Assessing and Measuring Positive Healthcare Effects in Germany’s Digital Health Applications (DiGA)

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The Digital Health Applications (DiGA) framework, established under the Digital Healthcare Act (DVG) in 2019, allows certain digital health apps to be prescribed by physicians and reimbursed by statutory health insurance. 

The following provides an in-depth look at the parameters governing DiGAs:

1. Regulatory Classification and Approval

2. Positive Healthcare Effect

3. Data Protection and Security

4. Technical Standards and Interoperability

5. Listing in the DiGA Directory

6. User Experience (UX) Considerations

To qualify as a DiGA, digital health applications must demonstrate a Positive Healthcare Effect, evaluated through Bona Fide Benefits and Structure and Process Quality Improvements.

Bona Fide Benefits and Structure and Process Quality Improvements form the cornerstone of assessing the efficacy of DiGAs. These benefits are measured through rigorous scientific methodologies to ensure that the applications not only deliver clinical advantages but also enhance the overall efficiency of healthcare delivery.

Patient-reported outcomes (PROs) are subjective measures reported directly by patients about their health status and quality of life. These outcomes are pivotal in assessing the real-world effectiveness of DiGAs.

Instruments such as the SF-36 (Short Form Health Survey), EQ-5D (EuroQol-5 Dimension), and disease-specific scales like the Fibromyalgia Impact Questionnaire (FIQ) are commonly used. For instance, the SF-36 assesses multiple dimensions of health, including physical functioning, bodily pain and mental health, providing a comprehensive overview of a patient’s quality of life.

Digital Surveys and App-Based Assessments: PROs can be integrated directly into the DiGA, allowing for real-time data collection and monitoring. For example, the DiGA “Kaia Back Pain” integrates PROs to monitor patients’ pain levels and activity, enabling timely interventions based on patient feedback.

Quality of Life (QoL): Assessed using scales like the SF-36, which measures physical and mental health domains. Improved QoL scores indicate that the DiGA is effectively enhancing patients’ overall well-being.

Symptom Relief: Quantified through symptom-specific scales (e.g., pain intensity ratings from 1 to 10). For example, a DiGA for chronic pain management may show a significant reduction in pain scores over six months.

Mental Health Status: Evaluated using tools like the Beck Depression Inventory (BDI) or General Anxiety Disorder-7 (GAD-7). A DiGA designed to support mental health may demonstrate reductions in BDI scores, indicating improved depressive symptoms.

Physical Activity Levels: Measured using activity trackers integrated within the DiGA or self-reported via questionnaires. The “Vivira” app, targeting back pain, incorporates activity tracking to encourage and monitor increased physical activity among users.

Clinical outcomes provide objective measures of the application’s impact on health status and healthcare utilisation.

Electronic Health Records (EHRs): Integration with EHR systems allows for the extraction of clinical data such as hospital admission rates, medication adherence and disease progression. For instance, a DiGA managing diabetes can integrate with EHRs to monitor HbA1c levels and adjust treatment plans accordingly.

Clinical Trials: Structured studies that compare health outcomes between users and non-users of the DiGA. An example is an RCT evaluating a DiGA for post-COVID-19 fibromyalgia-like syndrome, comparing symptom relief and QoL improvements between users and a control group over six months.

Reduced Hospital Admissions: Tracking the frequency of hospital stays before and after DiGA usage. For example, the DiGA “Companion Patella”, aimed at knee and patella pain, has shown a reduction in hospital admissions due to improved pain management.

Improved Disease Management: Monitoring biomarkers, disease-specific scores (e.g., Disease Activity Score (DAS) for rheumatoid arthritis), and adherence to treatment protocols. A DiGA for rheumatoid arthritis may demonstrate improvements in DAS scores, indicating better disease control.

Enhanced Treatment Adherence: Measuring the consistency of medication intake or therapy participation using digital logs or pharmacy refill data. The DiGA “Mawendo”, focusing on post-operative rehabilitation, has recorded higher adherence rates through its interactive reminders and progress tracking features.

Structure and Process Quality Improvements

These improvements focus on optimising healthcare delivery processes and enhancing the integration of DiGAs into existing healthcare infrastructures.

Workflow Analysis: Evaluating changes in clinical workflows through process mapping and time-motion studies. For instance, integrating a DiGA like “HelloBetter” for chronic pain management can streamline patient-provider communication, reducing the time clinicians spend on routine follow-ups.

Data Management Audits: Assessing the efficiency and accuracy of data handling processes. A DiGA designed for diabetes management may undergo audits to ensure accurate data capture and secure storage of patient information

Data Management Efficiency: Time taken to input, retrieve, and process patient data.

Streamlined Communication: Frequency and quality of interactions between patients and healthcare providers facilitated by the DiGA.

Workflow Efficiency: Reduction in time spent on administrative tasks, allowing healthcare providers to focus more on patient care.

Interoperability Testing: Ensuring that DiGAs can seamlessly exchange data with other healthcare systems and EHRs. The DiGA “Companion Patella” underwent extensive interoperability testing to ensure smooth data flow with existing orthopaedic care systems.

System Integration Evaluations: Reviewing the compatibility of DiGAs with existing IT infrastructures. This involves assessing the technical requirements and ensuring that the DiGA can integrate without disrupting current healthcare operations.

Data Exchange Rates: Percentage of successful data transfers between the DiGA and other systems.

Integration Smoothness: Number of technical issues encountered during data exchanges.

Coordinated Care Outcomes: Improvements in patient care coordination, such as timely referrals and consolidated patient histories.

Clinical Trials and Studies

To substantiate the Positive Healthcare Effect, DiGAs undergo rigorous clinical evaluations through various types of studies.

Mandatory and Optional Evidence

Mandatory Evidence:

For a DiGA to be listed in the DiGA directory and eligible for reimbursement, it must demonstrate a positive healthcare effect. This is achieved through:

• Randomised Controlled Trials (RCTs): Gold-standard studies where participants are randomly assigned to either the intervention (DiGA) or control group, ensuring unbiased comparisons.
  • Example: An RCT evaluating a DiGA for post-COVID-19 fibromyalgia-like syndrome might compare symptom relief and QoL improvements between users and non-users over six months. Results from such a trial could show that 70% of DiGA users report significant symptom relief compared to 40% in the control group.
• Observational Studies: Non-randomised studies that observe outcomes in real-world settings, useful for long-term and large-scale assessments.
  • Example: A cohort study tracking hospital admission rates among DiGA users versus a matched control group over a year could reveal a 15% reduction in admissions among DiGA users.
• Real-World Evidence (RWE): Data collected from routine clinical practice outside the controlled environment of trials, providing insights into the DiGA’s effectiveness in everyday use.
  • Example: Analyzing data from EHRs to assess the impact of a DiGA on treatment adherence and subsequent health outcomes might show that users of the DiGA “Mawendo” exhibit a 20% higher adherence rate to post-operative rehabilitation protocols.

Optional Evidence:

While not always required, conducting clinical trials enhances the robustness of the evidence base, increasing the DiGA’s credibility and acceptance among healthcare providers.

• Enhanced Credibility: Clinical trials provide high-quality evidence of efficacy and safety, making it easier for physicians to prescribe DiGAs confidently.
  • Example: The DiGA “HelloBetter” for chronic pain management conducted an RCT that demonstrated a statistically significant improvement in pain scores and mental health outcomes, bolstering its credibility among clinicians.
• Market Acceptance: Strong evidence from trials can facilitate broader adoption and integration into clinical guidelines.
  • Example: Positive trial results can lead to endorsements from medical associations, further encouraging healthcare providers to integrate the DiGA into their practice.

Study Design Considerations

• Randomisation and Control Groups: Ensures unbiased allocation and serves as a baseline for comparison.
  • Implementation: Using computer-generated sequences to assign participants.
  • Control Groups: May include placebo controls or standard care controls.

• Outcome Measures:
  • Relevance and Alignment: Outcomes must align with DiGA’s therapeutic goals, such as reduced pain levels or improved mobility.
    • Example: For a DiGA targeting diabetes management, relevant outcomes might include HbA1c levels, incidence of diabetic complications, and patient adherence to insulin therapy.
  • Measurability: Outcomes should be objectively and reliably measurable using validated tools.
    • Example: Using continuous glucose monitoring data to assess the impact of a diabetes management DiGA on blood sugar control.
  • Types of Outcome Measures:
    • Primary Outcomes: Main results, e.g., improvement in PROs.
    • Secondary Outcomes: Additional effects, e.g., changes in healthcare utilisation or cost-effectiveness.
  • Measurement Tools:
    • Validated Instruments: Established questionnaires ensure reliability and validity.
      • Example: Employing the Beck Depression Inventory (BDI) to measure changes in mental health status among users of a mental health DiGA.
    • Digital Tracking: Continuous and real-time data collection through DiGA capabilities.
      • Example: Integrating wearable devices with the DiGA “Kaia Back Pain” to track daily physical activity levels and correlate them with pain relief.

Health Economics of DiGAs

Assessing the economic implications of DiGAs is crucial for their integration into the healthcare system. Health economic evaluations provide insights into cost-effectiveness, budget impact, and overall economic sustainability.

Cost-Effectiveness Analysis (CEA)

CEA compares the relative costs and outcomes of different interventions to determine the value for money provided by DiGAs.

• Metrics:
  • Quality-Adjusted Life Years (QALYs): Combines quantity and quality of life, providing a comprehensive measure of health benefits.
  • Incremental Cost-Effectiveness Ratio (ICER): Ratio of cost changes to QALYs gained compared to standard care.
• Assessment Process:
  • Cost Identification: Includes development, implementation, and maintenance costs.
    • Example: The total cost of developing and deploying “Vivira” includes software development, user training, and ongoing technical support.
  • Outcome Measurement: Quantifies health benefits using QALYs gained through the DiGA.
  • ICER Calculation: Determines if the DiGA is cost-effective based on willingness-to-pay thresholds (€20,000 – €50,000 per QALY).

Budget Impact Analysis (BIA)

BIA evaluates the financial impact of adopting DiGAs within the statutory health insurance framework. Budget Impact Analysis (BIA) assesses the financial implications of adopting Digital Health Applications (DiGAs) within the statutory health insurance framework. It provides healthcare payers with critical insights for budget planning and resource allocation, ensuring that the integration of DiGAs is both economically feasible and sustainable.

Components of BIA

1. Adoption Rates:
   • Definition: Projecting the number of patients who will adopt and consistently use the DiGA over a specific period.
   • Considerations: Factors influencing adoption rates include patient demographics, disease prevalence, physician endorsement, user-friendliness of the app, and marketing efforts.
   • Example: Estimating that 10,000 patients will use the DiGA “Mawendo” for post-operative rehabilitation within the first year of its launch.
2. Cost Savings:

1. Definition: Estimating the reductions in healthcare costs resulting from the effective use of DiGAs.
2. Types of Cost Savings:
   • Reduced Hospital Admissions: Fewer hospital stays due to better disease management.
   • Decreased Emergency Visits: Lower frequency of emergency room visits through proactive symptom monitoring.
   • Lower Medication Costs: Reduced reliance on medications through effective non-pharmacological interventions.
   • Improved Treatment Adherence: Enhanced adherence leading to fewer complications and subsequent treatments.
3. Example: The DiGA “Vivira” for back pain management leads to a 10% reduction in emergency room visits, saving approximately €50,000 annually.
4. Implementation Costs:

1. Definition: Expenses associated with integrating DiGAs into existing healthcare infrastructures.
2. Components:
   • Technical Integration: Costs of integrating the DiGA with Electronic Health Records (EHRs) and other healthcare IT systems.
   • Training and Education: Expenses for training healthcare providers and patients on using the DiGA effectively.
   • Support and Maintenance: Ongoing costs for technical support, system updates, and user assistance.
3. Example: Implementing “Companion Patella” requires €40,000 for IT integration, €20,000 for training orthopaedic specialists, and €10,000 annually for maintenance.

The primary outcome of a BIA is to provide healthcare payers with a clear understanding of the financial impact of adopting a DiGA. This includes:

• Budget Planning: Informing allocation of funds towards DiGAs based on projected cost savings and implementation expenses.
• Resource Allocation: Guiding decisions on where to invest resources to maximise cost-effectiveness and healthcare outcomes.
• Financial Feasibility: Ensuring that the adoption of DiGAs does not strain the healthcare budget and is sustainable in the long term.

Example : “Mawendo” for Post-Operative Rehabilitation

• Adoption Rates: Projected uptake of 10,000 patients within the first year.
• Cost Savings:
  • Reduced Hospital Readmissions: 15% decrease in readmission rates, saving €150,000 annually.
  • Faster Recovery Times: Shorter rehabilitation periods reduce overall hospital stay costs by €100,000.
• Implementation Costs:
  • Integration Costs: €50,000 for IT systems integration.
  • Training Costs: €25,000 for training physiotherapists and healthcare providers.
  • Maintenance Costs: €15,000 annually for technical support.
• BIA Outcome: The cost savings of €250,000 outweigh the implementation costs of €90,000, indicating a positive budget impact with payback achieved within the first two years.

Return on Investment (ROI) assesses the financial returns generated by a Digital Health Application (DiGA) about the costs incurred during its development and implementation. This metric is pivotal in determining the economic viability of a DiGA, enabling stakeholders to make informed decisions regarding investment and further development.

ROI measures the efficiency and profitability of investing in a DiGA by comparing the benefits it delivers against the associated costs. Unlike simple calculations, a comprehensive ROI assessment encompasses both direct financial impacts, such as cost savings and revenue generation, and indirect benefits, including improved patient outcomes and enhanced healthcare delivery efficiency. This multifaceted approach provides a nuanced understanding of the DiGA’s overall economic value.

The ROI of a DiGA is determined through an in-depth analysis that includes various financial and non-financial factors:

1. Total Benefits:
   • Financial Gains: These include cost savings from reduced hospital admissions, decreased necessity for in-person consultations, and enhanced treatment adherence that leads to fewer complications. For example, a DiGA managing chronic pain effectively can result in significant reductions in medication costs, fewer emergency room visits, and improved patient productivity.
   • Non-Financial Benefits: Improved patient outcomes, increased quality of life, and greater efficiency in healthcare delivery are also considered. Enhanced patient satisfaction and reduced burden on healthcare providers contribute to the overall value of the DiGA.
2. Total Costs:

1. Development Expenditures: Initial costs related to software development, clinical content integration and user interface design.
2. Implementation Expenses: Costs associated with training healthcare providers, integrating the app into existing healthcare systems, and marketing.
3. Maintenance and Operational Costs: Ongoing expenses for technical support, updates, and user assistance over time.

By thoroughly evaluating both benefits and costs, ROI provides a comprehensive picture of the DiGA’s financial performance and sustainability.

Positive ROI: Indicates that the financial benefits of the DiGA surpass its costs, demonstrating economic viability. This supports sustained investment and further development, as the DiGA proves to be a financially sound addition to the healthcare system. A positive ROI not only covers initial investments but also generates additional financial value, making the DiGA an attractive proposition for healthcare providers and investors.

Negative ROI: Suggests that the costs outweigh the benefits, necessitating a re-evaluation of the DiGA’s implementation strategy, functionality, or cost structure. This might involve optimising the app’s features, enhancing user engagement strategies, or reducing operational expenses to improve financial performance.

Examples:

“HelloBetter” for Chronic Pain Management

“HelloBetter” is a DiGA designed to support individuals suffering from chronic pain. A comprehensive ROI analysis of “HelloBetter” considers multiple dimensions:

• Cost Savings: By reducing the need for frequent in-person physiotherapy sessions, “HelloBetter” saves approximately €80,000 annually in healthcare costs.
• Enhanced Treatment Adherence: Improved adherence to pain management protocols leads to fewer complications and hospital admissions, resulting in additional savings of €60,000.
• Increased Productivity: Patients experience less pain and better quality of life, contributing to €40,000 in increased workplace productivity and reduced absenteeism.
• Development Costs: €100,000 invested in software development, clinical content integration, and user interface design.
• Implementation Costs: €30,000 allocated for training healthcare providers and integrating the app into existing healthcare systems.
• Maintenance and Support: €20,000 for ongoing technical support, updates, and user assistance over the first year.

The total benefits amount to €180,000 (€80,000 + €60,000 + €40,000), while the total costs are €150,000 (€100,000 + €30,000 + €20,000). This results in a positive ROI of 20%, indicating that “HelloBetter” generates €0.20 in benefits for every euro invested, thereby justifying continued investment and expansion.

“Companion Patella” for Knee and Patella Pain

“Companion Patella” targets individuals with knee and patella pain, aiming to reduce surgical interventions and improve pain management.

• Reduced Hospital Admissions: Effective pain management through “Companion Patella” leads to a 25% reduction in hospital admissions, saving approximately €120,000 annually.
• Decreased Need for Surgeries: By managing pain non-invasively, the DiGA reduces the need for knee surgeries, resulting in savings of €150,000.
• Lower Medication Costs: Enhanced pain control reduces reliance on expensive pain medications, saving an additional €50,000.
• Development Costs: €200,000 for advanced software development, integration with orthopaedic care systems, and clinical content creation.
• Implementation Costs: €50,000 for training orthopaedic specialists and integrating the app into hospital workflows.
• Maintenance and Support: €30,000 for ongoing technical support, system updates, and user training over the first year.

The total benefits amount to €320,000 (€120,000 + €150,000 + €50,000), while the total costs are €280,000 (€200,000 + €50,000 + €30,000). This results in a positive ROI of approximately 14.3%, demonstrating that “Companion Patella” provides a substantial financial return, supporting its continued investment and integration into orthopaedic care protocols.

“Mawendo” for Post-Operative Rehabilitation

“Mawendo” focuses on enhancing post-operative rehabilitation adherence, thereby improving recovery outcomes and reducing hospital readmissions.

• Reduced Readmission Rates: Improved rehabilitation adherence decreases hospital readmissions by 20%, saving €200,000 annually.
• Faster Recovery Times: Enhanced rehabilitation leads to shorter hospital stays and quicker patient turnover, resulting in savings of €100,000.
• Lower Rehabilitation Costs: Efficient use of rehabilitation resources reduces overall rehabilitation programme costs by €80,000.
• Development Costs: €150,000 invested in app development, integration with physiotherapy records, and user engagement features.
• Implementation Costs: €40,000 for training physiotherapists and integrating “Mawendo” into hospital discharge procedures.
• Maintenance and Support: €30,000 for ongoing technical support, system updates, and user assistance over the first year.

The total benefits amount to €380,000 (€200,000 + €100,000 + €80,000), while the total costs are €220,000 (€150,000 + €40,000 + €30,000). This results in a highly positive ROI of approximately 72.7%, indicating that “Mawendo” delivers substantial financial returns, thereby justifying its continued investment and expansion across post-operative care programmes.

Summary of ROI Calculation Process

1. Identify Total Benefits: Quantify all financial and non-financial benefits derived from the DiGA, including cost savings, enhanced treatment adherence, and increased patient productivity.
2. Determine Total Costs: Include all costs associated with the development, implementation, and maintenance of the DiGA.
3. Apply the ROI Formula:

Return on Investment (ROI) = [(Total Benefits – Total Costs) / Total Costs] × 100

1. Analyse the Result:

1. Positive ROI: Indicates that the DiGA is financially beneficial, supporting continued investment and development.
2. Negative ROI: Suggests that costs outweigh benefits, necessitating a review of the DiGA’s effectiveness or cost structure.

Additional Health Economic Considerations

Beyond ROI, other health economic evaluations such as Cost-Effective Analysis (CEA), Budget Impact Analysis (BIA), and Cost-Consequence Modelling (CCM) provide a comprehensive understanding of the financial and health outcomes associated with DiGAs. These analyses help stakeholders make informed decisions regarding the adoption and scaling of digital health applications by offering insights into cost-effectiveness, financial feasibility and the broader impact on healthcare systems.

The Impact on Healthcare Utilisation

DiGAs can significantly influence healthcare utilisation patterns, leading to economic benefits.

• Hospital Admissions: Effective management can lead to fewer stays.
  • Example: “Vivira” may help manage back pain effectively, reducing the need for surgical interventions and prolonged hospitalisation.
• Emergency Visits: Improved symptom control may decrease emergency interventions.
  • Example: A DiGA for asthma management might reduce emergency room visits by enabling better daily control of symptoms.
• Medication Adherence: Enhanced adherence prevents complications, reducing additional treatments.
  • Example: DiGAs like “Companion Patella” may improve adherence to pain management protocols, preventing exacerbations that require intensive treatments.
• Integrated Data Systems: Facilitates better coordination between healthcare providers, leading to more efficient resource use.
  • Example: “Mawendo” integrates with physiotherapy records, ensuring that rehabilitation efforts are aligned and tracked comprehensively.
• Preventive Care: Proactive management can prevent disease progression, further reducing utilisation.
  • Example: A DiGA focused on diabetes management can help maintain blood glucose levels, preventing long-term complications like neuropathy and retinopathy.

Cost-Consequence Modelling (CCM)

CCM provides a comprehensive analysis by listing costs and various outcomes without aggregating them into a single metric like QALYs.

• Advantages:
  • Transparency: Presents all relevant costs and consequences.
  • Flexibility: Allows stakeholders to weigh different outcomes based on their priorities.
• Application: For a DiGA like “HelloBetter”, CCM might list costs associated with app development and deployment alongside outcomes such as improved QoL, reduced hospital admissions and enhanced treatment adherence.

Real-World Examples

Companion Patella: Targets knee and patella pain, showing a 30% reduction in pain scores and a 20% decrease in hospital admissions related to knee surgeries.

Mawendo: Focuses on post-operative rehabilitation, enhancing adherence to protocols by 25% and reducing hospital readmission rates by 15%.

HelloBetter: Addresses chronic pain, improving patient-reported outcomes by 40% and decreasing the need for in-person physiotherapy sessions.

Vivira: Manages back pain through wearable device integration, resulting in a 35% improvement in treatment adherence and a 10% reduction in emergency room visits for back pain exacerbations.

The assessment and measurement of the Positive Healthcare Effect within Germany’s DiGA framework are comprehensive, encompassing both clinical efficacy and healthcare process improvements. Utilising rigorous methodologies such as randomised controlled trials, observational studies and real-world evidence collection ensures that DiGAs deliver meaningful health benefits. Health economic evaluations, including Cost-Effectiveness Analysis (CEA), Budget Impact Analysis (BIA), Return on Investment (ROI), and Cost-Consequence Modelling (CCM), provide critical insights into the financial sustainability and cost-effectiveness of these digital therapeutics.

By adhering to stringent assessment protocols and demonstrating both clinical and economic benefits, DiGAs enhance patient health outcomes and contribute to a more efficient, cost-effective healthcare system. This dual focus ensures that digital health applications are not only clinically impactful but also economically viable, fostering their widespread adoption and integration into routine healthcare practices.