• Bonish Agarwal

Smart Feedback Analysis

Whether it’s finding a hotel, a place to eat dinner, choosing your next television or a destination for next trip, reviews do hold power. The ever increasing population of the world and growing spending capacity on medical treatments has put a tremendous amount of workload and social pressure on medical industry. Moreover with rapid growth in technology, people expect smart health care services and convenience for their medical treatments. According to WHO (Ref: https://www.who.int/news-room/detail/13-09-2019-who-calls-for-urgent-action-to-reduce-patient-harm-in-healthcare), millions of patients are harmed each year due to unsafe health care worldwide resulting in 2.6 million deaths annually in low-and middle-income countries alone. These dissatisfied patients and their relatives use social media to take out their frustration. Hence, feedback portals are crucial source to analyze performance of hospital services and management. Properly analyzed feedback can provide useful insights about strengths and scope for improvements of any service industry. e-Commerce websites are greatly impacted due to online feedback given by their customers. According to the study given on Learning Hub (Ref: https://learn.g2.com/customer-reviews-statistics), reviews uplift the sales by almost 18% and make 71% of the customers quite comfortable in buying the product.

At the same time, this analysis also gives power to patients to choose the hospital which matches to their criteria. We, the team of Optimum Data Analytics brings you one of the best hospitals review portal, which is quite user-friendly.

How this works?

Feedback portal is made in such a way that each hospital is ranked according to the reviews given to it. The Semantic Analysis of online feedbacks helps to vectorize the statements. Using deep learning models of Recurrent Neural Network (RNN) the data is trained for required categories.

Semantic analysis involves extracting meaningful information from natural language. To train the model more precisely and to make it more efficient, text pre-processing e.g. Tokenization, Stemming and Lemmatization is carried out. This processed text is then utilized for various Natural Language Understanding (NLU) like Named Entity Recognition or Topic Modeling. Algorithms like Latent Dirichlet Allocation (LDA) finds out required entities, e.g. Location, Facility, Cost etc. in case of medical industry, from the sentence. Sentiment Analysis algorithms help categorize the online reviews into the polarities within these topics. This process finally leads to displaying the reviews in a five-star ranking format along with a number of likes and dislikes.

Good online feedback can motivate medical staff for providing better service, increase their performance and thus increased workplace satisfaction. On the other hand this tool also gives power to patients to choose better medical facility for their treatment. In current COVID-19 pandemic, we get to see the news about low quality test kits being used (Ref: https://economictimes.indiatimes.com/industry/healthcare/biotech/pharmaceuticals/centre-decides-to-withdraw-faulty-covid-19-antibody-test-kits-cancels-import-orders-from-china/articleshow/75406955.cms?from=mdr). Our model would automatically lower down the score of such facilities based on this data. That’s the power of this tool.

These feedback portals will be the main source to decide medical facility in the digitized world.

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