Healthcare going digital was one of the biggest developments of 2020. Not that technology didn’t find its way in the sector before, but the magnitude suddenly amplified with the pandemic at hand.
Even after acknowledging the need of digitization and its role in the medical industry, clinicians are still quite sceptical about adopting data science in their day to day functioning. This resistance is far more in healthcare than any other sect. Irrespective of availability of state-of-the-art analytics solutions, not many of them have been put to use by clinicians.
A University of Virginia held health leaders responsible for establishing a culture that promoted data driven decision making by clinicians. Herein, data science was said to guide clinicians through the process of finding scope for improvement, formulating and implementing changes, and accessing results.
A senior data scientist pondered upon the challenges in wider adoption. As per his understanding, lack of interest came to be one of the possible causes. She elaborated this insight by suggesting that clinicians become fundamentally difficult to work with because of lack of interest in data science.
The director of data science at University of Virginia alongside one more doctor will address this issue at HIMSS21 next month. She suggested that the main hindrances in the path of analytics solution adoption are lack of trust and understanding of the same. In the similar vein, she added that introducing advanced analytics becomes an issue pertaining to varying levels of data literacy.
To find a way through this discrepancy, she suggested that opportunities with respect to clinicians adopting data science depend on analytics maturity and executive leadership support at the institution. From her understanding of the subject, she remarked that to excel in extremely clinical topics, it is important to have strong clinical support in place.
Although the importance of clinical topics are felt by medical institutions, it may not be possible to act upon the same readily. Contrary to this, topics like LOS and readmissions, for example, are quite crucial for clinicians but not entirely clinical.
To get into the trustworthy front with a clinician, it is advisable to progress from less clinical topics to more as they are quite likely to take suggestions from data scientists in less-clinical areas.
SoftGrid Computers operates on a similar model of establishing rapport by dealing with less-clinical domains initially. For instance, engaging them in data analysis about their patients and so on leads to data-driven decision making. Not only does such engagement help build trust, but also bring to surface the exact needs and wants of clinicians, which may have been latent earlier. That makes it easier to get clinicians on board.