Exclusive Interview with Satish Pala, CTO, Indium Software

Satish Pala is the Chief Technology Officer (CTO) of Indium Software. His specialized skills include project management, requirements analysis, business intelligence, data warehousing, SDLC as well as solutions architecture. Satish is popularly known as an eclectic manager to work within the industry.  He is one of the most diligent and technically strong performers with a humble demeanor. Indium Software is a technology solutions company providing deep expertise in digital and QA services to its global customers. Customers range from innovative start-ups to global well-known enterprises. Indium is focused on its specializations in AI, advanced analytics, text analytics CoE, big data, data engineering, stream processing, and data virtualization and is an expert in low-code development across multiple platforms. It has a mission to provide customer-centric high-quality technology solutions to deliver business value with expertise in multiple cloud environments. The company has served more than 350 clients in over 20 years. Here is an exclusive interview of Satish Pala, the Chief Technology Officer (CTO) of Indium Software, where he enlightens the readers about how big data and AI have revolutionized the healthcare and Life-science sector efficiently.  

1. Kindly brief us about Indium and the services/specialization the company offers

Indium Software is a digital engineering company offering services to enterprises across industry domains. Indium’s services include application engineering, data & analytics, cloud engineering, DevOps, and digital assurance. Our specialization is in AI, advanced analytics, big data, data engineering, low-code development, and QA. We have digital accelerators namely teX.ai for text analytics and uphoriX for smart test automation.  

2. How elements of automation will help hospitals handle complaints better through big data and AI? 

Big data and AI, if implemented suitably, enable better patient care in the healthcare industry. One such example of implementation in hospitals is complaint management. Automation through big data and AI can perform the following in order to help hospitals handle complaints better: • Automated routing of end-user query through IVR to the relevant section or department based on the information requested. This is done by AI being implemented like smart search on data and information repositories of the hospital. • A chatbot can be deployed for online queries and complaints. Intelligent AI engines behind the chatbot can make the interaction very specific and informative. AI engine is built based on various combinations of questions/queries and answers or redirects. • Gather deep insights for pro-active decision making; big Data and AI can gather, collect and analyze past complaint management history and provide insights like resolution efficiency, turn-around time, etc. Better visibility into metrics will enable better complaint management.  

3. In what ways these new-age technologies can help speed up the processes and improve the overall operational efficiency in healthcare/pharma?

Some of the new age techniques include: • Big data, AI, and Visualization: Visibility into Operation Equipment Efficiency (OEE) KPIs like productivity, quality, and availability in Pharma especially in production lines using big data and analytics. The data collection, KPI calculation, and insights visualization are automated. • Predictive Analytics: Data analysis and prediction can enable efficient operations in Hospital Management. One of the main challenges faced by hospitals is to keep the right amount of staff in the shifts. Over-staffing or under-staffing impacts cost and patient care adversely. This is where data analytics can help predict appropriate staff required and in-patient room allocation based on historical information and patterns. • Digitization: Implementing digital solutions like Electronic Health Records(EHR), electronic prescriptions, call recordings, voice to text conversion, healthcare ecosystem integration will assist in efficient patient care by the clinicians and reduce patient wait time.  

4. Can you elaborate more on the future of intelligent text analytics and its application in healthcare?

The healthcare industry is generating vast amounts of data as well as digitized records. These datasets are either unstructured text, such as doctor’s prescriptions, medical journals, EHR, clinical trial content, medical transcripts, call recordings, etc. This poses an enormous challenge in deciphering and generating valuable insights. This is where text analytics can help extract and provide insights for improving healthcare services and patient care. Text Analytics automatically extract features, entities, context, relationships, and sentiments from a text in various forms thereby generating insights. So, in healthcare, these are specific to the medical domain such as medicines, diagnosis, symptoms, treatments, examinations, dosages, etc. Some of the ways text analytics can help include • Increase in the effectiveness of treatment by analyzing various treatment outcomes and patterns • Make informed decisions due to better profiling of patients by collecting various forms of data and having a 360-degree patient view • Increase operational efficiencies by digitization and • To prevent fraud by identifying abnormal patterns in healthcare documentation. Additionally, redaction which is a component of text analytics enables you to automatically identify and remove confidential information within any document while retaining its overall context.  This helps comply with data privacy laws such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), FDA compliance, and more.  

5. How will big data and AI help pharmaceuticals meet regulatory commitments?

The Pharma industry is mostly change-shy and risk-averse predominantly due to a lack of confidence in digital tech. With the potential of digital technologies like big data, AI, and Automation being demonstrated quite widely, it has changed the perspective of the Pharma industry on adopting these new-age technologies. Big data and AI could help the Pharma companies to meet the regulatory commitments such as HIPAA, GDPR & FDA compliance in multiple ways. Some of these include: • Enable data storage, high availability as well as easy and secure access to data which are required to meet regulatory requirements • Continuous and real-time monitoring of the drug manufacturing processes and product feedback to ensure the compliance Enables claim referencing to trial reports by AI. The compliance reviews and audits require close checks on safety and efficacy claims lining back to the original clinical trial. We can train the AI models to identify safety and efficacy claims and recommend links to the relevant sections of the related clinical trial report. This process used to be a tedious and time-consuming effort.