Driving with Data

Survival of a healthcare organisation in today's dynamic environment requires data-driven management

By: Dr. AK Khandelwal

The healthcare industry in India is passing through a highly volatile, complex, competitive environment fueled with disruptive technology. Survival of a healthcare organisation in such a dynamic environment requires data-driven management.
In a rapidly-changing environment, organisations need innovations to optimise their operations.
Healthcare industry leaders strongly believe that data driven management shall be the main driver for innovation, productivity and success of an organisation in the present environment.
These days, payers are experimenting with bundled payments and hospitals will face increasing challenges in providing better care at a lower cost. As payers are trying to find ways to constrain payments, hospitals must contend with the steadily rising cost of pharmaceuticals, supplies, medical technology, and personnel. As payment reform gathers steam, this strategy will become increasingly difficult to pursue. To improve quality and efficiency while constraining costs, some hospitals have adopted quality improvement methodologies from industries outside of healthcare, like data driven management.
Experts opine that healthcare providers who make investments in analytics, including partnering with analytics- focused firms, will end up being the winners.

Data analytic management is the analysis of data available from clinical, financial and operational process occurring in the healthcare organisation to identify: what has happened? what can happen? what can be done?

What is data analytic management?

Data analytic management is the analysis of data available from clinical, financial and operational process occurring in the healthcare organisation to identify: what has happened? what can happen? what can be done?
Literature mentions health analytics is "the systematic use of health data and related business insights developed through applied analytical disciplines (e.g. statistical, contextual, quantitative, predictive, cognitive, other models) to drive fact-based decision making for planning, management, measurement and learning".

The types

Data analysis takes place on three levels:
Descriptive Analytics: This analytic uses data analysis to provide information of the past and answers: "What has happened?"
It provides information like average investigations per day, average admission per day, average discharge per day, average revenue per day etc. Needless to say, the vast majority of statistics we use fall into this category.
The advantage of descriptive analytics is that it helps us to learn from past sales, services, cost, and their influence on future outcomes.
Predictive Analytics: This method uses statistical model and forecast technique to predict future needs. So that we can use data to predict how many patients will be seen in the outpatient department on a given day or time of day, what percentage of these patients shall require admission, and how many patients shall undergo surgery. This information is very useful for both providers and payors.
Prescriptive Analytics: This method provides direction on what actions to take. It is a fact that prescriptive analytics are relatively complex to administer, and most healthcare organisations are not yet using them in their daily course of operation. However, if implemented correctly, they can have a significant impact on decision making and improve the bottom line of the organisation.

Challenges in implementation

Literature reveals that with years of relying on gut feelings and experience, hospital administrators are adopting data driven management very reluctantly. According to one study, only 4% of organisations rely on data driven analytics. Healthcare organisations utilising data driven management can be divided in to three categories according to their capabilities:

Steps to becoming a data-driven healthcare organisation

Healthcare organisations should ensure commitment of all stakeholders for considering data as a strategic asset, and integrating data as a part of their culture. They should implement an understanding of the complete flow of data and utilising data-driven insights. Leaders should support the integration and development of data analytics and build organisation culture encouraging and appreciating sharing of data and insights.
Companies should invest in adoption of new technologies and systems to ensure continuous quality improvement of data driven management. Although most organisations will take an incremental approach to becoming data driven, all should begin the process by creating an information strategy and roadmap and by putting in place an analytics platform and data governance policies that include the following 5-S steps.

1. Sort out the data sources: Healthcare organisations collect data from many individuals at many places. This complexity results in poor quality data. It is recommended that organisations should first sort out its data sources and its collection mechanism.
2. Set data quality metrics and assess and improve the quality of proposed sources: Wide range of data are collected in a healthcare organisation. These are often not standarised. It is essential that quality analysis and corrective action must exist for all new and established data sources. Assessments should compare data sources to established data quality targets and track improvements. After addressing quality issues, data must be normalised to standardise formats, structure. Because normalised data will be used for many types of initiatives, organisations may want to master data types, such as location, providers and patients, and make them available to all data sources. Use of a common healthcare data model for most organisations will be more useful.
3. Streamline data integration: After an organisation understands how it plans to use and analyse different data sources, it can determine the best platform for integrating its data. Literature mentions that several factors like whether data is structured or unstructured, streaming or stored historically, reports or exploratory analysis will be required, will decide the platform choice. Structured data is available from patients' history notes and unstructured data is available from emails, doctors' notes, test reports etc.
4. Search analytic need: Understanding the analytics requirement will help an organisation to define priorities and determine which visualisation and statistics are best suited to the task.
5. Secure and manage the data lifecycle: Healthcare organisations should ensure that robust security measures are in place to protect their data, associated hardware and software from both internal and external risks. Organisations should ensure that at the time of installation of system, appropriate decisions are taken about retention, cost-effectiveness, reuse and auditing of historical or new data.

Summing up

Though data driven management is in the nascent stage but with increased use of electronic medical records by healthcare providers and availability of skilled IT personnel, it will provide immense opportunity to improve our healthcare ecosystem.

About the author

Dr. Ashok Kumar Khandelwal is the Medical Director, Anandaloke Hospital & Neurosciences Centre, West Bengal. He is a trained Assessor from the National Accreditation Board for Hospital and Health Care Provider (NABH). He carries around two decades of experience in the hospital industry and 15 years of experience as a hospital administrator.