Healthcare and Life Sciences

Most countries today face the challenge of offering good quality healthcare services at a reasonable cost. When the doctor to patient ratio is poor, especially in smaller towns, use of technology becomes critical in filling the gap. AI is playing much role in areas like diagnostics and biological health monitoring using devices like fit-bits. AI is being used to find vaccines and medicines for deceases. 

Further, high cost of operations for providers mean there is a need for intelligent automation to improve processes. Hospitals are using RPA, OCR and ML for improving processes like managing patient appointments, invoice payment processing and health data management. Players are using Geographical Information System (GIS) for population health monitoring and planning location for healthcare facilities.

Featured Solutions

+ - Using Machine learning for predicting disease and managing data
Medicine and healthcare practice is driven by physician’s judgment and expertise and generates huge amount of unstructured data. When availability of doctors is scarce, there is a greater need to leverage technology that can aide doctors and improve their efficiency and reach to service more patients. Some of the ways in which ML can be leveraged, NLP for structured data – Medical data is generally unstructured and not systematically stored, making it tedious to retrieve and analyze patient history. We can use NLP to convert the unstructured EHR and other medical data to generate intelligence like patient classification, diagnostic summaries and cure paths. Predicting disease – Tools like fitbits and wearable devices are increasingly being used to capture personal health data with the aim to monitor wellness. Machine learning can be used to predict health risk by identifying patterns and early warning signals. Diagnosing disease – We can leverage machine learning to perform image analysis on pathology reports to diagnose disease. This can speed up the diagnostic process thus enabling serving larger number of patients.
+ - RPA for efficient hospital processes
Hospitals and healthcare centers have many administration tasks which are repetitive, large volume tasks and do not always directly contribute in patient care. These tasks not only result in cost overheads, but also distract management from improving care quality and number of patients that hospitals can service. Intelligent automation can contribute in handling these tasks. Patient scheduling – Appointment booking is moving online but administration still has to manually do many tasks like allocating doctors to cases, handling cancellations, collating patient data and intimating patients about expected wait time. RPA can wade through multiple IT systems and carry out these tasks thus reducing costs and service improving quality Claims administration – Claims processing involving multiple entities like TPA, Insurer, Hospital and Banks. The process is data intensive and calls for strong case tracking, updates and risk management. RPA can help fetch data from multiple sources in multiple templates and help update the Hospital Admin system and generate up to date MIS for the management. Regulatory reporting – Healthcare sector requires stringent regulatory reporting and producing these reports often entail huge human effort. RPA can help in collating data from multiple systems with pre-defined frequency and put together reports in standard formats for final review of officers. This improves reporting accuracy, reduces risk of any misses and helps reduce reporting costs.
+ - Bulk service agreement migrations for Telcos using RPA
A telco typically has numerous enterprise clients with varying duration of contracts for different connectivity services. While part of these contracts data are entered in enterprise systems like SAP, most contracts are stored as MS Word documents which are signed as agreements. Typically, dozens of such agreements are up for renewal every month and updating the new terms at multiple places is a tedious task. Fields like newly agreed rates, duration, discounts, service levels, payment terms, etc. have to be changed every time, which takes up large amount of human resource time. You can use intelligent automation which can peruse through emails, PDFs, MS Word and MS Excel documents to identify and pick new data fields (e.g. rates, SLAs) which have to be inserted in the new contract. Such an intelligent automation system would include RPA for operating ERP and other systems while OCR for reading unstructured data like PDF. Once the new clause related data are picked up and organized, RPA can draft new versions of agreements and make data entries into ERP systems which could be approved by the Commercial and Sales teams.
Such an intelligent automation can save precious time of resources in doing mundane tasks and improve customer satisfaction for turning around the renewal agreements quickly.
+ - Health management using GIS
Public and private health management has much to benefit from GIS tools to monitor disease sources and its spread and plan healthcare facilities. We can help government departments and hospitals to deploy GIS for improved healthcare management. Disease spread – Disease like Malaria or Covid-19 have typical pattern of spread which depends on factors like population density, hygiene and healthcare infrastructure. GIS can help in mapping these factors and cases of diseases to analyze and estimate disease spread. Healthcare planning – We can help planners to map doctors, hospitals, centers, medical suppliers, ambulances across cities and towns using GIS tools. This can help analyze areas with under penetration of healthcare components and plan for filling the gaps. Additionally, we can map the healthcare quality, health scores and cost of healthcare to improve planning by the authorities.

Case Studies

A leading Health insurer

The insurer conducted customer satisfaction study for their insurance claim process. We helped the client use NLP based Machine learning to analyze the customer feedback calls to assess topics highlighted by the customers and their sentiment related to claims experience.

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