There is an abundance of data in healthcare, the hospitals, clinicians, laboratories and patients all have evolved to build, develop and operate their own version of Medical Informatics Systems. The Public Health systems have also evolved the systematic and periodic data collection process generating significant insights into the disease patterns and trends across populations. The very nature of digital adoption across healthcare and many other sectors was piecemeal, wherein various stakeholders identified the needs for their operations and functioning and adopted the low hanging technology. The data collected across various systems and devices were used for operational planning, operations optimisation, and the perceived impact of improving the patient experience and increased revenue. The growing complexity of public health datasets has created the need for more innovative approaches to facilitate data analysis and decision making through meaningful use of data. The concept of data analytics is seen by many stakeholders as additional layer over the digitalisation efforts and the perceived value proposition is not clear to the managers and top management, leading to limited investments in setup, management, and maintenance.
This article will help readers understand the common terminologies in healthcare analytics, the common barriers to adoption of analytics in healthcare industry, a framework to undertake analytics adoption for healthcare organizations
Terminologies in Healthcare data analytics
The definition of analytics used by IBM is, “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.”1 The analysis of any healthcare data can be classified into
- Descriptive – standard types of reporting that describe current situations and problems
- Predictive – simulation and modelling techniques that identify trends and portend outcomes of actions taken
- Prescriptive – optimizing clinical, financial, and other outcomes
A subarea of data mining is text mining, which applies data mining techniques to mostly unstructured textual data. Another close but more recent term in the vernacular is big data, which describes large and ever-increasing volumes of data that adhere to the following attributes:
• Volume – ever-increasing amounts
• Velocity – quickly generated
• Variety – many different types
• Veracity – from trustable sources
In all healthcare organizations, clinical data takes a variety of forms, from structured (e.g., images, lab results, etc.) to unstructured (e.g., textual notes including clinical narratives, reports, and other types of documents). Another source of large amounts of data is the world’s growing base of scientific literature and its underlying data that is increasingly published in journals and other articles.
The big data domain covers areas of health surveillance, global health emergencies, health promotion effectiveness, clinical research, digital epidemiology, the new set of ethical challenges also arise with complexity in data ownership, data structure, privacy, informed consent, and data sharing policies
Barriers to adoption of analytics in healthcare industry
Various studies and surveys, and industry reports have emphasized the benefits of big data analytics, however, the reality remains far from the claimed. The report by McKinsey 2 indicated the penetration of big data analytics in the pharmaceutical and health industry at 28% (indicating about 28% of tasks are transferred to digital platforms), compared to other sectors like retail (42%). The factors that have contributed to the limited use of big data analytics technology in healthcare can be summarised from the paper by Sebastian Herems
- The complexity and regulatory overreach in healthcare have led to the emergence of silos of healthcare data and interoperability of health IT between key stakeholders are lacking, which hampers efficiency, undermines coordination of care, and increases costs.
- Adoption of health IT is often resisted by powerful actors in healthcare delivery, this is confounded by initial and ongoing costs, technical support, technical concerns, the loss of productivity during the transition, and concerns about future obsolescence of purchased health IT. Further, it is observed if organizations adopt health IT, individuals often avoid using IT.
- Healthcare information is highly personal, and the more patients perceive medical information as sensitive the less they are willing to disclose it or to adopt new health IT.
- Few health IT suppliers build products that are easy to use, on many instances this leads to the frustrated healthcare practitioner, that health IT requires lengthy data entry and disrupts rather than assists their practice.
The resistance for analytics adoption has been on decline after COVID-19 pandemic with data from various surveys4,5 indicating 30% more practitioners willing to use health IT tools to support their daily practice, 68% increase in organisation investing in enterprise wide IT solutions, post pandemic.
A Healthcare Manager overseeing digital transformation must consider the factors highlighted above and develop a systematic strategy to ensure successful implementation of digital transformation and effective uptake by the organisation. The emphasis of this article is to look at the digital transformation from eco-system perspective rather than a firm level perspective.
Defining the Organisation Capability
The IBM study, classified any organisation into three categories
A) Aspirational: These organizations are the farthest from achieving their desired analytics goals. They are focusing on efficiency or automation of existing processes and searching for ways to cut costs.
B) Experienced: Having gained some analytics experience – often through successes with efficiencies at the Aspirational phase – these organizations are looking to go beyond cost management. Experienced organizations are developing better ways to effectively incorporate data analytics so they can begin to optimize their organizations.
C) Transformed: These organizations have substantial experience using data analytics across a broad range of decision making. They use analytics infrastructure as a competitive differentiator and are already adept at organizing people, processes, and tools to optimize and differentiate.
It is thus essential to assess the organisations’ ability to setup and utilise the power of analytics for decisions making and more important to understand the human resource requirement.
The value proposition and capture are key steps in preparing for analytics adoption, wherein the decision maker
Define the Value proposition transformation
The articles on value proposition transformation indicate the emerging need to transforms from an acute view of healthcare, in which the hospital is the centre of care, toward one in which connected and remote care is focused on prevention.
Value capture transformation
The Manager needs to understand the costs of coordinating analytical services will go up, at least initially, whereas the total cost of services delivered and the need for further care will all be reduced and the quality of care improved. More significantly, as the coordination costs of traditional organizations goes up, their roles are reduced, and their revenues are reduced as a result, improvement in patient care and in system productivity may not be reflected in higher profits for traditional healthcare industry.
To summarise the data analytics should be seen as an investment for future of the organisation by the managers and decision makers, the initial phase of setting up the infrastructure for analytics will add up to cost, some frustration amongst the stakeholders. As a trait of good leadership, the focus should be on long term gains and benefits to the organisations.