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Diabetes is regarded as a global epidemic with 382 million people globally suffering from diabetes. It also has major implications on patients’ quality of life. There are also high cost of treatment associated with diabetes for both patient and healthcare provider. Telemonitoring represents an excellent technology opportunity to redefine health care delivery. Using technology for home-based care promises the ability to deliver more cost effective care whilst also enhancing quality of care and patient satisfaction. The paper aims to discuss these issues.

The current research aims to contribute to the methodological design of action research projects in their use to implementation health technologies such as telemonitoring. In particular, it seeks create a model which can be used to demonstrate the efficacy of the use of the action research method as a viable alternative to the traditional randomised control trials methodology currently employed in healthcare.

The paper contributes towards the methodological design to investigate the area of practice making use of the telemonitoring programme within a Victorian Health Services Network using action research.

It intends to address the research problem of the low utilisation of telemonitoring within Monash Health as a whole, and more specifically within the diabetes unit. In this context the research intends to utilise the benefits of telemonitoring to improve clinical outcomes of patients by increasing insulin stabilisation. It is also intended the research organisation benefits by increased efficiency by decreasing clinical workforce time spent on managing patient insulin data.

This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.

The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.

Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.

The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.