Introduction
This particular blog discusses the benefits of an Automation based Invoice Processing in the Administrative & Financial Department of any Healthcare Facility viz-a-viz Clinics, Nursing Homes, Hospitals and other Point-of-Care Facilities. Every Business Department dealing with Services & Financial transactions have long relied on paper invoices to process payments and maintain accounts. Invoice Reconciliation
involves someone spending hours browsing through these documents and populating them into a ledger system. These ledger systems can be from a decade old Windows based application to the current ERP or CRM based applications running on the latest Windows
environment.
Motivation & Objectives
Through the recent advent of Green Revolution, Can the World be a better place with the things being done more efficiently with less wastage of paper and also reduction of man power & hours? The answer to this question lies in the objectives of such an automation.
Taking a cue from the above, the Objectives for such an automation is a multi-folded one.
❖ Reduction in the higher amount of time in doing mundane tasks
❖ Reduction in Carbon / Ecological footprint
❖ Identification of Fraud Invoices or Digitally Manipulated Invoices
❖ To store the Digitized Invoices in repositories for future retrieval.
Solution Pipeline & Methodologies
The process of Digitization of Invoices for Reconciliation follows a typical workflow as given in Figure I.
The entire end-to-end process of handling volumes of invoices by any system can be streamlined by use of a tool under the broadband head of Robotic Process Automation (RPA). The process starts with the conversion of the invoice into some digital variant. Such an approach would relieve the processor from shuttling across different tools or applications to complete the process. This would
further help in reduction in the turnaround time, improvement in accuracy thereby increasing productivity. These RPA based activities can be accomplished by using the Keyboard automation functionality under the TagUI Library for Python. The recommendation would be to scan the invoice and preserving the scanned format in a digital repository. The next step involves the Pre-processing activity dealing with Noise Removal since the scanning of the images can introduce some noise. If we typically look into the next step, we can realize that a more traditional approach would involve reviewers performing this exercise manually. Typically, the reviewers analyse the invoices for errors, read the text and enter into ledger system for storage and retrieval. Now imagine if this particular step is automated, a lot of time can be optimized to yield more fruitful results. This particular step can be achieved using Optical Character Recognition Technique, OCR in short. OCR typically recognizes text and numbers in a document. To achieve this goal, its required to use another package called Tesseract along with the OpenCV to capture the content from the scanned invoices. The next step though an optional one but could be a very important step in some of the rarest events. Consider a hypothetical situation where the Hospital orders some Medical Equipments from a country which speaks a language other than English. The problem over here is a two headed one viz-a-viz the script is in English but it’s actually a different language, for example French, Spanish etc. The second situation could be that the script is in a different language Japanese, Chinese etc altogether. Over here Translating the Language to a standard format becomes an absolute necessity. The OpenCV combines with a package called Text blob library to achieve the task. The next step imposes a far more interesting challenge. Invoice Processing is considered a hugely challenging task since the automation needs to populate the text pieces corresponding to the specific fields like Name of the Payor, Invoice No, Invoice amount etc. The Data Retrieval step does exactly that and the same can be saved in a CSV or an Excel etc. This step can be accomplished by the use of Regular Expressions for extraction of the Key-Value pairs or retrieving the Data in a Named-Entity Format. The final step talks about integrating this Automation model with the Organization’s ERP or CRM systems and going live. This requires building an API around the model that would interact with the organization’s ERP or CRM systems. This actually completes the entire end-to-end pipeline for such an implementation.
Challenges
At the end of the day, Data is only as good as the Analytics Platform you use to manage it. Many healthcare organizations fail to garner any meaningful insights from their data due to poor governance and a lack of any strong foundation. Without proper management and analytic
capabilities, your data’s potential will never be realized. The necessity for a high quality and curated data has only become more apparent as businesses increasingly look to employ fact-based decision making, in their financial operations. Opportunities for improvement and potential bottlenecks only become apparent when you can dive deep into the data and evaluate the underlying trends. This is
where the advantages of an end-to-end analytics solution become more evident & applicable. One comprehensive platform conducting Integration, Rules Management, and Analytics provide the organization with the necessary resources to optimize operations and substantially increases return on investment or financial savings in a lot of daily transactions. This brings a better cash-in flow &
cash-out flow management, value additions into the table leading to Smarter Business and Strategic Decision making.
Future Directions
Fortunately, many of the challenges facing the healthcare invoice reconciliation process can be remedied through the adoption of a data-friendly work environment and support from the Business Leadership team. With proper technology adoption and a realistic yet ambitious Business Intelligence Strategy, many Providers or Healthcare Business Units can drive meaningful changes within their organization. A commitment to changing the company culture and how data is viewed at all levels of operation is paramount to improving patient outcomes or asset management, leading to increasing the ROI in the process as well. Moving forward, Providers or Healthcare Business Units
must take a deliberate look at what their long-term objectives are and what pathways they need to forge to get there. Data Analytics by itself won’t magically fix all the issues impeding Operations or Financial specific goals, but when accompanied by a committed Business Leadership team and a willingness to learn, organizations can make substantial strides in growing and optimizing their enterprise.
Name of Author: DEBAJIT SEN
E-mail id: sen.debajit@gmail.com
debajit_sen@outlook.com
Author Biography:
DEBAJIT SEN has close to 12 years’ experience in the field of Robotic Process Automation, Artificial Intelligence & Data Science, Machine & Deep Learning, Signal & Image Processing and Computer Vision applications. He has implemented numerous projects in the space of Manufacturing, Security & Surveillance and Biomedical & Healthcare Domain. Currently working as a Data Scientist in a Healthcare Organization. Presently, working on the development of RPA based Intelligent Content Processing (OCR & NLP based) for Healthcare Insurance Analytics encompassing automation of Claim Forms Processing, Electronic
Health Record Processing, Prior-Authorization, Dispute and Appeals & Grievances. He has worked as an Independent Researcher / Consultant collaborating for various R & D Projects with different Institutes. He has also published numerous papers in various National & International Journals, Conferences of repute & also has an International Innovation Patent Grant from the Australian Government (IP Australia) to his name for a novel work in the domain of medical science
