HOW DATA ANALYTICS CAN CHANGE THE HEALTHCARE INDUSTRY

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The global demand for healthcare services continues to rise significantly, with aging populations, increasing prevalence of chronic diseases, and the search for a better quality of life.
Such prospects combines with industry growth, technological integration, and strong value-creation potential make healthcare an exciting industry to work around. However at the same time, cost concerns, uncertainty, and complexity make it an unnerving one, providing substantial upsides for players that can deliver value-creating solutions and thrive under uncertainty.
McKinsey’s recent research into industry profit pools indicates that, on average, the industry is delivering value-creating solutions and consequently showing attractive profit growth. Between 2012 and 2016, total over-all healthcare industry profit pools grew at a faster rate than the combined figures of the top 1,000 US companies.

THE HEALTHCARE-TECH LANDSCAPE
The global healthcare services and technology market today consists of a wide range of companies that can be grouped into a select number of categories – business services, consulting, data, analytics and information and software platforms. Together, these categories had approximately $35 billion in profits in 2016 in US alone.

But keeping with trends it seems data, analytics, and information services, which includes reporting and benchmarking services, are likely to continue to have the fastest growth. McKinsey forecasts a CAGR in profits of 16% to 18% for this segment over the next five years, driven by increasing business demand for analytics services, both as a table-stakes capability and potential competitive advantage.

THE FUTURE LIES IN HEALTHCARE ANALYTICS
As these healthcare organizations keep developing more sophisticated big data analytics capabilities, they are beginning to move from basic descriptive analytics into the realm of much more insightful predictive insights. Instead of simply presenting information about past events to a user, predictive analytics can help hospitals estimate the likelihood of a future outcome based on patterns in the historical data.

This opens up the possibilities for clinicians, financial experts and administrative staff to receive alerts about potential events before they happen and therefore make more informed choices about how to proceed with a particular decision. The importance of being a step ahead of events can aid intensive care, surgery, or emergency care, where a patient’s life might depend on a quick reaction time and a finely-tuned sense of when something is going wrong.

However, as of now, very few high-value use cases for predictive analytics exist throughout the healthcare ecosystem and also may not always involve real-time alerts that require a team to immediately spring into action. Rather as a start organizations can apply predictive analytics tools to their financial, administrative and data security challenges to experience significant gains in efficiency and consumer satisfaction, but true potential of such predictive insights lies in execution of data to save lives in the following manners:

RISK SCORING FOR CHRONIC DISEASES
Prediction and prevention best applies in the world of population health management, as organizations that can identify individuals with elevated risks of developing chronic conditions as early in the disease’s progression as possible have the best chance of helping patients avoid long-term health problems which are costly and difficult to treat.
Using predictive analysis to create risk scores based on lab testing, biometric data, claims data, patient-generated health data and the social determinants of health can give healthcare providers insight into which individuals might benefit from enhanced services or wellness activities, in advance, saving patients huge costs in the long run.

MACHINE LEARNING TO COMBAT PATIENT DETERIORATION
During hospital stays, patients face a number of potential threats to their wellbeing, including the development of sepsis, the acquisition of a hard-to-treat infection, or a sudden downturn due to their existing clinical conditions.

Data analytics can help caregivers react quickly to changes in a patient’s vitals, and may be able to identify an upcoming deterioration before symptoms clearly manifest themselves to the naked eye.

Machine learning models are particularly suited to predicting clinical events in hospitals, such as the development of an acute kidney injury (AKI) or sepsis. A predictive analytics tool at the University of Pennsylvania, leveraging machine learning and EHR data helped to identify patients on track for severe sepsis or septic shock 12 hours before the onset of the condition, as explained by a 2017 study.

FORESTALLING APPOINTMENT NO-SHOWS
Unexpected gaps in the daily schedules can have financial ramifications for healthcare organizations while throwing off a clinician’s entire workflow.
Predictive analytics can be used to identify patients’ likeliness to skip an appointment without advanced notice which can improve provider satisfaction, reduce revenue losses and give organizations the opportunity to offer open slots to other patients, thereby increasing greater access to care and faster service delivery.
Hospitals may also be able to use this data to send additional reminders to patients at risk of failing to show up, offer additional services such as transportation to enable individuals to make their appointments or even suggest alternative settings and times that may better suit their needs.

PREDICTING HOSPITAL UTILIZATION PATTERNS
In addition to helping organizations get ahead of no-shows, predictive analytics can give providers a heads up as to when the clinic is about to get busy.

Hospitals that operate without fixed schedules, such as emergency departments and urgent care centers, need to vary their staffing levels to account for fluctuations in patient flow. Inpatient wards must have beds allocated for patients who need to be admitted, while outpatient clinics are responsible for keeping wait times low for patients.

Using analytics to predict such patterns in utilization can help to attain optimal staffing levels while reducing wait times and raising patient satisfaction. Visualization tools and analytics strategies can model patient flow patterns and highlight opportunities to make workflow adjustments or scheduling changes, to ensure better delivery.

MANAGING THE SUPPLY CHAIN
The supply chain is one of the business’s largest cost centers, and represents one of the most significant opportunities for healthcare institutions to trim unnecessary spending and improve efficiency. Predictive tools are in high demand among hospital executives looking to reduce variation in supply chain deliveries and gain more actionable insights into ordering patterns and supply utilization.

Cardinal Health in 2017 stated that only 17 percent of hospitals currently use automated or data-driven solutions to manage their supply chains. In the same year, Global Healthcare Exchange ranked predictive analytics for supply chain management as the number one item on the executive wish list.

USING DATA TO IMPROVE ENGAGEMENT & SATISFACTION
Apart from supporting chronic disease management strategies, cutting wait times, and attaining better utilization rates in cases for both hospitals and their supply chains, predictive analytics can keep patients engaged in other aspects of their care, as well.

Consumer relationship management has become a vital skill for both providers and payers, with health insurers promoting wellness and reducing long-term spending – and predicting patient behaviors is a key component of developing effective communications and adherence techniques.

Local health startups such as Praava Health is using its data analytics tools to create consumer profiles that allow the payer to send tailored messaging, improve customer retention and discover what strategies are most likely to be impactful for each individual. This study of behavioral patterns can go on to create meaningful care plans and keep patients engaged with their financial and clinical responsibilities, with greater focus on increasing efficiency, reducing costs and providing highest levels of customer satisfaction through use of big data.

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