An Adaptable Long-term Condition Workload Prediction Model for Primary Health Care

The primary health care (PHC) system must manage the growing demand for care due to patients with long-term conditions (LTCs) such as diabetes, hypertension, and asthma. Population-based care can help address this workload management problem, specifically by enabling a shift from a reactive to a proactive patient management approach. However, current PHC systems lack the ability to provide population-based care. This thesis presents a tool to predict the future workload generated by a population of patients. We use a rule-based system, for its modularity, flexibility and the automated modifiability behaviour, to develop the care pathways as rules that, when given the patient data, would simulate the patient visits for the upcoming year (since some start date). It is assumed that the GPs follow best practice and their patients adhere to their plan-of-care, making visits to the medical practice on their scheduled LTC appointments. Then, these visits are aggregated to a population-level as a count of appointments per week from these LTC patients, referred to as the workload to be managed within the capacity of the practice. Knowing this predicted workload, the PHC organisation can then plan and deliver care accordingly. In this thesis, we also explore using seven what-if scenarios the impacts of alternatives in practices and evaluate the strategies to address them. We then propose the use of Bayesian inference in our workload prediction model, in order to incorporate the variation in patient visits due to the impact of renewal of their LTC prescriptions. This work is done in collaboration with BPAC, a non-profit organisation that promotes best practice for primary care within New Zealand. The collaborations on this work were in the form of 1. health data from a medical practice; 2. the knowledge base to understand the primary health care domain, and the practical issues at a medical practice. Approval for this research using the anonymised patient data provided by BPAC has been given by the University of Otago Human Ethics Committee (Health). We follow the design science research (DSR) methodology to develop our adaptable best practice based workload prediction model for a PHC due to its LTC patients. From a DSR perspective, we developed a construct called the three-layer LTC PHC construct, and a process called the encounter-based unfolding plan-of-care process, which are used to build our artefact: the adaptable best practice based workload prediction model. In DSR, much emphasis is on communicating the developed artefact or model to a wider community and the feedback guides further improvement of the model or the artefact developed. We followed seven iterative cycles to incrementally build the rule base, and the feedback served as a guide to improve the simulation capability of our ABP-WPM. Apart from the feedback from the collaborator on this work, feedback was also collected through informal meetings with the care providers of a medical practice at Mosgiel, the executive members of two different PHOs (one in the North Island and the other in the South Island of New Zealand) at various stages of development of this artefact. The artefact developed was also communicated to the research community through two publications. In the process of developing this population-level workload prediction model, we identified shortcomings (for example, the LTC status of a patient is not explicit) in the current health data models and in the PHC data shared with us, which is needed to support a population-level workload analysis. We, therefore, developed a patient information data model that makes this information explicit

WORKLOAD PREDICTION MODEL OF A PRIMARY HEALTH CENTRE

Managing the growing demand for care due to long-term conditions (LTCs) is a big challenge for primary care providers across the globe. We argue that population-level care for LTC patients registered at a primary health centre (PHC) is possible through workload prediction using care plans. In this paper, we try to answer two research questions: i) How can the future demand for care of the patients with LTCs be predicted? and ii) How is the future demand for care affected by changes? We present a rule-based simulation model that, given the patient details, will predict the number of LTC patients who will be visiting the primary health centre for the next year. Knowing this workload would help the medical practice to meet the upcoming demand for care effectively. Our approach also allows simulation of the effects of changes to practice and resourcing to foresee how these changes may impact the practice. Following the design science research approach, our prediction results have been shared with an expert and the feedback guides us to refine our model

Patient Information Model to Support Population-level Workload Analysis

Evaluation activities in Design Science Research not only verify utility but also scientific rigour and the truth-like value of prescriptive knowledge contributions. Assuming a lack of guidance, evaluation frameworks like the one of Sonnenberg and vom Brocke have been pro- posed prior to evaluating their actual utility to scholars. This research now aims at evaluating how scholars actually apply the framework in practice and their reasoning for doing so. The research-in-progress paper at hand presents preliminary results from a citation analysis. We find an emphasis on ex-post evaluations in artificial settings and a lack of comprehensive detail on ex-ante evaluation activities. The call to accumulate incremental prescriptive knowledge has mostly been ignored and artifact changes are rarely disclosed. Therefore, we question the missing guidance as the sole reason that scholars emphasise building rather than evaluation. We propose to a conduct case study that investigates the reasons behind scholars’ evaluation decisions