Article type
Year
Abstract
Background: Personalized care plans are needed to optimize care for people who could benefit from multiple interventions.
Objectives: Show the feasibility of precision care via estimation of potential to benefit by calculating absolute risk reductions in a population and rating the importance of outcomes together with the patient when making personalized care plans.
Methods:
1. Interventions that could benefit patients (care gap) are identified via clinical decision support (CDS) rules analyzing individual patient data.
2. Absolute baseline risks (BR) of outcomes are estimated with risk calculators or by applying representative baseline risks from studies.
3. Summary of findings (SOF) tables are used to present the relative risk reductions (RRR) by various interventions.
4. Absolute risk reductions (ARR) are calculated: ARR = RRR x BR for each intervention and outcome.
5. Representative rating of importance of outcomes (IO) is used.
6. The individual’s potential to benefit (PTB) from each intervention is calculated: PTB = IO x ARR.
7. People are sorted by total PTB (sum of PTBs for different interventions) to find people who could benefit most and for whom a personalized care plan should be suggested.
8. People can individualize the IO ratings in a shared decision-making (SDM) process where decision aids based on the SOF tables can be used.
9. The interventions prioritized by the patients are included in personalized care plans.
10. Extracting interventions from care plans of all people helps to determine the need of interventions in the whole population.
11. When costs of interventions are known, the individualized cost-effectiveness (cost/PTB) of interventions can be used as a basis of coverage decisions and resource allocation in planning care provision for the population.
Results: Structured SOF tables from Cochrane Reviews and recommendations in the form of CDS rules enable evidence-based personalized care for individuals and for populations. The implications for authoring of SOF tables are discussed.
Conclusions: SDM, personalized medicine and population health can be combined by using SOF tables and CDS tools.
Objectives: Show the feasibility of precision care via estimation of potential to benefit by calculating absolute risk reductions in a population and rating the importance of outcomes together with the patient when making personalized care plans.
Methods:
1. Interventions that could benefit patients (care gap) are identified via clinical decision support (CDS) rules analyzing individual patient data.
2. Absolute baseline risks (BR) of outcomes are estimated with risk calculators or by applying representative baseline risks from studies.
3. Summary of findings (SOF) tables are used to present the relative risk reductions (RRR) by various interventions.
4. Absolute risk reductions (ARR) are calculated: ARR = RRR x BR for each intervention and outcome.
5. Representative rating of importance of outcomes (IO) is used.
6. The individual’s potential to benefit (PTB) from each intervention is calculated: PTB = IO x ARR.
7. People are sorted by total PTB (sum of PTBs for different interventions) to find people who could benefit most and for whom a personalized care plan should be suggested.
8. People can individualize the IO ratings in a shared decision-making (SDM) process where decision aids based on the SOF tables can be used.
9. The interventions prioritized by the patients are included in personalized care plans.
10. Extracting interventions from care plans of all people helps to determine the need of interventions in the whole population.
11. When costs of interventions are known, the individualized cost-effectiveness (cost/PTB) of interventions can be used as a basis of coverage decisions and resource allocation in planning care provision for the population.
Results: Structured SOF tables from Cochrane Reviews and recommendations in the form of CDS rules enable evidence-based personalized care for individuals and for populations. The implications for authoring of SOF tables are discussed.
Conclusions: SDM, personalized medicine and population health can be combined by using SOF tables and CDS tools.