The use and role of digital technology in learning health systems: A scoping review.

OBJECTIVE: The review aimed to identify which digital technologies are proposed or used within learning health systems (LHS) and to analyze the extent to which they support learning processes in LHS. MATERIALS AND METHODS: Multiple databases and grey literature were searched with terms related to LHS. Manual searches and backward searches of reference lists were also undertaken. The review considered publications from 2007 to 2022. Records focusing on LHS, referring to one or more digital technologies, and describing how at least one digital technology could be used in LHS were included. RESULTS: 2046 records were screened for inclusion and 154 records were included in the analysis. Twenty categories of digital technology were identified. The two most common ones across records were data recording and processing and electronic health records. Digital technology was primarily leveraged to support data access and aggregation and data analysis, two of the seven recognized learning processes within LHS learning cycles. DISCUSSION: The results of the review show that a wide array of digital technologies is being leveraged to support learning cycles within LHS. Nevertheless, an over-reliance on a narrow set of technologies supporting knowledge discovery, a lack of direct evaluation of digital technologies and ambiguity in technology descriptions are hindering the realization of the LHS vision. CONCLUSION: Future LHS research and initiatives should aim to integrate digital technology to support practice change and impact evaluation. The use of recognized evaluation methods for health information technology and more detailed descriptions of proposed technologies are also recommended.

Type and use of digital technology in learning health systems: a scoping review protocol.

INTRODUCTION: Health systems in North America and Europe have been criticised for their lack of safety, efficiency and effectiveness despite rising healthcare costs. In response, healthcare leaders and researchers have articulated the need to transform current health systems into continuously and rapidly learning health systems (LHSs). While digital technology has been envisioned as providing the transformational power for LHSs by generating timely evidence and supporting best care practices, it remains to be ascertained if it is indeed playing this role in current LHS initiatives. This paper presents a protocol for a scoping review that aims at providing a comprehensive understanding of how and to what extent digital technology is used within LHSs. Results will help to identify gaps in the literature as a means to guide future research on this topic. METHODS AND ANALYSIS: Multiple databases and grey literature will be searched with terms related to learning health systems. Records selection will be done in duplicate by two reviewers applying pre-defined inclusion and exclusion criteria. Data extraction from selected records will be done by two reviewers using a piloted data charting form. Results will be synthesised through a descriptive numerical summary and a mapping of digital technology use onto types of LHSs and phases of learning within LHSs. ETHICS AND DISSEMINATION: Ethical approval is not required for this scoping review. Preliminary results will be shared with stakeholders to account for their perspectives when drawing conclusions. Final results will be disseminated through presentations at relevant conferences and publications in peer-reviewed journals.

Capacity planning and appointment scheduling for new patient oncology consults.

To ensure that patients receive timely access to care, it has become increasingly important to use existing care provider capacity as efficiently as possible and to make informed capacity planning decisions. To support this decision-making process at a regional cancer center in British Columbia (Canada), we undertook a simulation and optimization based study that investigated the simultaneous impact of the available number of new patient consultation slots, appointment scheduling policies and oncologist specialization configurations on the timeliness of patient access to care and physician workload. The key contribution of this paper is the methodological framework it provides to decision makers who manage specialty clinics to ensure that they are using their resources efficiently and making informed strategic short- and mid-term capacity planning decisions for new patient demand.

A Quantitative Analysis of the Relationship Between Radiation Therapy Use and Travel Time.

PURPOSE: To model and quantify the relationship between radiation therapy (RT) use and travel time to RT services. METHODS AND MATERIALS: Population-based registries and databases were used to identify both incident cancer patient and patients receiving RT within 1 year of diagnosis (RT1y) in British Columbia, Canada, between 1992 and 2011. The effects of age, gender, diagnosis year, income, prevailing wait time, and travel duration for RT on RT1y were assessed. Significant factors from univariate analyses were included in a multivariable logistic regression model. The shape of the travel time-RT1y curve was represented by generalized additive and segmented regression models. Analyses were conducted for breast, lung, and genitourinary cancer separately and for all cancer sites combined. RESULTS: After adjustment for age, gender, diagnosis year, income, and prevailing wait times, increasing travel time to the closest RT facility had a negative impact RT1y. The shape of the travel time-RT1y curve varied with cancer type. For breast cancer, the odds of RT1y were constant for the first 2 driving hours and decreased at 17% per hour thereafter. For lung cancer, the odds of RT1y decreased by 16% after 20 minutes and then decreased at 6% per hour. Genitourinary cancer RT1y was relatively independent of travel time. For all cancer sites combined, the odds of RT1y were constant within the first 2 driving hours and decreased at 7% per hour thereafter. CONCLUSIONS: Travel time to receive RT has a different impact on RT1y for different tumor sites. The results provide evidence-based insights for the configuration of catchment areas for new and existing cancer centers providing RT.

The use of discrete-event simulation modelling to improve radiation therapy planning processes.

BACKGROUND AND PURPOSE: The planning portion of the radiation therapy treatment process at the British Columbia Cancer Agency is efficient but nevertheless contains room for improvement. The purpose of this study is to show how a discrete-event simulation (DES) model can be used to represent this complex process and to suggest improvements that may reduce the planning time and ultimately reduce overall waiting times. MATERIALS AND METHODS: A simulation model of the radiation therapy (RT) planning process was constructed using the Arena simulation software, representing the complexities of the system. Several types of inputs feed into the model; these inputs come from historical data, a staff survey, and interviews with planners. RESULTS: The simulation model was validated against historical data and then used to test various scenarios to identify and quantify potential improvements to the RT planning process. CONCLUSIONS: Simulation modelling is an attractive tool for describing complex systems, and can be used to identify improvements to the processes involved. It is possible to use this technique in the area of radiation therapy planning with the intent of reducing process times and subsequent delays for patient treatment. In this particular system, reducing the variability and length of oncologist-related delays contributes most to improving the planning time.