Article type
Year
Abstract
Background: Learning Health Systems (LHSs) and personalized medicine have fundamental synergy. Both advocate for the same underlying processes: to drive health system and practice improvement. These processes include the use of real-time data for clinical and patient decision making, cycles of iterative learning by communities of practice, and continuous development of the evidence base via a dynamic flow of knowledge between research and practice. We have studied three personalized medicine models within various health conditions. These models are at different stages of development and implementation which has provided us with a unique opportunity to: 1) investigate how potential LHSs emerge and evolve, and 2) identify the different stages of LHS development.
Methods: Qualitative interview data was collected on the implementation of 3 models of precision medicine: intellectual disability (ID) genomics, neurofibromatosis (NF) genetic integrated care, and renal genomics. Participants included: clinical geneticists, genetic counselors, disease specialists, consumer representatives, medical education specialists, and health system management staff (n=52). 1) Inductive thematic analysis was used to identify factors that give rise to the development of LHSs and then the results interpreted using complex systems theory to explore LHS emergence and evolution. 2) To assess which LHS features were detected within each model, and which features were absent or in development, a qualitative interview coding guide was developed to code for observable features of LHSs as defined by the Institute of Medicine (IoM 2013).
Results: The ID genomics model was classified as being in the exploration stage- here the need for a learning community with access to real-time knowledge was identified. However, the model requires both the design and implementation of a digital data-sharing platform connecting across the community. The NF integrated care model was assessed as being in the preparation stage, where the human and digital infrastructure has been established, and the future focus is on the improvement of implementation and scale-up of the LHS. The renal genomics model was assessed as the most advanced LHS model. Here the human and digital infrastructure has been implemented and scaled to a national network consisting of basic laboratory research teams, multidisciplinary clinical research, which are directly integrated with clinical care. Patient data is being used in real-time to lead clinical decisions. The future focus for the renal genomics model is sustainability and to increase the use of patient experience data. A comparison of the 3 models shows that each LHSs emerged from frontline clinicians who recognized the need for a continuous learning community and accompanying digital infrastructure to support real-time access to knowledge.
Conclusions: At a given time LHSs exhibit varying features and evolve at different rates, however they all begin with the human infrastructure as the foundation.
Consumer representatives included in all 3 studies.
Methods: Qualitative interview data was collected on the implementation of 3 models of precision medicine: intellectual disability (ID) genomics, neurofibromatosis (NF) genetic integrated care, and renal genomics. Participants included: clinical geneticists, genetic counselors, disease specialists, consumer representatives, medical education specialists, and health system management staff (n=52). 1) Inductive thematic analysis was used to identify factors that give rise to the development of LHSs and then the results interpreted using complex systems theory to explore LHS emergence and evolution. 2) To assess which LHS features were detected within each model, and which features were absent or in development, a qualitative interview coding guide was developed to code for observable features of LHSs as defined by the Institute of Medicine (IoM 2013).
Results: The ID genomics model was classified as being in the exploration stage- here the need for a learning community with access to real-time knowledge was identified. However, the model requires both the design and implementation of a digital data-sharing platform connecting across the community. The NF integrated care model was assessed as being in the preparation stage, where the human and digital infrastructure has been established, and the future focus is on the improvement of implementation and scale-up of the LHS. The renal genomics model was assessed as the most advanced LHS model. Here the human and digital infrastructure has been implemented and scaled to a national network consisting of basic laboratory research teams, multidisciplinary clinical research, which are directly integrated with clinical care. Patient data is being used in real-time to lead clinical decisions. The future focus for the renal genomics model is sustainability and to increase the use of patient experience data. A comparison of the 3 models shows that each LHSs emerged from frontline clinicians who recognized the need for a continuous learning community and accompanying digital infrastructure to support real-time access to knowledge.
Conclusions: At a given time LHSs exhibit varying features and evolve at different rates, however they all begin with the human infrastructure as the foundation.
Consumer representatives included in all 3 studies.