HARNESSING DATA SCIENCE IN HOMECARE TO ANTICIPATE CARE FOR “PERSONS WITH AMYOTROPHIC LATERAL SCLEROSIS” (PALS)
Abstract
BACKGROUND: In response to a global focus on sustainable healthcare, less is known about the role of palliative care for “persons with ALS” (PALS) across the disease trajectory. With one’s home, the preferred place to manage complex disease conditions and plan end-of-life care by palliative care patients/caregivers, it is important to identify risk factors that may signal early institutionalization. With the proliferation of electronic health records (EHRs), the intensity of care requirements associated with fatal neurological conditions (ALS) is becoming more visible. Data science originating from the fields of computer science, statistics and information science, an emerging field within nursing research is creating new avenues to discover knowledge about patient experiences previously unanswerable using traditional methods. An understanding of home healthcare experiences for PALS will provide opportunities to re-design sustainable, health service delivery programs that align with payor mandates and meet societal expectations.
AIM: In Canada where nursing data science research is less common, this study applied data science methods to homecare EHRs to explore factors as a means of determining PALS’ capacities and preferences to remain cared for at home.
METHOD: A nursing informatics framework (data, information, knowledge, wisdom [DIKW]) integrated with a data science methodology, (knowledge discovery in databases [KDD]) was employed in a retrospective analysis of an ALS homecare EHR dataset. Logistic regression modelling was used to evaluate risks for PALS’ institutionalization.
RESULTS: Five significant logistic regression models were generated. The final model, with six variables, offered the greatest clinical utility. Four risk factors characterized ALS clinical manifestations of disease decline, and two were electronic assessment algorithm-based outcome scales, recognized as powerful predictors of PALS’ institutionalization and informal caregivers’ burden.
CONCLUSION: Study findings highlight the utility of applying big data science to EHR datasets to introduce new knowledge into homecare practice settings. Homecare providers who understand electronic assessment by-products (factors) associated with clinical documentation practices will be well positioned to be proactive and mitigate institutionalization risks of PALS under their care. This study's data science approach fosters a new path in Canadian homecare research to advance health service delivery, education, and policy.