On the Annotation of Health Care Pathways to Allow the Application of Care-Plans That Generate Data for Multiple Purposes

Building digital patient pathways for the management and treatment of multiple sclerosis

Recent advances in the field of artificial intelligence (AI) could yield new insights into the potential causes of multiple sclerosis (MS) and factors influencing its course as the use of AI opens new possibilities regarding the interpretation and use of big data from not only a cross-sectional, but also a longitudinal perspective. For each patient with MS, there is a vast amount of multimodal data being accumulated over time. But for the application of AI and related technologies, these data need to be available in a machine-readable format and need to be collected in a standardized and structured manner. Through the use of mobile electronic devices and the internet it has also become possible to provide healthcare services from remote and collect information on a patient's state of health outside of regular check-ups on site. Against this background, we argue that the concept of pathways in healthcare now could be applied to structure the collection of information across multiple devices and stakeholders in the virtual sphere, enabling us to exploit the full potential of AI technology by e.g., building digital twins. By going digital and using pathways, we can virtually link patients and their caregivers. Stakeholders then could rely on digital pathways for evidence-based guidance in the sequence of procedures and selection of therapy options based on advanced analytics supported by AI as well as for communication and education purposes. As far as we aware of, however, pathway modelling with respect to MS management and treatment has not been thoroughly investigated yet and still needs to be discussed. In this paper, we thus present our ideas for a modular-integrative framework for the development of digital patient pathways for MS treatment.

Using Existing Clinical Information Models for Dutch Quality Registries to Reuse Data and Follow COUMT Paradigm

BACKGROUND

 Reuse of health care data for various purposes, such as the care process, for quality measurement, research, and finance, will become increasingly important in the future; therefore, "Collect Once Use Many Times" (COUMT). Clinical information models (CIMs) can be used for content standardization. Data collection for national quality registries (NQRs) often requires manual data entry or batch processing. Preferably, NQRs collect required data by extracting data recorded during the health care process and stored in the electronic health record.

OBJECTIVES

 The first objective of this study was to analyze the level of coverage of data elements in NQRs with developed Dutch CIMs (DCIMs). The second objective was to analyze the most predominant DCIMs, both in terms of the coverage of data elements as well as in their prevalence across existing NQRs.

METHODS

 For the first objective, a mapping method was used which consisted of six steps, ranging from a description of the clinical pathway to a detailed mapping of data elements. For the second objective, the total number of data elements that matched with a specific DCIM was counted and divided by the total number of evaluated data elements.

RESULTS

 An average of 83.0% (standard deviation: 11.8%) of data elements in studied NQRs could be mapped to existing DCIMs . In total, 5 out of 100 DCIMs were needed to map 48.6% of the data elements.

CONCLUSION

 This study substantiates the potential of using existing DCIMs for data collection in Dutch NQRs and gives direction to further implementation of DCIMs. The developed method is applicable to other domains. For NQRs, implementation should start with the five DCIMs that are most prevalently used in the NQRs. Furthermore, a national agreement on the leading principle of COUMT for the use and implementation for DCIMs and (inter)national code lists is needed.