Can machine learning and predictive analytics usher the new healthcare segment?
The one sector that can benefit the most from big data and analytics is healthcare. It has the power to democratise and decentralise healthcare delivery through data, providing absolute transparency as well as accuracy. Data analytics has institutional as well as patient-centric applications such as discovery of the most suitable choice of doctor, hospital or treatment to more advanced applications for healthcare organisations such as accessing and applying latest research in daily practice.
The transition to a data-driven system must first begin with the effective collection of data. Healthcare organisations collect and analyze data from hospitals, wellness centers, ambulance services, referral networks, labs and imaging centers, research and other non-traditional data sources. The process of collecting, integrating, and analysing data can be complex. Since the data is sourced from different internal and external locations, its quality can be difficult to determine. In addition, about 80 percent of knowledge is unstructured, which further increases the challenge. The solution for this lies in the adoption of data mining to search for and apply relevant and authentic information.
Data mining and analytics paired with cloud technology enables ease of sharing and accessing patients’ data, which leads to efficient management of time for doctors, thus, improving the overall standard of operations for a hospital. However, it is difficult for healthcare organisations to improve on something they cannot measure. The best way for healthcare service providers to ensure efficiency and success in the long-run is by leveraging data to:
- Accurately determine costs and outcomes
- Identify potential areas for most improvement
- Regularly and accurately track performance
Data mining can have different connotations for different people, much like analytics or business intelligence. Simply put, data mining involves analysing large data sets, identifying patterns and predicting future trends and events. However, not all large data sets involve data mining for analytic purposes. Analytics are usually categorised as follows:
- Descriptive analytics
- Predictive analytics
- Prescriptive analytics
Data mining applies primarily to the predictive analytics category that helps to build predictive models from data analyzed. Several industries have successfully applied data mining and analytics and have witnessed phenomenal results because of it. The retail industry uses it to gauge customer response and banks use it to predict cost per customer, credit worthiness for lending among other sectors as telecom, manufacturing, automobiles, education, life sciences, etc. Unfortunately, for the healthcare sector, data mining is still largely an academic exercise with very little practical success.
Predictive analytics and machine learning in healthcare are the most talked-about areas currently. Healthcare can replicate the success machine learning has achieved in other sectors by gaining valuable insights to utilise predictive analytics and improve some of the most basic functions such as patient care, chronic disease management, hospital administration, and supply chain efficiencies.
Machine learning challenges the outdated, reactive approach to healthcare. It is in fact the exact opposite! Predictive analytics and machine learning offer proactive and preventative solutions that are indispensable for creating a modern, digital healthcare system.There are limitless opportunities for the healthcare sector to apply machine learning that can prove to be one of the most important life-saving technologies today. It can help enhance clinical, workflow, and financial outcomes with the following being a few of the examples:
- Cut down on re-admissions: Data collected through machine learning can help lower hospital re-admissions in a focused, efficient, and patient-centric manner. Doctors and practitioners can receive regular updates on patients most likely to be re-admitted. They can therefore take all the necessary precautions to reduce such risks.
- Prevent hospital-acquired infections (HAIs): Health systems can reduce HAIs, such as central-line associated bloodstream infections (CLABSIs) — 40 percent of CLABSI patients die — by predicting which patients with a central line may develop a CLABSI. Doctors can observe high-risk patients and intervene to reduce that risk by focusing on patient-specific risk factors.
- Predict chronic diseases: Machine learning can help hospital systems identify patients with undiagnosed or misdiagnosed chronic diseases, predict the likelihood that patients will develop chronic diseases, and present patient-specific prevention interventions.
- Predict paying capabilities: Health systems can identify who requires financial assistance, send reminders and determine how the probability of payment may change over time and after certain events.
- Empowers users to track their health: Medical apps and wearable devices empower users to be responsible for their own health by providing information that they require to take decisions about diet, nutrition, and exercise routine. This information, stored on the cloud can further be used by doctors and healthcare providers to treat certain diseases and illnesses by studying a patient’s history, past treatments, medications, etc.
- Reduce cost: With hospitals and healthcare organisations having access to relevant data on patients, administrative costs can be cut down by minimising wastage and errors
A data-driven healthcare system can lead to a paradigm shift within the sector to help engage patients and provide patient-centric care by collecting and storing information on patients centrally through electronic health records (EHR) and cloud storage. The rising costs of healthcare delivery, an aging population and a shortage of professionals are forcing the healthcare industry to rethink its strategy and build a more efficient, tech-enabled system. The industry and leading organisations must consider data as a strategic asset to help them reduce costs, improve decision-making and get efficient outcomes.
This article has been authored by Ravi Virmani, Co-Founder and Managing Director, CrediHealth