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Five Trends Shaping the Digital Health Customer Experience

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4 minutes, 38 seconds read

The fast growth of the digital health industry in India due to COVID-19 has led to the reshaping of customer health experiences. Innovations like mobile healthcare apps, telehealth services, e-pharma services are witnessing higher adoption rates and transforming the digital health customer experience. 

Lack of awareness regarding the use of mhealth apps, uncertainty about apps’ working efficiency, security issues, etc. were the root cause of its wavering development, prior to the pandemic. During lockdowns, nearly 67% of Indians felt comfortable receiving medical advice over calls and video sessions, according to a Royal Philips survey. 

The healthcare industry has shifted towards a patient-centric model to deliver convenient and meaningful experiences from the patient’s home. Below are the top five trends that are shaping digital health customer experiences:

Customers are relying on mHealth apps

Mobile health apps in India have witnessed an increase in downloads due to changes in lifestyle, increased interest in fitness & wellness programs and to track & monitor a variety of health data — sleep patterns, calorie intake, physical activity, etc. Followed by the telehealth segment, the mHealth segment is expected to dominate the Indian market by reaching approximately USD 1.87 Bn by 2024. 

Mobile health apps in India such as Practo, PharmEasy, 1mg, Medlife, cure.fit etc. allow customers to order healthy food, buy medicines with discounts, receive health tips and attend virtual doctor consultations by staying at home. Even though mHealth apps are general wellness related, the number of condition management apps are likely to increase with customer engagement. Moreover, the growth of the mHealth segment will ensure cost effective healthcare services that will prompt the consumers to use health apps. With the rise of mobile health apps, more benefits are likely to be incorporated such as in the case of health emergencies where an app can send the location of the needy to the hospital, thus saving ambulance drivers’ time in following directions.

Increase in Demand for Personalized Care

Customers have begun to feel empowered and valued through wearable devices and other digital health tools as it is enabling them to take control of their health. With electronic health records in hand, healthcare organizations are leveraging patients’ health records that are helping in optimizing the digital health customer experience. Predicting problems and providing solutions before they bother the patients has become the new model. This has paved the way for hyper personalization. By analyzing an individual’s DNA, it allows HCPs to monitor patients’ medication, provide health tips and helps them to diagnose diseases early. For instance, DNAfit offers genome-personalized health advice, workout plans, etc. that help customers in framing a daily routine. Apple Healthkit also functions in a similar fashion to personalize healthcare services as patient data is collected, compared and mined to result in a customized health experience.

Younger generation has more trust in tech companies

Around 32% of gen X and 43% of millennial are open to receive virtual healthcare, according to an Accenture survey. As the younger generation provides active feedback to the healthcare organizations, examining their behaviour can provide significant insights that might help mending the existing gaps between HCOs and customers. According to a recent Deloitte survey, empathy and reliability are the two factors that customers expect from healthcare providers. This shows that when customers are given the option to own their personal data related to health, healthcare organizations are more likely to attract customers. Considering how consumers are sensitive about their data, data interoperability is likely to help organizations in meeting consumer needs. In addition to this, increase in digital touchpoints are likely to multiply to meet diverse consumer needs.        

Increased Demand for Value-added services

According to an Accenture survey, around 57% of customers are open to remote virtual care. This shows the increasing appreciation of real-time assistance and contactless healthcare. Healthcare providers are likely to produce more value-added services by enhancing patient engagement, data collection, digital health channels. Traditional ways of treatment will change when HCOs leverage patient data from technologies and smart devices. Expert advice of HCPs in developing value-added services will further assist in producing accurate solutions for patients. Consumer demand for value-added services shows the increasing expectations from the digital health industry that will transform the customer experiences, as the leading health organizations are likely to produce more digitally enabled health solutions. Post COVID-19 when people begin to socialize, the contactless health services will be useful in cases of health emergencies, or for old people who find it hard to travel. 

Consumers are open to omnichannel virtual care

Be it buying of medicines, or keeping a regular check on health, the digital health tools such as mHealth apps, fitness trackers, etc have been adopted by consumers to satiate their healthcare needs. Openness to various digital health channels shows the strengthening of consumer trust. Recently Apple launched Apple Watch series 6 that allows users to take on-demand readings of blood oxygen level anytime. Its potential to give readings anytime and anywhere reflects customers’ increase in usage of digital health tools. Apart from tracking steps, fitness trackers also have advanced health features like, heart-rate monitors, SpO2 monitors, sleep tracking, etc. Web apps and chatbots are being used by healthcare organizations to assist people with health-related problems. Digital healthtech company Your.MD uses chatbot and web app to help customers get personalized health information. The future of the digital health industry is likely to witness an enhancement and increase in the number of access points. Increased acceptance of omnichannel will lead to optimization of customer engagement as HCOs will have more resources from where they can leverage patient data. 

To know about how healthcare industry is bringing hospitals to a customer’s doorstep, watch our webinar on Digital Health Beyond COVID-19.

Know about our work in Digital Health and how we have helped clients such as Suraksha Diagnostics, Abbvie, Religare Health Insurance, and SBI Health Insurance build mobile and web applications improving their operational efficiency and customer experience.

Further Readings:

  1. Building Consumer Trust in the Digital Healthcare Era
  2. HealthTech 101: How are Healthcare Technologies Reinventing Patient Care
  3. Virtual health: Delivering care through technology
  4. How Mobile Micro-Health Insurance can unlock ‘Digital for Bharat’?
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Tabular Data Extraction from Invoice Documents

5 minutes, 12 seconds read

The task of extracting information from tables is a long-running problem statement in the world of machine learning and image processing. Although the latest accomplishments in the field of deep learning have seen a lot of success, tabular data extraction still remains a challenge due to the vast amount of ways in which tables are represented both visually and structurally. Below are some of the examples: 

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Invoice Documents

Many companies process their bills in the form of invoices which contain tables that hold information about the items along with their prices and quantities. This information is generally required to be stored in databases while these invoices get processed.

Traditionally, this information is required to be hand filled into a database software however, this approach has some drawbacks:

1. The whole process is time consuming.

2. Certain errors might get induced during the data entry process.

3. Extra cost of manual data entry.

 An invoice automation system can be deployed to address these shortcomings. The idea is to upload the invoice document and the system will read and generate the tabular information in the digital format making the whole process faster and more cost-effective for companies.

Fig. 6

Fig. 6 shows a sample invoice that contains some regular invoice details such as Invoice No, Invoice Date, Company details, and two tables holding transaction information. Now, our goal is to extract the information present in the two tables.

Tabular Information

The problem of extracting tables from invoices can be condensed into 2 main subtasks.

1. Table Detection

2. Tabular Structure Extraction.

 What is Table Detection?

 Table Detection is the process of identifying and locating tables that are present in a document, usually an image. There are multiple ways to detect tables in an image. Some of the approaches make use of image processing toolkits like OpenCV while some of the other approaches use statistical models on features extracted from the documents such as Text Position and Text Characteristics. Recently more deep learning approaches have been used to detect tables using trained neural networks similar to the ones used in Object Detection.

What is Table Structure Extraction?

Table Structure Extraction is the process of extracting the tabular information once the boundaries of the table are detected through Table Detection. The information within the rows and columns is then extracted and transferred to the desired format, usually CSV or Excel file.

Table Detection using Faster RCNN

Faster RCNN is a neural network model that comes from the RCNN family. It is the successor of Fast RCNN created by Ross Girshick in 2015. The name Faster RCNN is to signify an improvement over the previous model both in terms of training speed and detection speed. 

To read more about the model framework, one can access the paper Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

 There are many other object detection model architectures that are available for use today. Each model comes with certain advantages and disadvantages in terms of prediction accuracy, model parameter size, inference speed, etc.

For the task of detecting tables in invoice documents, we will select the Faster RCNN model with FPN(Feature Pyramid Network) as a feature extraction network. The model is pre-trained on the ImageNet corpus using ResNET 101 architecture. The ImageNet corpus is a public dataset that consists of more than 20,000 image categories of everyday objects.  We will therefore make use of a Pytorch framework to train and test the model.

The above mentioned model gives us a fast inference time and a high Mean Average Precision. It is preferred for cases where a quick real time detection is desired.

First, the model is to be trained using public datasets for Table Detection such as Marmot and UNLV datasets. Next, we further fine-tune the model with our custom labeled dataset. For the purpose of labeling, we will follow the COCO annotation format.

Once trained, the model displayed an accuracy close to 86% on our custom dataset. There are certain scenarios where the model fails to locate the tables such as cases containing watermarks and/or overlapping texts. Tables without borders are also missed in a few instances. However, the model has shown its ability to learn from examples and detect tables in multiple different invoice documents. 

Fig. 7

After running inference on the sample invoice from Fig 6, we can see two table boundaries being detected by the model in Fig 7. The first table gets detected with 100% accuracy and the second table is detected with 99% accuracy.

Table Structure Extraction

Once the boundaries of the table are detected by the model, an OCR (Optical Character Reader) mechanism is used to extract the text within the boundaries. The text is then processed using the information that is part of a unique table.

We were able to extract the correct structure of the table, including its headers and line items using logics derived from the invoices. The difficulty of this process depends on the type of invoice format at hand.

There are multiple challenges that one may encounter while building an algorithm to extract structure. Some of them are:

  1. The span of some table columns may overlap making it difficult to determine the boundaries between columns.
  2. The fonts and sizes present within tables may vary from one table to another. The algorithm should be able to accomodate for this variation.
  3. The tables might get split into two pages and detecting the continuation of a table might be challenging.

Certain deep learning approaches have also been published recently to determine the structure of a table. However, training them on custom datasets still remains a challenge. 

Fig 8

The final result is then stored in a CSV file and can be edited or stored according to one’s convenience as shown in Fig 8 which displays the first table information.

Conclusion

The deep learning approach to extracting information from structured documents is a step in the right direction. With high accuracy and low running time, the systems can only learn to perform better with more data. The recent and upcoming advancements in computer vision approaches have made processes such as invoice automation significantly accessible and robust.

About the author:

Prateek Sethi is a Data Scientist working at Mantra Labs. His work involves leveraging Artificial Intelligence to create data-driven solutions. Apart from his work he takes a keen interest in football and exploring the outdoors.

Further Reading:

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