Advanced Modules¶

Overview¶

Advanced modules provide sophisticated functionality for complex digital health applications, including machine learning, research capabilities, and enterprise features.

Available Advanced Modules¶

1. Machine Learning¶

The ML module provides machine learning capabilities for health data analysis, prediction, and pattern recognition.

import Spezi
import SpeziML

@main
struct MyApp: App {
    var body: some Scene {
        WindowGroup {
            ContentView()
        }
        .spezi(ML())
    }
}

Key Features: - Health data pattern recognition - Predictive analytics - Anomaly detection - Personalized insights

2. Research Framework¶

The Research module enables clinical research capabilities with data collection, analysis, and reporting tools.

import Spezi
import SpeziResearch

@main
struct MyApp: App {
    var body: some Scene {
        WindowGroup {
            ContentView()
        }
        .spezi(Research())
    }
}

Key Features: - Clinical trial support - Data collection protocols - Statistical analysis - Research reporting

3. Interoperability¶

The Interoperability module provides advanced healthcare system integration capabilities.

import Spezi
import SpeziInteroperability

@main
struct MyApp: App {
    var body: some Scene {
        WindowGroup {
            ContentView()
        }
        .spezi(Interoperability())
    }
}

Key Features: - HL7 FHIR integration - DICOM support - EHR system connectivity - Data transformation

4. Security & Compliance¶

The Security module provides enterprise-grade security and compliance features.

import Spezi
import SpeziSecurity

@main
struct MyApp: App {
    var body: some Scene {
        WindowGroup {
            ContentView()
        }
        .spezi(Security())
    }
}

Key Features: - HIPAA compliance - End-to-end encryption - Audit logging - Access control

5. Analytics & Reporting¶

The Analytics module provides comprehensive analytics and reporting capabilities.

import Spezi
import SpeziAnalytics

@main
struct MyApp: App {
    var body: some Scene {
        WindowGroup {
            ContentView()
        }
        .spezi(Analytics())
    }
}

Key Features: - Advanced data visualization - Custom reporting - Trend analysis - Performance metrics

Implementation Examples¶

Machine Learning Integration¶

struct MLHealthPredictor {
    @Environment(\.ml) private var ml

    func predictHealthRisk() async throws -> HealthRiskAssessment {
        let healthData = await collectHealthData()

        return try await ml.predict(
            model: .healthRisk,
            input: healthData,
            confidence: 0.85
        )
    }

    func detectAnomalies() async throws -> [HealthAnomaly] {
        let vitalSigns = await getVitalSigns()

        return try await ml.detectAnomalies(
            in: vitalSigns,
            threshold: 0.9
        )
    }
}

struct HealthInsightsView: View {
    @State private var insights: [HealthInsight] = []
    @State private var isLoading = false

    var body: some View {
        VStack {
            if isLoading {
                ProgressView("Analyzing your health data...")
            } else {
                ForEach(insights) { insight in
                    InsightCard(insight: insight)
                }
            }
        }
        .task {
            await loadInsights()
        }
    }

    private func loadInsights() async {
        isLoading = true
        defer { isLoading = false }

        let predictor = MLHealthPredictor()
        let riskAssessment = try? await predictor.predictHealthRisk()
        let anomalies = try? await predictor.detectAnomalies()

        insights = generateInsights(
            from: riskAssessment,
            anomalies: anomalies
        )
    }
}

Research Study Setup¶

struct ResearchStudy {
    @Environment(\.research) private var research

    func setupStudy() async throws {
        let study = Study(
            id: "heart-health-2024",
            title: "Heart Health Monitoring Study",
            description: "Study to evaluate heart health patterns",
            duration: .months(6),
            participants: 1000
        )

        let protocol = StudyProtocol(
            dataPoints: [
                .heartRate,
                .bloodPressure,
                .activityLevel,
                .sleepQuality
            ],
            frequency: .daily,
            consentRequired: true
        )

        try await research.createStudy(study, protocol: protocol)
    }

    func enrollParticipant() async throws {
        let participant = Participant(
            id: UUID(),
            demographics: collectDemographics(),
            consent: obtainConsent()
        )

        try await research.enrollParticipant(participant, in: "heart-health-2024")
    }
}

struct StudyDashboardView: View {
    @Environment(\.research) private var research
    @State private var studyData: StudyData?

    var body: some View {
        VStack {
            if let data = studyData {
                StudyProgressView(data: data)
                ParticipantListView(participants: data.participants)
                DataCollectionView(metrics: data.metrics)
            } else {
                ProgressView("Loading study data...")
            }
        }
        .task {
            studyData = await research.getStudyData("heart-health-2024")
        }
    }
}

Enterprise Security¶

struct EnterpriseSecurityManager {
    @Environment(\.security) private var security

    func setupEnterpriseSecurity() async throws {
        // Configure encryption
        try await security.configureEncryption(
            algorithm: .AES256,
            keyManagement: .hardwareBacked
        )

        // Set up audit logging
        try await security.enableAuditLogging(
            events: [.dataAccess, .dataModification, .userAuthentication],
            retention: .years(7)
        )

        // Configure access controls
        try await security.configureAccessControl(
            roles: [
                .admin: [.read, .write, .delete],
                .clinician: [.read, .write],
                .patient: [.read]
            ]
        )
    }

    func authenticateUser() async throws -> UserSession {
        return try await security.authenticate(
            method: .biometric,
            requireMFA: true
        )
    }
}

Best Practices¶

1. Performance Optimization¶

Use background processing for ML tasks
Implement caching for frequently accessed data
Optimize data transfer and storage

2. Privacy and Security¶

Implement data minimization principles
Use differential privacy for analytics
Regular security audits and updates

3. Scalability¶

Design for horizontal scaling
Use efficient data structures
Implement proper error handling

4. Compliance¶

Stay updated with regulatory requirements
Implement proper consent management
Maintain audit trails

5. User Experience¶

Provide clear explanations of advanced features
Offer opt-out options for ML features
Ensure transparency in data usage

Integration Patterns¶

Combining Multiple Advanced Modules¶

@main
struct AdvancedHealthApp: App {
    var body: some Scene {
        WindowGroup {
            ContentView()
        }
        .spezi(
            // Core modules
            HealthKit(),
            User(),

            // Advanced modules
            ML(
                models: [.healthRisk, .anomalyDetection],
                enableOnDevice: true
            ),
            Research(
                studies: ["heart-health-2024"],
                enableDataSharing: true
            ),
            Interoperability(
                fhirServer: "https://fhir.example.com",
                enableDICOM: true
            ),
            Security(
                encryptionLevel: .enterprise,
                enableAuditLogging: true
            ),
            Analytics(
                enableAdvancedMetrics: true,
                enableCustomReports: true
            )
        )
    }
}