Why Data Science is Integral to the Clinical and Healthcare Fields

In order to reap the benefits of data, it calls for evolving an organization-wide data science strategy. What with many other segments such as banking has adopted Data Science, health care is an exception. With the absence of a data science strategy, healthcare firms find it difficult to handle the increasing volume of data. Also, individual clinicians find it cumbersome to improve the safety, quality and efficiency of the care they provide. An effective data science strategy for health care organizations is, therefore, the need of the hour, says, a catalyst.nejm.org report. Data Science has five key components.

Repository

A secure entity-wide data repository allows it to keep a complete inventory of their data assets, though planning a repository presents humongous challenges in the form of scoping the existing data, creating meta-data, finding ways to combine data sources and develop strategies to figure out what data is produced, stored, used and reused and how and by whom, notes the report.

Integration

Again, bringing different data sets together in itself is a major challenge in terms of reconciling formats as well as breaking down siloes, says the report. Development and consistent use of an enterprise master patient index (EMPI) allows linkage of disparate data sources on individual patients but requires significant organizational and process changes to be achieved across information systems, including the elimination of duplicate records and establishing new procedures surrounding the addition of new patients.

If creating an EMPI for initial data collection poses too many challenges, either administrative or technical, a firm may achieve a reasonable equivalent by using a “data lake,” a technology platform that allows linking of highly disparate data, which keep source data in its original state for analysis if needed but also allow organizations to navigate across different sources and explore new relationships among them.

The data lake is fed with real-time data from its electronic health record as well as an enterprise resource management system and several other sources. This combination of data from disparate sources pulls together patient-specific information across a range of operational and clinical issues. This information can be fed back in real-time to clinicians at the bedside and can also be used for operational and strategic planning and overall quality analysis.

Security

Protecting privacy and anonymity are always important and that task becomes more complex when an organization uses a patient’s data for purposes that go beyond immediate patient care. This is particularly crucial given that some health systems, including BIDMC, are moving to the use of private space on public clouds. Firms need to create data governance frameworks to ensure those protections and spend money to cyber-security measures.

Support

Entities need teams with a slew of skills in data processing and cleaning, statistics, computer science, visualization, operational research and change management, artificial intelligence and archiving/curation, says the report.

“Boundary spanners” who can establish links among data science staff, the firm’s management and its clinicians, they can also identify data query priorities that are both organizationally and clinically relevant and can help users of data understand the full range of analysis that is available to them (such as near real-time queries regarding particular patient populations, medications, or treatment outcomes).

Feedback

An effective data science strategy is dependent on not only on well-structured databases and advanced analytics but also on having solid underlying data. Predictive analytics can be extremely valuable but require high-quality data for reliable insights, notes the report. Strategic approaches to analysis should create a virtuous cycle in which data are repeatedly scrutinized as they are reused for different purposes, driving improvements in data quality, it said.

Such work should harness innovative analytical tools that employ artificial intelligence approaches such as machine and deep learning, and a complete service redesign may be required in which insights from data can inform important organizational and service delivery decisions in real time. To achieve this level of effectiveness, frontline staff may need to change how they work in order to incorporate these insights and act on them at the point of care, according to the report.

Stating that the implementation of a data science strategy represents one of the cornerstones of better care, as well as greater operational efficiency and, eventually, more effective approaches to population health, the report concluded, and “Our healthcare system will increasingly depend on data to improve care, reduce costs and expand access.”

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