Better value digital health: the medium, the market and the role of openness

Effective resource management using machine learning in medicine: an applied example

Background

The field of medicine is rapidly becoming digitised, and in the process passively amassing large volumes of healthcare data. Machine learning and data analytics are advancing rapidly, but these have been slow to be taken up in the day-to-day delivery of healthcare. We present an application of machine learning to optimise a laboratory testing programme as an example of benefiting from these tools.

Methods

Canterbury District Health Board has recently implemented a system for urgent lab sample processing in the community, reducing unnecessary emergency presentations to hospital. Samples are transported from primary care facilities to a central laboratory. To improve the efficiency of this service, our team built a prototype transport scheduling platform using machine learning techniques and simulated the efficiency and cost impact of the platform using historical data.

Results

Our simulation demonstrated procedural efficiency and potential for annual savings between 5% and 14% from implementing a real-time lab sample transport scheduling platform. Advantages included providing a forward job list to the laboratory, an expected time to result and a streamlined transport request process.

Conclusion

There are a range of opportunities in healthcare to use large datasets for improved delivery of care. We have described an applied example of using machine learning techniques to improve the efficiency of community patient lab sample processing at scale. This is with a view to demonstrating practical avenues for collaboration between clinicians and machine learning engineers.

Free/Libre open source software in health care: a review

Objectives

To assess the current state of the art and the contribution of Free/Libre Open Source Software in health care (FLOSS-HC).

Methods

The review is based on a narrative review of the scientific literature as well as sources in the context of FLOSS-HC available through the Internet. All relevant available sources have been integrated into the MedFLOSS database and are freely available to the community.

Results

The literature review reveals that publications about FLOSS-HC are scarce. The largest part of information about FLOSS-HC is available on dedicated websites and not in the academic literature. There are currently FLOSS alternatives available for nearly every specialty in health care. Maturity and quality varies considerably and there is little information available on the percentage of systems that are actually used in health care delivery.

Conclusions

The global impact of FLOSS-HC is still very limited and no figures on the penetration and usage of FLOSS-HC are available. However, there has been a considerable growth in the last 5 to 10 years. While there where only few systems available a decade ago, in the meantime many systems got available (e.g., more than 300 in the MedFLOSS database). While FLOSS concepts play an important role in most IT related sectors (e.g., telecommunications, embedded devices) the healthcare industry is lagging behind this trend.