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AI Care Companion Cuts Patient Attrition 20% for Cardiovascular Programme

An eleven month engagement for a Massachusetts cardiovascular care company: strategic assessment, discovery, solutioning, and phased implementation across two tracks — revenue and operations automation, and a custom patient engagement platform with an AI care companion and AI session intelligence. 20% attrition reduction within 90 days.

Simple Health Cardiovascular Case Study
Home / Success Stories / Custom Patient App, AI Care Companion, and Revenue Cycle Automation for Cardiovascular Disease Management

Custom Patient App, AI Care Companion, and Revenue Cycle Automation for Cardiovascular Disease Management

Industry
Healthcare & Life Sciences
Geography
United States
Cooperation Period
11 months

Summary

Clixlogix delivered an eleven month engagement for a Massachusetts cardiovascular care company, spanning strategic assessment, discovery, solutioning, and a phased implementation across two parallel tracks. Track A automated the revenue and operational workflows behind the referral to billing funnel. Track B replaced a vendor locked patient application with a custom Flutter platform built on the existing electronic health record, an AI care companion grounded in the Client’s clinical protocol, and AI session intelligence running on a rolling transcript pipeline. Within 90 days of deployment, monthly patient attrition fell by approximately one fifth, daily active app users tripled, and the AI care companion handled three fifths of routine patient queries.

About the Client

The Client is a chronic care management company based in Massachusetts that delivers insurance reimbursed cardiovascular health programmes to hospitals, health systems, and self insured employers across the United States. Through a network of licensed clinical practitioners, the Client treats two patient cohorts. The first is post discharge cardiovascular patients requiring rapid enrolment into structured rehabilitation within the first week of referral. The second is long term hypertension and cholesterol management cases with extended programme timelines carrying higher between session disengagement risk. The Client operates a proprietary intervention protocol and is in network with major US health insurers, with most patients accessing the programme at zero or minimal out of pocket cost.

The Challenge

The Client engaged Clixlogix to address revenue leakage in the patient referral funnel, reduce manual operational load, and build a custom patient engagement platform on top of the existing electronic health record (EHR) infrastructure.

At the time of engagement, the Client was processing hundreds of patient referrals monthly. Approximately 60% of referrals converted to a booked appointment. Of those, roughly 70% completed a billable session. The remaining patients were lost between referral intake and revenue capture, producing annualised revenue leakage in the millions.

Five operational issues were driving the loss:

  • Low self scheduling adoption. Self scheduling sat below 30%, leaving the majority of referrals dependent on manual staff coordination.
  • PBM enrolment failures. Insurance enrolment failed the majority of the time at one specific step, pharmacy benefit manager (PBM) registration for the medication management component, where over 75% of attempts failed and opened a multi day window in which patients abandoned the process.
  • Manual RPM review. Remote patient monitoring (RPM) data, including blood pressure readings and resting heart rate, was reviewed by hand, which did not scale with patient volume and left long gaps between appointments without automated clinical oversight.
  • Fragmented partner reporting. Hospital partner reporting was assembled manually by different team members through different systems at different cadences.
  • Vendor locked patient app. The existing patient facing application, built on a vendor white label product, could not be extended to support custom features or AI driven engagement.

The Client also held a substantial untapped asset. Session transcription infrastructure had been recently deployed and was capturing data reliably, but the transcripts sat unused as a structured data source ready for intelligence work. Transcripts were stored but not yet processed for protocol adherence, follow up prompting, or compliance monitoring.

The Solution

Clixlogix delivered the engagement in four sequential phases. Each phase produced a defined deliverable and required sign off before the next phase began.

Phase Timeline

Fig 1 – Phase Timeline

Phase 1: Strategic Assessment

Before scoping any technical work, Clixlogix conducted independent research across the Client’s competitive landscape, technology stack, patient demographics, unit economics, and operational workflows. The objective was to determine where the Client’s defensible advantages sat, where the competitive threats were closing in, and where capital deployment would return the highest economic value. Three findings shaped the engagement structure.

First, the market had shifted. In the preceding eighteen months, well funded competitors had deployed AI powered features that were becoming standard across the competitive set, including AI care companions, computer vision lifestyle tracking, wearable integration, and AI scribes. The Client’s defensible assets, which included hospital B2B referral relationships, a proprietary clinical protocol, and specialist cohort expertise, sat outside what capital alone could replicate. Clixlogix assessed that the technology gap could be closed in months while the relationship and protocol gaps competitors faced would take years to close.

Consulting Insight

Clixlogix reframed the engagement from defensive modernisation to offensive capability expansion. The Client’s competitive moat was secure. The build was about pulling forward while the moat held.

Second, the opportunity sat in attrition, not acquisition. By mapping the full referral to billing funnel against the Client’s operational data, Clixlogix identified that the largest economic opportunity sat in attrition prevention, well above the return available from increased patient acquisition spend. The referral pipeline carried sufficient volume. The leakage between referral and billing represented the highest return target for automation investment.

Consulting Insight

The Client had assumed acquisition was the constraint. The funnel analysis showed the loss sat downstream. Stopping the bleed returned more value than driving new volume, validated entirely from the existing operational data.

Third, an unpackaged B2B product already existed. The Client was already operating multiple medication management protocols internally that remained packaged for clinical use only, ready for B2B productisation. Clixlogix assessed that these protocols represented a productisation opportunity addressable to self insured employers and PBMs, with projected annual medication spend reductions per employee cohort. The revenue line needed only commercial packaging, with the technology already in place.

Consulting Insight

The most economically significant insight of the assessment centred on identifying an unpackaged B2B product hidden inside the Client’s existing clinical operations, addressable to employers and PBMs through commercial packaging alone.

These findings led Clixlogix to structure the engagement in two parallel tracks. Track A automated revenue and operational workflows. Track B built the patient engagement platform.

Phase 2: Discovery

The paid discovery engagement ran for three weeks across fifteen working days and deployed a Technical Architect, a Business Analyst, and a Senior Healthcare AI Consultant.

Revenue Leakage Funnel

Fig 2 – Revenue Leakage Funnel

The Technical Architect mapped the integration surface across the Client’s five connected platforms. These included the EHR and patient portal, the CRM and scheduling system on Salesforce Health Cloud, the billing and revenue cycle platform on Waystar, the AI voice automation system on Twilio, and the session transcription pipeline. The EHR’s REST API documentation was comprehensive, and the team mapped the full endpoint surface within the first week, cataloguing available resources, authentication methods, rate limits, and data model relationships.

The Business Analyst mapped all seven operational workflows, covering referral intake, scheduling, insurance verification, enrolment, appointment completion, billing, follow up, and partner reporting. Joint discovery calls were conducted with the Client’s leadership across business, financial, and technical functions.

Four findings altered the build plan:

  • The PBM bottleneck was concentrated. Enrolment failures clustered at a small number of specific carriers, so direct API integration with those carriers alone could resolve the majority of failures and compress the referral to active patient window from several days to under 48 hours.
  • The CRM already held churn signal. Behavioural data in the CRM was sufficient to train a churn prediction model directly on the existing data.
  • Rolling transcripts beat stored audio. The transcription pipeline was storing full audio at a cost that scaled with session volume; a rolling transcript approach would eliminate this cost while enabling the intelligence capability.
  • The EHR API supported a full custom front end. API coverage was broad enough to build a complete custom application with the existing data set preserved in place.

Consulting Insight

Discovery surfaced that the PBM bottleneck was concentrated at two specific carriers. Solving for those two carriers returned more economic value than building a generic enrolment automation across the full PBM landscape. The 80/20 was visible in the data once the workflow mapping was complete.

The phase concluded with the Master Discovery Report, a consolidated document covering platform assessment, integration constraints, workflow gap analysis, and a prioritised recommendation brief for Solutioning.

Phase 3: Solutioning

Clixlogix produced a fixed cost implementation proposal with milestones and sign off gates. The Client’s leadership reviewed the full technical architecture and approved the plan before any build commitment.

Two Track Solution Architecture

Fig 3 – Two Track Solution Architecture

Track A. Revenue and Operations. Five workstreams were scoped: a churn prediction model on CRM behavioural data with automated outbound re engagement and practitioner alerting; insurance enrolment automation with direct API integration to the primary PBM carriers; a partner reporting engine producing standardised monthly outcome reports across all hospital partners; an AI billing agent on the revenue cycle platform handling copay processing, uninsured patient workflows, and claims reconciliation; and the packaging of the Client’s existing medication management protocols into a B2B product with employer facing ROI documentation.

Track B. Patient Engagement. Seven workstreams were scoped: a custom Flutter mobile application built on the EHR’s REST API; an AI care companion using a retrieval augmented generation architecture grounded in the Client’s clinical protocol and patient health data, with automated escalation to the assigned practitioner when clinical thresholds are crossed; AI session intelligence with two functions running on a rolling transcript pipeline (real time protocol suggestions during sessions, and post session compliance reports for management); RPM integration through a third party data aggregation service for blood pressure and resting heart rate ingestion; AI lifestyle logging using computer vision for meal composition analysis tuned to cardiovascular dietary markers; a progress dashboard rendering cardiovascular health metrics; and native in app telehealth replacing the existing third party video redirect.

Clixlogix proposed the rolling transcript architecture for AI session intelligence after assessing the storage economics of the Client’s existing pipeline. Stored audio carried a per session cost that scaled linearly with patient volume. A rolling transcript approach processing text in place eliminated the cost while simultaneously enabling the intelligence capability. The same architectural choice produced better unit economics and better clinical capability.

Platform Integration Map

Fig 4 – Platform Integration Map

The Flutter app build was made commercially viable by Clixlogix’s API surface mapping in Discovery. The EHR’s REST API covered the entire existing data set, meaning a custom front end could connect to clinical records, scheduling, messaging, and patient profiles with every record preserved in place. The transition kept the data set intact and patient care active throughout.

Clixlogix proposed Microsoft Azure as the cloud infrastructure for both tracks. Azure was chosen for three reasons. First, Azure’s HIPAA Business Associate Agreement covers a broader set of services than alternative cloud providers, which simplified the compliance architecture across the AI care companion, session intelligence pipeline, billing automation, and partner portal. Second, Azure OpenAI Service provided enterprise grade access to GPT models within a HIPAA covered boundary, allowing the AI care companion and session intelligence to operate on patient data while keeping protected health information inside a covered environment by default. Third, Azure Health Data Services offered a managed FHIR API that simplified clinical data integration between the Flutter app, the EHR, and the AI capability. The deployment used encryption at rest and in transit, full audit logging, role based access control, and reserved capacity pricing aligned with the Client’s predictable daily workload profile.

Phase 4: Implementation

Implementation followed a three phase delivery structure. A pilot group of approximately fifty patients across two states validated the AI care companion and session intelligence modules before broader deployment.

Phase 4a: Foundation (Months 1 to 3)

The churn prediction model went live on the CRM within the first six weeks. A daily batch job extracts behavioural features from the CRM (appointment attendance, lifestyle logging consistency, message response latency, session completion history) and outputs a risk score per patient. Patients crossing the intervention threshold trigger an automated outbound call through the voice automation system and a notification to the assigned practitioner’s dashboard. Clixlogix calibrated the threshold to intervene approximately two weeks before the predicted drop off date, giving the care team an actionable window rather than a reactive alert.

Churn Prediction Workflow

Fig 5 – Churn Prediction Workflow

The Flutter application launched with messaging, scheduling, health data logging, RPM integration, the AI care companion, and a progress dashboard rendering blood pressure trends, cholesterol panel results, and resting heart rate over time. All patient data remained in the EHR. The application connected to the existing data set through authenticated REST calls with OAuth 2.0 token management.

AI Care Companion Architecture

Fig 6 – AI Care Companion Architecture

Insurance enrolment automation. Went live for the primary PBM carriers. The median referral to active patient window dropped from several days to under 48 hours.

The second of the two primary PBM carriers presented an unexpected obstacle. Discovery had mapped the carrier’s public API documentation, but the actual enrolment confirmation flow required an undocumented callback verification step the documentation did not mention. The team spent four weeks working directly with the carrier’s developer relations group to obtain the verification flow and integrate it. The delay pushed the second PBM carrier live a month after the first, and the integration approach uncovered during the work later accelerated integration with two additional secondary carriers in Phase 4c.

Automated partner reporting. Replaced the manual process across all hospital partners, standardising scheduled monthly reports to a single cadence. The self service partner portal followed in Phase 4b, giving hospitals on demand access to the same data.

AI session intelligence. Deployed with both functions. Practitioners received real time protocol suggestions during sessions through rolling transcript analysis, and management received automated compliance reports after each session, replacing the manual review process.

Two weeks into the pilot, the clinical team flagged that the AI care companion’s responses to lifestyle questions were drifting toward general DASH diet guidance rather than the Client’s proprietary sodium reduction schedule. The retrieval index was treating the Client’s protocol and external clinical reference material with similar weight, so the model occasionally pulled in generic advice.

“It would tell a patient to gradually reduce sodium when our protocol calls for a specific four week step down,”

~ One of the Client’s senior clinicians explained during the review.

The team rebuilt the retrieval index with explicit document priority weighting, placing the Client’s protocol documentation at the top of the retrieval hierarchy and treating external reference material only as a fallback when no protocol guidance applied. The pilot resumed two weeks later, with the clinical team approving the revised responses across a sample of forty patient queries.

Phase 4b: Differentiation (Months 4 to 6)

Native in app telehealth. Replaced the third party video redirect, implemented via a WebRTC video component integrated directly into the Flutter application.

Practitioner adoption of the real time session prompting feature ran below projections in the first month of broad deployment. Interviews with the resistant practitioners surfaced a clear signal: the suggestions were arriving too frequently and breaking the conversational rhythm of clinical sessions. The team added a configurable suggestion frequency setting and introduced an intervention only mode that surfaces prompts only when a clinical threshold is crossed, with a daily summary digest replacing the in session prompting for routine items. Adoption recovered to projected levels within three weeks of the configuration update, and the daily digest became a feature several practitioners cited as more useful than the original real time prompting.

The phase also delivered:

  • AI lifestyle logging. Patients photograph their meal, a computer vision model identifies food items and estimates sodium, saturated fat, and macronutrient composition, and the entry is written to the patient’s health record in the EHR. The model was trained on a nutrition dataset augmented with the Client’s cardiovascular dietary protocol categories.
  • RPM anomaly detection. The AI care companion expanded to send automated alerts to the patient and the assigned practitioner when blood pressure or heart rate data indicated a sustained out of range trend.
  • B2B partner portal. Launched as a React web application consuming the same NestJS API service powering the Flutter app. Hospital partners accessed referral conversion funnels, clinical outcome trends, and cohort level analytics through a self service dashboard with role based access control.
  • In app educational content. Launched as structured courses generated from the Client’s session transcript library through an extractive summarisation pipeline.
  • Billing automation. Deployed via the revenue cycle platform API, handling copay processing, claims submission, and reconciliation through an AI agent.

Phase 4c: Market Leadership (Months 7 to 9)

  • Packaged B2B medication management product. Launched targeting self insured employers and PBMs. The existing clinical protocols were consolidated into a single offering with employer facing ROI documentation showing projected annual medication spend reductions per employee cohort.
  • Health metric forecasting engine. A time series prediction model trained on each patient’s historical blood pressure readings, medication adherence, and lifestyle adherence history to generate 30 day and 90 day trajectory projections.
  • Composite cardiovascular health score. Aggregates blood pressure time in range, cholesterol trend direction, lifestyle logging consistency, appointment adherence, and medication adherence into a 0 to 100 index updated daily.
  • Multilingual care companion and advanced cohort analytics. The AI care companion gained multilingual support through a translation service, and hospital partners gained programme level reporting with segmentation by referral source, programme duration, and clinical outcome tier.

Results

The Client received an end to end patient engagement and revenue cycle platform that automated the operational workflows responsible for the majority of revenue leakage, replaced a vendor locked patient application with a custom extensible platform on the existing EHR, and introduced AI capabilities across patient engagement, clinical session support, and compliance monitoring.

20% Lower Monthly Patient Attrition

20% Lower Monthly Patient Attrition

The churn prediction model reached at risk patients approximately two weeks before predicted drop off through automated outbound calls and practitioner alerts. Monthly attrition decreased by approximately one fifth within the first 90 days of deployment.

3x Increase in Daily Active App Users

3x Increase in Daily Active App Users

The custom Flutter application launched with the AI care companion, RPM integration, and progress dashboard drove daily active users to roughly three times the previous vendor portal baseline.

60% of Patient Queries Handled by AI

60% of Patient Queries Handled by AI

The AI care companion handled over three fifths of routine programme, lifestyle, and medication adherence questions without practitioner involvement, while clinical decisions remained with the assigned practitioner.

Referral to Active Patient in Under 48 Hours

Referral to Active Patient in Under 48 Hours

Insurance enrolment automation for the primary PBM carriers compressed the median referral to active patient window from several days to under two days, removing the gap that had been driving the majority of patient abandonment.

100% Hospital Partner Reporting Automated

100% Hospital Partner Reporting Automated

All hospital partner outcome reports were automated and standardised to a monthly cadence, replacing the fragmented manual process that had previously consumed significant staff time across multiple team members.

Compliance Monitoring Across All Sessions

Compliance Monitoring Across All Sessions

Post session protocol adherence and compliance monitoring shifted from manual audio review by senior clinicians to automated transcript analysis with structured deviation reporting delivered to management after each session. Manual review is reserved for edge cases flagged by the system.

Across the first 90 days of deployment, the platform reduced monthly patient attrition by approximately one fifth, tripled daily active app users against the previous vendor portal baseline, and shifted the majority of routine patient queries and all post session compliance monitoring onto AI, with clinical decisions remaining with the assigned practitioner.

Technologies and Tools

Mobile: Flutter (custom patient application on the EHR REST API)

Frontend: React (B2B hospital partner portal)

Backend: NestJS, REST API integration, OAuth 2.0 token management

CRM and Revenue Cycle: Salesforce Health Cloud, Waystar, Twilio

Cloud and Infrastructure: Microsoft Azure (Azure OpenAI Service, Azure Health Data Services managed FHIR API, HIPAA compliant deployment with encryption, audit logging, and role based access control)

AI and Machine Learning: Retrieval augmented generation, logistic regression (churn prediction), BERT based NLP, computer vision (lifestyle logging), time series forecasting

Real Time: WebRTC (native in app telehealth)

Services Delivered
AI Software & Application Development, Mobile App, AI Strategy Consulting, CRM Integration, Digital Engineering
Team Composition
Solution Architect, Business Analyst, Healthcare AI Consultant, Mobile App Engineers, Backend Engineers, Frontend Engineers, AI/ML Engineers, Project Manager

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