The Need for Change: Too Many Health Systems are Data Rich, Information Poor (DRIP)


Mike Norton a, Laia Buigues Pastor b

Introduction
Dr. Paul Forte, described the UK NHS as being Data Rich, Information Poor (DRIP) back in 1995. Things have moved on quite a bit since then, but many still feel the ways and means of converting data into information have not kept pace with the exponential increase in our acquisition of data. This picture is complicated by the fact that data structures are often complex and all too often siloed by location, specialty and/or service line.

So how do we turn our data into information and draw actionable insights from that information to improve patient outcomes?

Methods
Solventum Clinical Risk Groups (CRGs) are a clinical categorical methodology for converting clinical data into information, by bringing together clinically coded data (diagnoses, procedures, drug codes, functional and mental health status) from multiple siloes to assign each patient into a single, mutually exclusive category that fully describes their total burden of illness.

When combined with age and gender, CRGs can create clinically homogenous groups that facilitate the step from information to insight and accelerate a healthcare organization's journey to value-based care, specifically by reducing the hurdle of how to identify and measure unwarranted variation across the spectrum of care.

This abstractr looks at the various uses of CRGs by the Health Authority of the Region of Valencia, who initially implementation them in 2008 under the General Pharmaceutical Authority (DGF) and then expanded their use to other bodies belonging to the authority, including the General Health Care Authority.

The Region of Valencia at a glance ...Results
Significant reduction in pharmaceutical spends:
A reduction of 6,5% was achieved in pharmaceutical spend associated with the prescription of medication at pharmacies; this represents a positive impact in financial terms.

Improvement in clinical management:
Health care professionals witnessed an improvement in clinical management resulting in more efficient care focused on the needs of each patient group.

Improvement in financial management and the assignment of human resources:
The identification of areas of greatest demand for medical care, supported the more efficient assignment of human resources and thus optimised operating costs.

Support in decision making:
CRGs aid clinical decision making leading to a better informed and more customised levels of care.

The drafting of key indicators:
CRGs were used to develop key performance indicators, providing a solid foundation for continuous improvements.

Identification of at-risk populations:
Populations with particular pathologies can be more effectively identified for more focussed prescription management, supporting a more proactive management of those populations.

Conclusions
Health systems need to move away from patient management on a case-by-case or disease-by-disease basis and adopt a patient-centric approach to care.

Taking a whole-person, clinical categorical approach to measuring a patient's burden of illness supports the more efficient management of resources, reduces clinical outliers and promotes value-based care.


a Solventum, United Kingdom
b Generalitat Valenciana, Spain

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