Analytics and Organizational Structures in the Healthcare Industry

Having an organizational structure is important and inescapable for every form of organization in every industry no matter how small. Even in a two-person organization, there exists a form of structure. The most common organizational structure for healthcare organizations is a functional organizational structure whose key characteristic is a pyramid-shaped hierarchy, which defines the functions carried out and the key management positions assigned to those functions.

Just as the healthcare organization needs an organizational structure, so does the data available in the healthcare organization. The structure of this data is commonly known as healthcare analytics. The Use of Analytics in the Healthcare Industry involves the activities that are undertaken or actions carried out as a result of data collected from different areas within healthcare. Healthcare analytics helps in simplifying and fine-tuning the process of data collection and mining. Data mining is very important in the healthcare industry as it enables health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce cost. So data mining and healthcare analytics work hand in hand. Some medical and research experts in the healthcare industry believe the opportunities to improve care and reduce costs concurrently could apply to as much as 30% of overall healthcare spending.  This could be a win/win overall for the entire industry. However, due to the complexity of healthcare and a slower rate of technology adoption, the healthcare industry lags behind other industries in implementing effective data mining and analytic strategies. In the healthcare industry, data mining has for the most part, been just an academic exercise with only a few pragmatic and real life success stories, however, with the proper use of analytics, this can be improved upon.

Healthcare Analytics focuses on the examination of patterns among various healthcare data and more importantly, Big Data. A proper analysis of this big data provides healthcare researchers and practitioners with a comprehensive clinical, financial, fraud, HR and supply chain analysis.

The Healthcare Analytics Adoption Model provides a framework for evaluating the industry’s adoption analytics, a roadmap for organizations to measure their own progress toward analytics adoption and finally it also provides a framework for evaluating vendor products. The model has eight levels and they are:

Level 0 – Fragmented Point Solutions
Level 1 – Enterprise Data Warehouse
Level 2 – Standardized Vocabulary & Patient Registries
Level 3 – Automated Internal Reporting
Level 4 – Automated External Reporting
Level 5 – Waste & Care Variability Reduction
Level 6 – Population Health Management and Suggestive Analytics
Level 7 – Clinical Risk Intervention & Predictive Analytics
Level 8 – Personalized Medicine & Prescriptive Analytics

The first three levels explained:

Level 0 – Fragmented Point Solutions

The level (0) of the Analytics Adoption Model, focuses on areas with limited analytics capabilities such as finance, acute care nursing, pharmacy, laboratory and physician productivity. This fragmented point solutions and the knowledge it generates is isolated in order to optimize sub-processes at the expense of enterprise-wide processes. Reports tend to be labor-intensive and inconsistent. There is no formal data governance function tasked with maximizing the quality and value of data in the organization.

Level 1 – Enterprise Data Warehouse

This involves collecting and integrating the core data content. When the core transaction systems are integrated into the data warehouse then level 1 has been satisfied. This data could include things like patient financial data, materials and supplies data, clinical data, patient experience data and insurance claims data.

Level 2 – Standardized Vocabulary & Patient Registries

This involves relating and organizing the core data content. At level 2, reference data and master vocabularies are defined and made available. This data includes patient identity, physician identity, procedure codes, diagnosis codes, facility codes, department codes etc.