Big Data and analytics have transformed the way we look at data. While the healthcare industry is no stranger to tools that capture and store data (Electronic Health Records), the industry is swiftly moving towards deep healthcare analytics, to radically change the way healthcare is delivered. The focus is shifting from merely treating, to prediction, and subsequently prevention. The move from volume-based to value-based models creates a strong case for deep analytics that provides personalized medicines tailored to the needs of individual patients, while widening the understanding of population health, to spot trends and prescribe effective solutions to the forecasted issues.
What is Deep Healthcare Analytics?
Gartner predicts that "automated healthcare" could account for up to 85% of all healthcare customer experiences by 2020”. This automation could range from a simple patient form to detecting diseases from an MRI or CAT scan (AI based).
Under the umbrella of AI based healthcare analytics, predictive analytics though in a nascent stage itself, is giving way to cognitive analytics, which includes conversational AI and related complex technologies. But one shouldn’t look at them as independent technologies, rather as levels on the scale of analytics adoption. Predictive analytics uses data from the past and present to forecast future scenarios, thus, allowing healthcare providers to plan, manage, and even preempt. Cognitive analytics using machine and deep learning techniques has the capability to provide actionable insights in real-time, by piecing together relevant data and applying the findings to a particular context. It is meant to supplement human decision-making.
Benefits and opportunities presented by Deep Healthcare Analytics
Personalized healthcare and precision medicine benefit patients because they facilitate comprehensive, customized, and accurate diagnosis and treatment in the present; while also recommending preventive measures based on future predictions.
AI based analytics are used to forecast and solve a number of operational challenges, like patient admissions, staffing shortages, and inventory requirements. When hospitals optimize their limited resources, they can provide better healthcare.
The use of analytics has tangible benefits for pharmaceutical companies. As reported in ‘The age of analytics: Competing in a data-driven world’ by McKinsey Global Institute, “Data are being used in R&D to identify the right target population for drug development, which can reduce the time and cost of clinical trials by 10 to 15 percent.”
Personalization of healthcare is estimated to have a cost impact anywhere between $2 trillion to $10 trillion globally. Deep analytics also helps in the forecast, prevention, and minimization of outbreaks of deadly and contagious diseases.
The use cases of AI in healthcare can be seen across patient care, R&D, and diagnostics. Chatbots are already functioning as digital assistants; reminding patients to take their medication on time, alerting them to refill prescriptions and take routine tests, and scheduling and managing appointments with doctors. Today, intelligent Chatbots can assess the symptoms of common ailments and provide immediate assistance to the patients. They can answer common queries and provide personalized solutions based on an analysis of the patient’s medical history. With respect to drug discovery, companies are using AI and Big Data to draw connections between diseases and medication, in order to come up with quicker and faster solutions. Cognitive analytics in healthcare is also opening ways to support sales and marketing efforts by studying demographic sentiments and eventually drive medicine uptake, as demonstrated in the next section.
CSS Corp and Deep Healthcare Analytics
Intel’s whitepaper titled ‘Predictive Analytics in Healthcare’ states that the “highest performers in analytics in healthcare are using it to help improve patient engagement, population health, quality of healthcare, and business operations.” CSS Corp’s cognitive analytics solutions have helped healthcare organizations succeed on that front.
Case in point
A renowned global pharmaceutical company was concerned about the decline in sales of flu vaccines and a lack of awareness regarding the importance of vaccination among its target customers. They wanted to gain insight into the factors impacting vaccination and customer behavior during flu season. Additionally, they required a model that would forecast flu outbreaks based on certain information.
CSS Corp provided a solution that supported and enabled flu vaccine interventions, while automating the flu forecasting framework using:
- Simulation Modeling - A framework for identifying the best time for vaccine intervention, using machine learning models and predictive modeling techniques. This helped in driving sales enablement initiatives based on pre-defined performance measures, and in predicting the flu outbreak three weeks in advance.
- Business Intelligence and Visualization Tools – Collated, integrated, and processed real-time data, created a dashboard to track flu outbreak risks, vaccination rates, patient behavior and online search trends on flu vaccines. This helped in gaining a deeper understanding of the situation, leading to vaccine promotion at the right time.
- Social Media Analytics - Algorithms to mine and understand social media comments and sentiments on flu vaccines. This helped in streamlining the flow of information to the targeted users.
Results
- The Customized Analytics Model with a real-time dashboard helped reduce the over-all cycle time from data to insights.
- The solution provided a recommendation framework for identification of enablers driving flu vaccine acceptance rates, leading to informed decision making.
- The solution provided actionable insights leading to a deeper understanding of patient segments, as well as improved sales margins.
- The mapping of online topics to offline patient behavior helped in designing and implementing initiatives to educate patients (to ease their apprehension, and clarify doubts regarding vaccination side-effects), thereby maximizing vaccine acceptance rates.
Healthcare Analytics is the present
A budding field, healthcare analytics has a long way to go, but the impact of using AI in drug discovery, diagnosis, treatment, and clinical decision making is far-reaching.
The right technology partner can help organizations navigate this journey from mere data collection to analysis, automation, prediction, and prevention. For more information on our Analytics offering, please visit: https://www.csscorp.com/services/digital-transformation-consulting/analytics-consulting-services/
References
- “The age of analytics: Competing in a data-driven world” by McKinsey Global Institute (McKinsey & Company)
- https://www.intel.in/content/www/in/en/healthcare-it/article/advance-analytics-is-coming-to-healthcare-in-a-big-way.html
- https://connect.intel.com/us_en_2017BUC-healthcare-LP02?_ga=2.194744558.27312971.1539594159-1168850462.1539594159
- “Predictive Analytics In Healthcare” by Intel
- https://www.wipro.com/blogs/manish-jindal/taking-healthcare-to-the-next-level-with-predictive-analytics/
- https://conversahealth.com/research/
- https://blog.appliedai.com/healthcare-ai/
- https://www.beckershospitalreview.com/hospital-physician-relationships/is-healthcare-ready-for-conversational-artificial-intelligence.html
- https://chatbotsmagazine.com/is-conversational-ai-the-future-of-healthcare-658a3d8e9dd5
