Potential of Predictive Analytics in Healthcare

Robby Gupta

Predictive analytics is transforming how decisions are being made for different industries today. It has the potential to fundamentally change how decision-makers draw conclusions for their business. For industries such as healthcare, predictive analytics can truly be revolutionary as we continue to explore its potential.

Consumers already experience predictive suggestions every day as they type in Google search box or they type on their keypad. Targeted ads on the web pages are also a part of the predictive analysis and appear based on the history of internet usage.

Is healthcare ready for predictive analytics?

The concept of offering suggestions and services based on online behavior predictions of consumers is being introduced into the healthcare sector now.

In the past decade, there have been a few key developments that have paved a smooth path for data and automated predictive analysis in healthcare. With the adoption of electronic health records, healthcare data has been digitized and predictive analytics can now be leveraged.

Today, the healthcare sector is focusing on cost reduction and healthcare quality measures with the help of big data and analytics. The adoption of new models for care reimbursements, and latest guidelines & regulations are facilitating the process largely.

In addition, technology is also evolving to support the development of the latest software and various data analysis applications.

These developments and advancements are preparing healthcare industry for the momentous adoption of predictive analytics and for the coercion of next wave of digitization. This is definitely going to lead to new models of care in precision medicine; in addition to the leveraging of EHR and predictive data for diagnosis and treatments.

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Exploring the current potential of predictive analytics in healthcare

During cancer treatment, specialists used predictive data to diagnose the disease for some patients and to give the prognosis. This success allowed the stakeholders to test the grounds further as federal government decided to render support with an increased activity towards the precision medicine initiatives.

Private players in the biotech and healthcare space have also joined in which has enabled an aggregation of clinical, pathological and genomic data. These large storehouses of data assisted the study of specific mutations in cancer and development of highly sophisticated and targeted therapies that did not cause any collateral damage to the adjoining healthy tissue.

The predictions say that with the improved understanding of oncology and enhanced information within the repositories of predictive data, the therapies will be further focused and much more effective.

In addition, the availability of supporting predictive data and the right technology will aid a substantial reduction in the cost of research and development. This leads to the conclusion that in the next few years, the same technology can prove to be successful for other conditions that need extreme corrective therapies.

This, in itself, establishes the potential of predictive analytics in healthcare.

Uses of predictive analytics in healthcare

To execute possible predictive analytics use cases in healthcare, we must explore EHR which makes immense data waiting to be tapped. Major specialty medicine foundations are building large data deposits by using the EHR statistics of past and present patients.

This data is put to good use for research, benchmarking, and for reporting performances against applied measures. This phenomenon further holds an enormous potential when we talk about large-scale studies that can help identify the health patterns of a complete population.

Medical sciences have also witnessed some interesting uses of healthcare analytics based on EHR data where it helped envisage mortality risk and complications occurring due to hospital-acquired infections.

Some other use cases of predictive analytics are:

  • ◙ Predictive analytics in healthcare is an effective tool to risk-stratify patients at the time of discharge and to make sure that the patients get adequate post-acute care to avoid further complications.
  • ◙ Another potential use of predictive analytics is to detect fraud, assault, and squander within the healthcare system which can lead to lower costs and easier treatments.
  • ◙ Predictions based on data can be significant in the selection of the best possible treatment and therapies for a patient. It is possible to achieve the precise cure plan based on the specific disorder and various personal or social determinants including individual health parameters.
  • ◙ In the modern outset of healthcare, providers are using clinical data to predict risk for patients sending out targeted messages that influence change in behaviors, impel healthier effects and reduce overall costs.

“The idea that machine learning is about to be launched in our healthcare system is tremendously exciting. It could really turn the system on its head. Ever since healthcare was something humans did, the patient has had to hold up his hand and the system would respond. The idea here is that if you have rich enough data you can instead predict who may need help and do outreach and move care upstream. That is a goal for healthcare in general, whether it is dealing with cancer or a person heading toward self-harm. I am excited and thrilled to see how clinicians will use it.”

Don Mordecai, M.D., Kaiser Permanente National Leader for Mental Health and Wellness

The challenges in execution

Unfortunately, the health groups and specialty establishments leave this data soiled for use. In the recent past, medicine sector witnessed cases of analytics engines scanning hoards of medical papers to find the diagnosis for rare diseases.

In these cases, if there were an availability of EHR data combined with the repositories of medical knowledge, it would have facilitated the providers with an ability to make good use of analytics engines, scan actually available data, and find the remedies.

The potential for big data and predictive analytics in healthcare is enormous and yet untapped owing to the following challenges:

Thus, for a successful implementation of predictive analytics, the following catalysts must come together:

  • ◙ Electronic Health Records
  • ◙ Data Integration Tools
  • ◙ Cloud Computing
  • ◙ Skilled and Talented Workforce

Conclusion

Medical institutions and healthcare facilities are willing to pursue predictive analytics in healthcare to renovate the systems.

However, the diverse challenges that interoperability and lack of talent within various processes pose are huge, and need to be addressed.

As we gradually overcome these challenges, predictive healthcare analytics will continually prove its potential in offering cost-effective patient care, reducing patient readmissions, identifying the patients at risk, driving proactive preventive care and improving the management of chronic conditions.

We, at TechJini, work closely with healthcare providers to improve patient experience, increase revenue and achieve new efficiencies. With our expertise in the latest technologies and experience in working with some of the industry leaders, we can ensure smooth implementation with immediate ROI on your project. Please feel free to reach out to us if you have any questions.

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about the author

Robby Gupta

Robby Gupta is the head of US operations for TechJini, Inc. He has had varied experiences working in New York, Cupertino, and Bangalore with packaged & amp; custom web and mobile app development for an assortment of industries. His current focus is Immersive Technologies, IoT, AI bots and their applications in the digital enterprise.