Healthcare industry is revolutionizing at every step to improve patient care. On one side a constant rise in chronic diseases and on other side high consumer (patient) expectations, both are compelling the healthcare organizations to evolve and turn to data and analytics to improve service and reduce costs. Due to digitalization and increasing access to social media, healthcare patient management system is now compelled to follow an outcomes-based or accountable-care approach that not only requires accurate storage of patient data but also timely and easy access to track results.
Data is part of many internal and external locations, traditional and non-traditional sources in a healthcare organization like patient, staff and population profiles, electronic medical records (EMRs), public health records etc. as well as financial, clinical and operational processes. This data is valuable but collecting, integrating and analysing this data can be a complex task.
Application of Clinical and Advanced Analytics in Healthcare Organizations
Application of clinical and advanced analytics to healthcare data provides better insight into risk, outcomes, resources, referrals, performance and readmissions. Clinical analytics performs retrospective analysis of the clinical, financial and operational data. It evaluates the effectiveness of the various programs and performance of the healthcare organization by answering following:
- Major health indicators across patient population
- Provider quality score
- Total cost of care
- Resource utilization
Advanced analytics predicts the at-risk patients by its predictive and forward looking nature by answering following:
- Indicators for readmission
- High-risk patients for a bad outcome
- Helping patients in making better treatment choices
- Best patient-specific treatment program
How can a Healthcare Organization become data-driven?
It is definitely not a one day task for any healthcare organization to become data-driven. First of all the organization must start considering “Data as a strategic asset”. Moreover it requires planning and cultural and technological transformation to create a data-driven healthcare organization. The transformation begins by creation of an information strategy and roadmap.
This would be followed by appropriate placing of an analytics platform and data governance policies that include the following five steps:
>Identify all traditional and non-traditional data sources: Any current or future potential data needs to be identified including structured and unstructured data from sources like clinical, operational and financial systems, data from monitoring and sensing devices, and data from social media or public health records etc. At this level, foundation for analytical initiatives is laid by doing following analysis of the data:
- Learning points from each data source
- Either combination or single analysis of data from different sources will give better insights
- Identification of understated data that should be augmented with new sources
>Assessment of data quality levels and setting of data quality metrics: Data sources need careful examination by comparing it with established data quality targets in order to assess the quality of data, track improvement and find out corrective actions, if required. All the data should be normalized to standard formats, structure and quality as it will be used for many types of initiatives to master data types.
>Integration of data sources: Best platform for integration of data is determined depending upon factors like whether:
- data is structured, unstructured or both
- data is streaming or stored historically
- reports or exploratory analysis is required etc.
>Identification of need for analytics: It is very important to understand why analytics is required. Understanding it will define priorities and determine the best suitable visualization and statistics.
>Security and management of data life cycle: The Big data must be secured and protected. The data life cycle plan need to be established at the inception only to ensure that appropriate decisions are made about retention, cost-effectiveness, reuse and auditing of historical or new data.
Outcomes of Data Analytics in Healthcare Organizations
This analysis enables the organization to inform decision-making processes and take prescriptive action. In addition, healthcare organizations will be able to:
- Identify factors to reduce the growth of chronic diseases and advancement of diseases
- Improve healthcare for aging populations
- Meet consumer (patient) expectations
- Facilitate personalized care
- Achieve more insight into patient outcomes and care
- Meet requirements for outcomes-based or accountable-care payment models
- Transform the way healthcare is delivered