## Healthcare/Medtech

### Common business problems & personas

#### Key business problems
- **Poor patient outcomes** – Patients not achieving desired health results
- **Low user adoption** – Healthcare providers not using the system effectively
- **Compliance violations** – Difficulty meeting HIPAA and other regulatory requirements
- **Poor clinical workflow efficiency** – Inefficient processes causing delays
- **Data accuracy issues** – Incorrect or incomplete patient data
- **High training costs** – Expensive to train healthcare staff on new systems
- **Poor integration** – Systems not working well with existing healthcare infrastructure
- **Security concerns** – Patient data privacy and security risks

#### Primary personas & their pain points

**Clinical Teams**
- **Pain points:** Poor clinical workflow efficiency, data accuracy issues, difficulty tracking patient outcomes
- **PostHog solutions:** Workflow optimization, data quality monitoring, patient outcome tracking, clinical efficiency analytics

**Compliance Officers**
- **Pain points:** Compliance violations, poor audit trails, difficulty meeting regulatory requirements
- **PostHog solutions:** Compliance monitoring, audit trail automation, regulatory reporting, data access tracking

**IT/Engineering Teams**
- **Pain points:** Poor system integration, security concerns, performance issues
- **PostHog solutions:** Integration monitoring, security event tracking, performance optimization, system health monitoring

**Training Teams**
- **Pain points:** High training costs, poor user adoption, difficulty measuring training effectiveness
- **PostHog solutions:** User adoption tracking, training effectiveness measurement, onboarding optimization, learning analytics

**Product Managers**
- **Pain points:** Poor user experience, low feature adoption, difficulty measuring clinical impact
- **PostHog solutions:** User experience analysis, feature adoption tracking, clinical impact measurement, product optimization

### Key metrics & PostHog

#### Patient Outcomes
**Importance:** This is the core metric for any healthcare product, measuring the actual health impact on patients. Demonstrating positive patient outcomes is crucial for clinical validation, provider adoption, regulatory approval, and building patient trust. It is the ultimate measure of product value and efficacy.

**PostHog approach:** Track key events in the patient journey, such as `treatment_plan_started`, `outcome_measured`, and `follow_up_completed`. Use properties to segment by treatment type, patient demographics, and specific outcome metrics. Cohort analysis can track how outcomes trend over time for different patient groups. Dashboards can visualize progress towards clinical goals, and correlation analysis can help identify which product features are linked to better outcomes. Non-technical users, like clinicians, can use dashboards to monitor patient progress without writing code.

#### Compliance Metrics
**Importance:** Healthcare is a highly regulated industry (e.g., HIPAA in the US). Compliance metrics track adherence to these regulations, particularly around data privacy and security. Failure to comply can result in severe penalties, loss of trust, and legal action, making it a foundational requirement for any MedTech product.

**PostHog approach:** Track all compliance-related events, such as `hipaa_audit_trail_accessed`, `data_access_logged`, and `patient_consent_obtained`. Properties should include the user role, type of data accessed, and audit results to create an immutable log. Dashboards can provide a real-time view of compliance activities, and alerts can be set up for any unauthorized access attempts or compliance failures. Non-technical compliance officers can use these dashboards to monitor activity and generate reports.

#### User Adoption
**Importance:** Measures how effectively healthcare providers (doctors, nurses, etc.) are integrating a new tool into their daily work. Low adoption by clinicians can undermine the intended benefits of a technology, regardless of its potential. High adoption is key to realizing efficiency gains and improving patient care at scale.

**PostHog approach:** Track user interactions such as `feature_used`, `workflow_completed`, and `training_module_completed`. Segment by user role (e.g., doctor, nurse) using properties. Adoption funnels can show where users drop off during onboarding. Session recordings are invaluable for understanding how clinicians use the product in a real-world context. Alerts can flag low adoption in specific departments. Non-technical training teams can analyze session recordings to improve their training materials.

#### Clinical Workflow Efficiency
**Importance:** Measures the time and effort required for clinicians to complete tasks using the product. In the high-pressure healthcare environment, time is a critical resource. Improving workflow efficiency can reduce clinician burnout, lower operational costs, and allow more time for direct patient care.

**PostHog approach:** Track workflow events from start to finish: `workflow_started`, `step_completed`, `workflow_completed`. Use properties to capture the duration of each step and the user role. Funnel analysis is perfect for identifying bottlenecks where users get stuck or take too long. Dashboards can monitor average completion times for key workflows. Non-technical managers can use these funnels to identify areas for process improvement without needing technical assistance.

#### Data Accuracy
**Importance:** In healthcare, critical decisions are made based on patient data. Inaccurate or incomplete data can lead to misdiagnosis, incorrect treatment, and serious patient harm. This metric tracks the integrity and reliability of the data within the system, which is fundamental to patient safety.

**PostHog approach:** Track data entry and validation events like `data_entered`, `data_validated`, and `error_detected`. Use properties to specify the data type, validation method, and error type. Create dashboards to monitor data quality trends and error rates. Correlation analysis can help identify if specific user roles or workflow steps are associated with higher error rates. Alerts can notify teams of spikes in data entry errors, allowing for swift investigation.