Data Analytics in Healthcare Industry

Can historic data reveal future trends? Health care industry has always relied on meticulously measured /recorded data. Advent of new tools and technology to capture information has increased datavolumes to 25000 petabytes in 2020.

Digitization will provide impetus for the healthcare industry to move towards patient specific treatment for better health outcomes.Global big data analytics in the healthcare market is estimated to grow with 19.39% CAGR during the year 2019 – 2027. The market will grow to $96,844 Million by 2027.(source Medical Health & Life Science Research News)

So what is Data Analytics?

Data analytics aims is making sense of large data, by systematic collection and analysis of data to identify patterns and predict trends, revolutionizing the way data is managed and visualized.

In healthcare facilities both historic and real time data is collated from four key areas:

  • Claims and Cost
  • Pharmaceutical and Research and Development (R&D),
  • Clinical data (from electronic medical records),
  • Patient behavior and sentiment

Data analytics in healthcare industry focuses on clinical and financial analysis, supply chain and recourse management, handling of claims and frauds.

How can healthcare industry benefit from data analytics?

Data analytics has emerged as a major subset of business intelligence that has impacted areas likedecision making, marketing, customer care and operations. In healthcare industry it has significantly improved patient care with delivery of personalised and timely diagnosis, optimising resources and implementation of better regulatory compliance and data confidentiality protocols.

Healthcare data analytics will not only help doctors make better decisions but also aid the business end of healthcare like staffing, budgeting and payer contracting.

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Resource optimization

Data analysis can help hospitals identify bottlenecks easily and accuratelyaiding in smooth functioning.

  • Help hospitals to ensure optimum use of their personnel, diagnostic and consumable resources.
  • Reduce pharmacy inventory overloads.
  • Help to reduce wastage resulting from repetitive diagnostic testing.

Remote data capture and analysis through hand-held devices can result in reduced hospital visits for chronic conditions and will also provide a platform for performing routine healthcare virtually.This will contribute immensely towards cost reduction makingquality healthcare affordable.

Timely and precise diagnostics

Data analytics and especially predictive data analytics has proved to be a vital tool in early diagnosis of medical conditions. Data analytics offers insights to patient condition which can be used by doctors to make accurate and timely diagnosis, limiting human errors which is a major factor contributing to poor healthcare management.

In critical care units like intensive care, during surgical procedures or trauma care units’early disaster prediction and quick reaction is important, predictive data analytics has proved invaluable in providing timely care to avert medical emergencies or death.

Data analytics can help hospitals to deliver treatment proactively thereby reducing hospital mortality and readmission rates. Predictive analytics can warn facilitatorsof patient exposure to hospital acquired infectionsensuring timely containment and prevention.

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Personalised health care

Data analytics helps hospitals deliver personalised health care and improve patient engagement by:

  • Identifying patient-specific health information, medical risk scores and previous medical information at the point of care.
  • Helping detection of chronic conditions risk in patients much earlier and prevent onset of these conditions.
  • Predicting patient response to specific drug/treatment.
  • Assisting patients in directly involving in their health management leading to improved lifestyles.

Continuous monitoring of health parameters of a patient’s with chronic conditions allows doctors to make better decisions to manage day to day healthcare.

Data confidentiality and regulatory compliance

In the rapidly changing regulatory scenario data analytics tracks regulatory changes and ensures patient’s data protection ensuring regulatory compliance. Real-time data acquisition for analytics can easily flag atypical data sets preventing frauds/mismanagement.

Data analytics and care payers

Health insurance companies collect vast data fromhospitals and patients, with the increasing dependency on data analytics and shifting focus of healthcare industry towards value based patient centric healthcare it is imperative that care payers harness the value of their data to:

  • Access member personal and hospitalisation data to device personalised health plan or plan for specific health condition.
  • Analyse past and current claims data to detect fraud
  • Use predictive data analytics to identify chronic and high risk prospective members

Data analytics in healthcare will create new roles for healthcare professionals hitherto unheard of in the conventional setup. It also redefines conventional roles allowing care givers to shift focus to care giving and spend less time as data recorders.

Challenges in implementing healthcare data analytics

Sheer volume of data generated/capturedfrom various sources and in varying formats poses a number of challenges in implementation of data analytics solution in health care industry. Some of them include:

  • Quality of the data generated. Collected data needs processing to eliminate duplicate, irrelevant and incomplete information.
  • Availability of relevant data at the point of use becomes a deterrent, due to regulation on patient information confidentiality.
  • High cost involved in storage and handling of huge volume of data compounded with high securityrequirement.
  • Need forinstitutional level acceptance and adaptation.
  • Reduced doctor patient interaction might take away the human factor in treatment.

Selecting a Healthcare Analytics Solution Provider

Data analytics is redefining the way healthcare organisations are looking to optimize patient care. With a number of players in the field, choosing the right partner is critical for implementing a robust data analytics solution.

Before you approach a vendor

  • Understand existingdata analytics technology options
  • Assess the analytic tools/technologies that exist in your organisation and evaluate your needs.
  • Decide what needs to be done and who does it.

Choose your vendor on following criteria:

Time to value: Solution with rapid time-to-value is important for reduced project timelines that ensures quick access to reliable data and cost effectiveness.

Experience and track record: Service providers experience and success in implementing similar projects will guarantee optimum solution with fewer roadblocks.

Extensibility: In the ever changing regulatory requirements, analytic solution that can easily adopt and upgrade becomes equally important.

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Phygital Insights for healthcare data analytics

Recognising the need for a tool to utilize the huge amount of data available in health care facilities Phygital Insights has designed a data analytics solution that mines the wealth of knowledge available in databases, addressing all the core verticals of the healthcare facility.

  • Clinical Healthcare Services
  • Infrastructure and Operations Services
  • Revenue Cycle Management Services

Unification and presentation of data from all the sources within a facility using cutting edge technology defines our data analytics solution.

Clinical Healthcare Services

Our Artificial intelligence driven health care analytics can be deployed in critical care area like ICU to monitor patient vital signs and other parameters to improve patient health practices, treatment outcomes and plans. It facilitatesmonitoring and triaging in an ER, collatesdata from video and image processing units for analytics in both disease pathology and medical image processing.

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In predictive analytics our AI driven solution has eased interpretation of biomarkers and identification of specific diseases and use score analytics for predicting mortality risk. Machine learning approach has driven non invasive detection and treatment of cancer.

Infrastructure and operation services

Analytics services for Infrastructure and management ensures smooth management of day to day activities within the acceptable hospital working framework.  E-ICU and remote monitoring promotes effective use of healthcare professionals’ time. Voice to text conversion ensures easy data capture for documentation.

Revenue cycle management services

AI driven analytics can simplify hospitals’revenue cycle process right from patient admission to the payment of claims. Seamlessly integrates with healthcare payers analytics.

Devising a data collection and analytics solution for healthcare industry is challenging.  A good analytics solution should be able to collect data in varying formats, times and present it in a reliable format so that the industry can achieve improved patient care and health outcomes at affordable cost.

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