Big data analytics to boost excellence in clinical research

Clinical research will leverage automation and big data analytics to reduce errors, develop products faster and reduce operating costs.

IDG STAFF Jan 17th 2018 A-A+

At 17.6 percent of the GDP, healthcare industry in India has significantly grown over the last 20 years. According to a McKinsey report that taps various medical platforms for professional and patient engagement, the focus in healthcare sector is largely on clinical data management with real-world evidence (RWE). New data analysis tools today collaborate with different healthcare authorities keeping patient care in mind.


Clinical trials are transforming

Since 2005, clinical trials have significantly increased in India. Consequently, domestic biotech companies as well as international pharmaceutical companies have recognized the potential of big data in patient recruitment for experiments. Big data includes a plethora of medical case studies which involve genetics and innovation via technology. As 2017 ends, the right data standards and rules that govern clinical research will change the way clinical trials are conducted.

Some of the biggest trends in the healthcare industry with respect to big data include studies that focus on the following:

Although Indian healthcare sector has been slow in the adoption of analytics, the scenario is changing as more skilled professionals have entered the fray.

Patient-oriented care: It begins with validation from researchers as they conduct trials in a more organized way. Gathering systematic data mitigates errors, increases quality control of services, reduces costs for patients from false readings or reports and improves payment structures.

Drug development to commercialization: Experts are outsourced for large trials in clinics and specialty hospitals and government facilities. Compliance with rules and time-to-market products is critical.

Skilled human resources and infrastructure: Affordable skills and research analysts with technologies like SAS, and SPSS are the need of the hour.

Pooling of unstructured data:  This includes prescriptions, diagnostics, lab tests, patient care records and insurance claims.


Clinical data management (CDM)

Although Indian healthcare sector has been slow in the adoption of analytics, the scenario is changing as more skilled professionals have entered the fray. The trials are cost-effective even for small health clinics which have shied in the past due to exorbitant cost structures and security of patient data. Usage of EMRs and HMIs has already begun in many healthcare facilities across India.

Clinical data management is ushering in automation with complete data sharing between different medical experts to treat patients. It is also ensuring the reliability of records during processing from one format to another. Automation of data is also expected to greatly bring down the number of problems in large clinical trials.

Clinical data insights are available publically too. This will eventually let different healthcare systems to collaborate and cut operating costs. Many software tools are used for CDM. Some of them have been exclusively designed for pharma companies and others are built on open source. Various new policies ensure that an audit trail is available for all tests. If one needs a competitive edge, getting precise predictive models helps. These make the research more precise and the new products can be made available in the market sooner.

To make the results more authentic during the crucial R&D process multiple IDs can be created. The actual authorization can be limited to a few personnel only to ensure that the trial design is secure and safe. Any clinical failure is rectified with the expertise of specific professionals involved during the audit. This drastically reduces false claims during the trials with guidelines provided to meet healthcare demands of the future.