Dr Sanjeev Sood
Hospital and Health Systems Administrator,
SMC, Air Force Station
Smarter decisions always yield a better outcome. The article gives a picture where leveraging data analytics in healthcare has now become obvious and therefore, aims at ensuring business intellegence for the sector in near future
It is said that as a general rule, the most successful organisation today is the one with the best information and knowledge. In recent years, breakthroughs in data-capturing technologies, data standards, data warehousing and data mining, health management information systems (HMIS) and modelling and optimisation sciences have created opportunities for large-scale analytics programs. Several health care organisations in the private sector have not only leveraged fact based decision making, but also created sustained competitive advantage from data-based analytics. They have their business strategies at least in part-around their analytical capabilities.
In a recently concluded 6th International eHealth Conference in Hyderabad, organised by CSDMS and ELETS, importance of quality data management, data warehousing, data mining and analytics was repeatedly emphasised by various speakers during the sessions, ‘Transform, Perform and Reform-Charting blueprint for the future of health care’ and ‘Hospital CIOs’ Conclave. IBM Centre for The Business for Government had also set up an exhibition stall on ‘Strategic use of Analysis’.
What is behind the name?
Data Analytics can be defined as the science of extensive use of data, statistical and quantitative analysis, explanatoryand predictive models, and fact-based management to drive decisions andactions. Analytics is a subset of what has come to be called business intelligence: a set of technologies and processes that use data to understand and analyse business performance.
In health care organisations, managers and physicians often base their most decisions on experience, intuition, unreasoned assumptions or memory rather than on scientific evidence and analytics.
Analytics and fact based decision making can make just as much or even more of a powerful contribution to the achievements of governmental missions as they can to the accomplishment of corporate business objectives.
Role in healthcare
The use of analytics is also gaining popularity due to availability of several software such as Minitab, SPSS and Epi_Info and statistical tools in spreadsheets, to more complex business intelligence suites, predictive applicators and the reporting and analytic models of major enterprise systems.
So far, the health information and analytics have been extensively used in healthcare to measure health status of the population, to assess their health problems, for making comparisons for health status, for planning and administration of quality health services and for carrying out scientific research. More recently, the data has been increasingly used by health care organisations as a part of Business Intelligence, to make strategic decisions and choices, and to gain competitive advantage in market. Today, analytic strategy is viewed as a key engine of a dynamic capability of an organisation.
Most Indian Health Care Organisations are yet to embark on analytics journey or are still in early stages of it. The Indian health care organisations need to generate and compile good quality data by structured and reliable reports and returns from multiple sources. This data needs to be transformed into intelligence to guide decision and policy makers, administrators and health care personnel. Certain hospitals, which have moved to EMRs, have already begun to deep archive their data into warehouses, so as to subsequently use it for research, mining and subject it to analytics to make smarter decisions and improve quality of care.
As of now, availability of quality data on morbidity patterns and patient safety are grossly inadequate in India, so as to design innovative health insurance products for population and institutionalise effective patient safety programmes in hospitals. We have been drawing inferences from US data and applying to our health problems. Currently, most Indian HCOs are data poor; some are data rich, but information poor; very few could be data and information rich.
Data: The data should be discrete, granular, reliable, and clean and standardised across the health care organisations.
Enterprise: An enterprise approach to analytics implies that organisations work across functions in a unified manner rather than fragmented nature of information held in disparate silos.
Leadership: The leadership should be committed to use analytic tools and techniques to achieve strategic goals.
Target: The healthcare organisations must have a long term strategic target with a broad based strategic intent followed by analytics focused strategy.
Analysts: The healthcare organisations must have analytic talent, either in house, or consultants to provide continuous high quality advice.
Applications in healthcare
Analytics is increasingly important in healthcare and find applications in diverse situations. In practicing of evidence based medicine and adhering to online clinical protocols need serious data managment and analysis. The Department of Veterans Affairs is currently using this approach extensively.
Performance and outcome measurement based on integrated health information systems also needs data interpretation. Many private hospitals in India too have developed their in house quality management programmes based on data analytics.
Capacity management is among hospitals’ key challenges. When hospitals do not successfully manage capacity assets, they suffer by way of revenue loss, operational inefficiency, delay and patient dissatisfaction. Advanced Analytics can impact the way hospitals manage their capacity and other processes by enabling forecasting and scheduling for the immediate and longer term. Another desirable attribute is the ability to predict each patient’s pathway within hospital. Hospitals seeking to acquire an Advanced Analytics solution would do well to bear these factors in mind.
Situations like early detection of emerging disease vectors, spotting outbreak of epidemic and prevention of fraudulent health insurance claims also depend on data analysis.
Conditions like to indentify the patient most at risk for chronic disease and high cost diseases such as diabetes and congestive heart failure and implementation of effective chronic disease management programme demands the same.
Availability of quality data on morbidity patterns and patient safety are grossly inadequate in India, to design innovative health insurance products and effective patient safety programmes
Inventory management programmes and supply chain management, disease analysis, forecasting of demand for medical items and RFID or bar-coding technology to prevent spurious drugs can also become effective . Besides, decisions such as buy or outsource, underwriting for an expensive biomedical equipment like MRI, Digital radiology, brain suite and PET scan needs clarity.
Best marketing strategies, holding medical camps or advertising, resources spent in finance and accounts , activity based costing , multiple regression analysis, transportation, replacement and assignment models to refine processes can be well assessed through data analysis.
National Rural Health Mission
National Rural Health Mission (NRHM), a flagship program under Ministry of Health and Family Welfare (MoHFW), Government of India (GoI), has launched its ‘Health Statistics Information Portal,’ a web-based health management information system, a one-stop-site that will facilitate quick and efficient flow of information starting from the facility-level, up to the district, state and finally the centre. On top of all this, the system will provide an array of intelligent tools for advanced data analytics, robust data warehousing, reporting, monitoring, uation and overall program management.
Apllo Group of Hospitals
Each of the hospitals in the Apollo group tracks infection control parameters month after month and these are benchmarked with standards and variations and values are thoroughly analysed. Periodically clinical studies on infection control, pathogens and other related areas are also carried out. All infection control parameters are tracked as part of the ACE 25 clinical excellence initiative of Apollo hospitals where key quality parameters of each hospital in the Apollo group are entered on an online dashboard, scored and reviewed by the highest leadership of the group each month.
Sir Ganga Ram Hospital
Sir Ganga Ram Hospital (SGRH), a pioneer in health informatics, has been using data mining with SpeedMiner, a data mining software product by Hesper. SpeedMiner was installed as an adjunct to HIS at SGRH two years ago and has proved to be an effective business intelligence tool which helps in data dnalytics and real time monitoring of the Key Performance Indicators (KPI), query handling , and serves as a quality dashboard through the various data collated over a period of time under specific heads.
Johns Hopkins has created the Comprehensive Unit-Based Safety Program (CUSP) model, which supports local efforts to reduce hospital acquired infections and complications, and also improves nurse and physician satisfaction. CUSP lets hospital identify safety concerns, learn about successful approaches, develop and initiate solutions, and perform regular safety assessments based on data analytics.
National Health Service, UK
The United Kingdom’s (UK) National Health Service (NHS) is funded through general tax revenues. The funds are dispersed to about 105 different local health authorities, amounting to annual funding for approximately US$ 35 billion. With such a large sum of national funds going to such an important area, the decision-making process to justly allocate funds can be difficult indeed.
An expert team from York University spent 14 months studying the problem and developed a decision-making model, identifying key variables to explain health care needs and usage in UK. This analytics based model allocates resources more justly and fairly to the genuinely needy.
Manila Health Centre
In Manila Health Centre, whose drug supply to patients afflicted with category one tuberculosis was not being efficiently allocated to its 45 regional health centres. Researchers at the Mapka Institute of Technology set out to create a model.
The goal programming model successfully dealt with all of these goals and raised the TB cure rate to 88%, a 13% improvement in drug allocation over the previous distribution approach. This means that 335 lives per year were saved through this thoughtful use of goal programming.
Data analytics focuses on inference, the process of deriving a conclusion based solely on scientific knowledge and facts At a time when health organisations are operating in an competitive enviro ment and want to wring value for every penny spent, data analytics will provide them with the strong foundation and confidence they need to excel with minimised risk. Today, analytic strategy is viewed as a key engine of a d namic capability of an organisation. Indian HCOs need to generate quality data first and then analyse this for strategic decisions and research.