Improving Healthcare Efficency & Delivery by Adopting Healthcare Analytics

In countries with universal health coverage and government funded healthcare system, expenditure has reached to an unmanageable level and lawmakers are under tremendous pressure to decrease expenses, while ensuring a high standard of care. This has led to the widespread adoption of healthcare information technology tools and healthcare analytics tools comprising clinical, financial, administrative/operational and research analytics. In the United States, enactment of legislations such as Patient Protection and Affordable Care Act (PPACA), Health Information Technology for Economic and Clinical Health Act of 2009 (“HITECH”) are considered as the first step in achieving the goal of affordable yet effective healthcare. These regulations have mandated all federally funded health care providers to adopt information technology tools such as electronic health records (EHR) which are the precursor to the development of various tools such as predictive analytics, cognitive analytics and Population Health Management (PHM). Recent events such as Britain’s exit (Brexit) from the European Union is set to impact its National Health Services (NHS) with staff shortage, the decline in investment due to currency devaluation, and extra burden on healthcare companies with new administrative and regulatory hurdles.

Meanwhile, developing and mid-income countries, where providing even basic healthcare is a challenge can adopt these tools and delivery models to reach the target population and develop a robust healthcare delivery system. Adoption of analytical tools in conjunction with telemedicine, wearable devices will not only reduce the cost of delivering healthcare services but bring about a transformation in healthcare.

Evolving payment models such as “risk-based” or “value-based” in which payments are dependent on the physician’s performance and moving away from traditional “fee-for-service” model are set to change healthcare industry. The objective of Healthcare Analytics is to use the massive data to provide the right intervention at the appropriate time, lower health care cost and ensure quality health care is given. The potential benefits include a reduction in medical errors, fraud detection, detecting disease at earlier stages and increase preventative measures, decrease the length of stay at hospitals, and avoid repetitive hospital admission.

Factors such as federal health care mandates in U.S., transition from volume-based to value-based medicine, the emergence of big data and advancements in analytical technologies, digitalization of healthcare records, personalised medicine are some of the factors driving the healthcare analytics market growth. Factors such as security issues and data breaches, lack of skilled labour with analytical skills, lack of patient data confidentiality and transparency, lack of interoperability, increase in complex governmental regulation and reimbursement issues are hampering the market growth.

The healthcare analytics tools are stratified into descriptive, predictive and prescriptive analytics. These models need to be implemented in a chronological order, descriptive, predictive and prescriptive, respectively. Although descriptive analytics has a widespread adoption, predictive analytics is being implemented rapidly by healthcare stakeholders. While descriptive analytics focuses on providing a retrospective view of data, predictive analytics goes one step ahead and provides accurate predictions based on available data. For instance, predictive analytics can be used to identify patients who are at risk for hospital readmission and provide necessary care to avoid complications. Advanced analytics such as Image analytics and cognitive computing are emerging trends in the industry. IBM Watson health is leading this market and is expanding rapidly through acquisitions such as Truven Health and Merge health.

Healthcare analytics can be applied in various departments including clinical, financial, administrative/operational, research analytics. Clinical data analytics enables stakeholders to provide quality care, reduce medical error by evaluating physician performance. Clinical decision support is a major motivator for the adoption of clinical analytics. However, analytics is not an entity that would replace physicians; they are meant to enhance their performance. Financial analytics enable healthcare organisations to streamline their resources and operations to manage their costs efficiently. In recent years, many vendors are investing in research analytics platform to lower cost and time for drug discovery and development and gear towards personalised medicine.

Big data analytics which involves processing voluminous and complex data demands to scale up traditional hardware platforms and software tools. The most commonly used platform for data storage and processing is the open source apache Hadoop which enables distributed processing of large dataset and run applications across clusters of servers. Spark is a next generation concept in data processing and an alternative to Hadoop, developed by University of California, Berkley. Hardware and Software vendors market is dominated by IBM (U.S.), Oracle (U.S.), Microsoft (U.S.) and SAS Institute (U.S.) The majority of analytical tools are provided as a service, where analytical tools cannot be a standalone system, but needs to be integrated with clinical workflow to gain the actual benefits. In healthcare analytics market, rivalry among existing competitors is high. The top five players control about 69% of the market. These companies are expanding their competitive advantage through acquisition to grow organically as well as by adding more capabilities to their existing product offerings.

Cloud platform is emerging as an essential technology as it requires less investment and less deployment time. Data is stored in the cloud and eliminates the need to have large storage facilities. Small physician clinics and centres are able to implement platforms in the cloud which was not feasible earlier due to substantial investments. Cloud platform vendors allow pay-as-you-go method of payment where clients could pay as per usage. On-premises and web-hosted models of delivery have large client base albeit huge upfront investments due to secure data storage. In addition, on-premises and web-hosted model vendors can hold on to existing clients as the switching cost is very high due to substantial financial investment and time spent on training of staff. Cloud platforms are still working towards secure data storage.

North America dominated the global market as all the major players are located in U.S. With publicly funded healthcare system in Europe with stringent regulations, analytics is not implemented as widespread as North America. In addition, the government provides fewer incentives compared to U.S. for the adoption of EHR or EMR. Asia Pacific is an emerging market, where mobile health, telemedicine along with analytical tools provides opportunities for expansion.

The major players operating in the healthcare analytics market are Cerner (U.S.), Epic (U.S.), IBM (U.S.), McKesson (U.S.), Oracle (U.S.), Allscripts (U.S.), Optum (U.S.), Information Builders (U.S.), Medeanalytics (U.S.) and Verisk Analytics (U.S.).

Some of the other players are eClinicalWorks (U.S.), GE Healthcare (U.K.), Healthcatalyst (U.S.), Kronos (U.S.), Dell (U.S.), Microsoft (U.S.), Mu Sigma (U.S.), Next Gen Healthcare (U.S.), Nuance (U.S.), Philips (Netherlands), Practise Fusion (U.S.), SAS Institute (U.S.), Tableau Software (U.S.), Predilytics (U.S.), Welldoc (U.S.).


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