcomes. This touchpoint management module (60) also records timestamps, channel used, success/failure outcomes, and user remarks. It may also provide audit trails and compliance reports for operational analytics.
In accordance with aspects of the present disclosure, the patient journey visualizer (70) is a data visualization engine that may track the patient's trajectory over time. It presents data on the patient’s care activities, lab trends, medication adherence, symptom reports, risk fluctuations, and escalations in the form of graphs, timelines, calendars, and traffic-light indicators. Stakeholders can filter data by episode, timeframe, condition, or care type. The visualizer (70) may include interactive elements to allow drill-down into raw data, document views, or care coordinator notes.
In accordance with aspects of the present disclosure, the user interface module (80) connects all key modules and presents the information in a role-specific format. Physicians may receive clinical dashboards highlighting critical alerts, pending diagnostics, and unresolved symptoms. Care coordinators may view upcoming calls, overdue tasks, and escalation queues. Insurers may review population risk summaries, cost forecasts, and intervention outcomes. The interface (80) supports access control, audit logs, and data export features for external reporting.
Figure 2 illustrates a detailed architectural block diagram of the healthcare management system (100), which comprises a collection of interconnected, function-specific modules that collaboratively enable proactive patient monitoring, dynamic care plan generation, and personalized health service delivery. At the core of the system (100) is a data orchestration layer that ensures the seamless exchange of information among all components, facilitates real-time processing, and enforces data governance rules for accuracy, security, and compliance.
In operation, the healthcare management system (100) functions by first acquiring patient data through the data ingestion module (10), which consolidates information from various sources such as EMRs, lab systems, insurer platforms, and manual inputs. This data, both structured and unstructured, is then processed by the natural language processing (NLP) module (20) to extract one or more relevant clinical parameters from documents such as discharge summaries, physician notes, and diagnostic reports. The structured data is fed into the care plan generation module (30), which dynamically creates a personalized care plan tailored to the patient’s medical profile, lifestyle inputs, and social context. Based on the care plan, the patient is enrolled into one or more care programs—chronic care via sub-module (41), post-discharge tracking via sub-module (42), or infectious disease control via sub-module (43)—within the care program execution module (40). The communication module (50) then delivers scheduled interactions and alerts through various channels including WhatsApp (51), voice calls (52), and mobile notifications (53), while the touchpoint management module (60) ensures each interaction is scheduled, executed, and monitored for compliance. All patient activities, health trends, and engagement metrics are visualized in real time through the patient journey visualizer (70), and stakeholders such as physicians, care coordinators, and insurers access personalized dashboards via the user interface module (80) to take timely, informed actions. The system (100) operates in a continuous feedback loop, dynamically adjusting the care plan and outreach based on new inputs such as lab results, patient-reported symptoms, or missed engagements, thereby enabling proactive, data-driven healthcare management.
Various exemplary embodiments of the healthcare management system (100) are described hereinafter to illustrate the process, working, and use-case adaptations of the disclosure, which may be employed either individually or in combination, without deviating from the scope of the present disclosure.
In an exemplary embodiment, a 58-year-old male patient with known diabetes and hypertension is enrolled into the system (100) by his healthcare provider following a recent hospital discharge. His demographic data, clinical conditions, and discharge summary are ingested through the data ingestion module (10) via a hospital's EMR interface. His discharge note, containing unstructured physician comments, is parsed by the natural language processing module (20), which extracts key terms such as “suboptimal glycemic control,” “prescribed metformin,” and “requires follow-up for creatinine monitoring”. This structured information is then passed to the care plan generation module (30), which creates a personalized care pathway including weekly digital check-ins, monthly HbA1c testing reminders, medication adherence prompts, and a scheduled teleconsultation. The post-discharge care sub-module (42) within the care program execution module (40) initiates daily symptom check-ins for 30 days, while the chronic care management sub-module (41) concurrently schedules monthly lifestyle coaching sessions. The communication module (50) delivers these touchpoints via the messaging interface (51) on WhatsApp and automated phone calls through the voice calling module (52). Each interaction is recorded and tracked by the touchpoint management module (60), and the patient's recovery progress, compliance metrics, and any alerts are visualized on the patient journey visualizer (70) for review by his assigned care coordinator through the user interface module (80). If the patient fails to respond to two consecutive check-ins or reports symptoms such as fatigue and dizziness, the system (100) escalates the case to a human care coach for immediate follow-up.
In another exemplary embodiment, the system (100) is deployed at a public health agency during a dengue outbreak. The infectious disease control sub-module (43) utilizes the geolocation risk analyzer (44) to identify patients residing in high-incidence zones based on government-reported case density. The data ingestion module (10) imports regional patient addresses from insurer databases, while the care plan generation module (30) automatically generates a risk-adjusted care protocol involving educational messaging, symptom surveys, and calls to action for preventive measures like mosquito control. The communication module (50) dispatches multilingual awareness messages through the messaging interface (51) and push notifications via the notification module (53). Symptom surveys triggered through this program are monitored by the touchpoint management module (60), and any patients reporting fever or body aches are flagged on the visualizer (70). Care managers access these alerts in real-time via the user interface module (80) and initiate escalation protocols such as home visits or telehealth assessments to prevent disease progression.
In another exemplary embodiment, a corporate wellness program integrates the system (100) to manage employees with sedentary lifestyles and early signs of metabolic syndrome. The data ingestion module (10) receives health check-up data and lifestyle surveys submitted during annual screenings. The NLP module (20) extracts risk indicators from wellness coach notes and blood test summaries. The care plan generation module (30) designs a low-intensity, long-term digital engagement plan that includes daily physical activity reminders, bi-weekly nutrition tips, and quarterly virtual consultations. The chronic care management sub-module (41) coordinates these interactions, while the communication module (50) delivers content through mobile app notifications (53) and WhatsApp messages (51). The touchpoint management module (60) adapts scheduling based on user engagement patterns, and the patient journey visualizer (70) tracks improvement in behavior adherence over time. Human wellness coaches use the user interface module (80) to adjust plans or provide direct motivational support when engagement drops.
In another exemplary embodiment, a multi-specialty hospital implements the system (100) across its cardiology department to reduce 90-day readmissions for congestive heart failure (CHF). The data ingestion module (10) ingests discharge summaries and in-patient monitoring reports. The NLP module (20) extracts structured values such as “ejection fraction 35%” and “prescribed beta-blocker.” The care plan generation module (30) creates an intensive post-discharge program using the post-discharge care sub-module (42), which includes daily fluid intake logs, symptom surveys for edema or breathlessness, and weekly tele-consults. Voice calls made through the voice calling module (52) ensure elderly patients are engaged. Missed touchpoints or abnormal survey responses are captured by the touchpoint management module (60), and escalated to a CHF nurse via the user interface module (80). The patient journey visualizer (70) provides the cardiology team with risk trend graphs, enabling data-driven adjustments to follow-up intensity.
These exemplary embodiments demonstrate that the system (100) is highly adaptable across use cases, healthcare settings, and patient populations. The modular architecture allows selective activation of specific sub-modules such as chronic care (41), post-discharge (42), or infectious disease response (43), and supports a wide range of communication and monitoring strategies through modules (50), (60), and (70). All modules are interoperable and can be configured or scaled depending on the clinical context, user role, or operational infrastructure, ensuring broad industrial applicability and customization for diverse real-world scenarios.
Figure 6 depicts a method (200) for proactive monitoring and personalized care coordination using a healthcare management system (100). The method (200) includes the steps of:
At step (201): acquiring one or more patient data via a data ingestion module (10);
At step (202): extracting structured medical parameters from unstructured records using a natural language processing (NLP) module (20);
At step (203): generating a customized care schedule using a care plan generation module (30);
At step (204): assigning the patient to one or more care programs selected from chronic care, post-discharge care, or infectious disease management using a care program execution module (40);
At step (205): executing care interactions through a communication engine (50) according to the care schedule;
At step (206): tracking and updating touchpoints using a touchpoint management module (60); and
At step (207): displaying care plan status, alerts, and patient summaries to relevant stakeholders via a role-based user interface module (80).
In some aspects of the present disclosure, the method (200) may further comprise the step of identifying geographic disease risk zones and adapting communication intensity accordingly through the infectious disease control sub-module (43).
In some aspects of the present disclosure, the method (200) may further comprise the step of escalating non-responsive or deteriorating patients to human care coordinators for direct clinical intervention.
In some aspects of the present disclosure, the method (200) may further comprise the step of dynamically adjusting the care schedule in response to new lab values, physician feedback, or patient-reported outcomes.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the healthcare management system (100) and its method (200) described above are merely exemplary embodiments or examples and that the scope of the present disclosure is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.
The implementation set forth in the foregoing description does not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the subject matter described. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and/or variations can be provided in addition to those set forth herein. For example, the implementation described can be directed to various combinations and sub combinations of the disclosed features and/or combinations and sub combinations of the several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and/or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Other implementations may be within the scope of the following claims
, Claims:1. A healthcare management system (100) for proactive monitoring and personalized care coordination, the system comprising:
a data ingestion module (10) configured to receive one or more patient data from at least one source including demographics, clinical conditions, laboratory test results, discharge summaries, and lifestyle information via at least one of an application program interface (API), file upload, or data-entry portal;
a natural language processing (NLP) module (20) configured to extract at least one structured clinical data from unstructured documents including discharge summaries, physician notes, and diagnostic reports;
a care plan generation module (30) configured to dynamically generate a personalized care plan for each patient based on a risk category, patient’s medical history, lab values, lifestyle inputs, and social determinants of health;
a care program execution module (40) comprising:
a chronic care management sub-module (41) configured to manage patients with long-term conditions including diabetes, hypertension, and respiratory disorders through scheduled digital and telephonic touchpoints;
a post-discharge care sub-module (42) configured to track recently discharged patients for a predefined follow-up period and detect signs of adverse health events; and
an infectious disease control sub-module (43) configured to identify patients in high-risk geographic zones and trigger health guidance and symptom monitoring workflows,
a communication module (50) configured to deliver care interactions via instant messaging, automated phone calls, push notifications, emails, and SMS, in accordance with the generated care plan;
a touchpoint management module (60) configured to schedule, trigger, and track a sequence of healthcare interactions based on the personalized care plan;
a real-time patient journey visualizer (70) configured to render interactive timelines, health trends, risk level transitions, intervention history, and care adherence indicators; and
a user interface module (80) operatively coupled to the care plan generation module (30), the communication module (50), and the visualizer (70), configured to display patient data, alerts, compliance summaries, and recommended next actions to physicians, care managers, and insurers.
2. The system (100) as claimed in claim 1, wherein the NLP module (20) is trained to extract clinical metrics including lab values, treatment descriptions, medical conditions, and prescription compliance indicators from unstructured clinical documents.
3. The system (100) as claimed in claim 1, wherein the care plan generation module (30) adapts the patient’s care journey dynamically based on updated data from lab results, medication adherence records, or missed interactions.
4. The system (100) as claimed in claim 1, wherein the communication module (50) comprises:
a messaging interface (51) configured to send personalized health prompts and check-ins via WhatsApp or other messaging platforms;
a voice calling module (52) configured for automated telephonic health surveys and reminders; and
a notification module (53) configured to send patient alerts and care plan updates on mobile applications.
5. The system (100) as claimed in claim 1, wherein the post-discharge care sub-module (42) is configured to:
send daily symptom check-ins to patients for a predefined number of days; and
automatically escalate cases to care coordinators when critical symptoms or missed responses are detected.
6. The system (100) as claimed in claim 1, wherein the infectious disease control sub-module (43) comprises a geolocation risk analyzer (44) configured to identify patients residing in high-incidence areas based on seasonal or regional disease data.
7. The system (100) as claimed in claim 1, wherein the visualizer (70) displays patient health progression over time, scheduled interventions, compliance statistics, and unresolved alerts using color-coded visual indicators.
8. A method (200) for proactive monitoring and personalized care coordination using a healthcare management system (100), said method (200) comprising steps of:
acquiring one or more patient data via a data ingestion module (10);
extracting structured medical parameters from unstructured records using a natural language processing (NLP) module (20);
generating a customized care schedule using a care plan generation module (30);
assigning the patient to one or more care programs selected from chronic care, post-discharge care, or infectious disease management using a care program execution module (40);
executing care interactions through a communication engine (50) according to the care schedule;
tracking and updating touchpoints using a touchpoint management module (60); and
displaying care plan status, alerts, and patient summaries to relevant stakeholders via a role-based user interface module (80).
9. The method (200) as claimed in claim 8, wherein the method (200) further comprising the step of identifying geographic disease risk zones and adapting communication intensity accordingly through the infectious disease control sub-module (43).
10. The method (200) as claimed in claim 8, wherein the method (200) further comprising the step of escalating non-responsive or deteriorating patients to human care coordinators for direct clinical intervention.
11. The method (200) as claimed in claim 8, wherein the method (200) further comprising the step of dynamically adjusting the care schedule in response to new lab values, physician feedback, or patient-reported outcomes.