Wockhardt Hospitals CIO Sumit Singh and his team worked for long 18 months to build an automated costing analysis model that not only reduced complexity and saved time but also made the data available in real-time
For healthcare organisations formulating the costing of its various healthcare services, procedures, treatments and surgeries along with the operational costs remains a highly complex and challenging task. The nature of this exigent task can even become extremely intricate for large hospitals that operate in multiple locations with huge infrastructure, staffs and patients.
Wockhardt Hospitals, one of India’s top speciality healthcare networks was exactly facing the same challenging task some two years ago. Wockhardt Hospitals CIOSumit Singh and his team were asked to come up with a technology enabled solution that would help the organisation to simplify its time consuming lengthy and complex manual process of dealing with cost analysis.
Given the complexity of this mammoth problem, CIO Singh and his team worked had to work for almost two years to understand the various aspects and factors of cost analysis before they come up with an in-house technology enabled simple real-timedata based cost analysis model that helped to meet the need of the healthcare organisation.
CIO Singh, who has been in the health and finance industry for over two decades, discusses at length how this entire cost analysis project was executed successfully, right from the initial problem assessment, execution and its technology implementation. Based on this project, Singh also shares some advices and learnings for his fellow CIOs in the industry.
Q1). Tell us about the recent cost analysis project?
Let me give you a brief background for this project, as it was a culmination of few realizations and understanding that preceded it. Each month at Wockhardt, the executive team conducts reviews that touch upon various parameters and operational details. Wockhardt is present in a competitive market and patients have choices to meet their healthcare needs. Many choose not based on quality concerns but on cost and affordability. While we will not compromise on quality and we strive to ensure best of outcomes for our patients, we also want to keep a control on the cost. A tertiary care super speciality hospital like ours offers thousands of different surgeries and procedures at multiple locations. In addition, there are varieties of input costs involved in providing them, making it extremely difficult to figure out the true cost involved for each of the services offered. This is the origin of this project and for which we worked for around eighteen months since mid-2015.
Q2). What kind of challenges you were facing with the entire cost analysis exercise before it was automated?
As I just mentioned, the variety of service offerings and consequence to that the inputs required are extremely large. Therefore, to get to the costing details, which directly impacted the pricing consequently the bottom line for the enterprise, we only did periodically taking one of our units or speciality in a manual mode. It was a laborious exercise and still not necessarily accounted for all possible required cost centres. We thus would at the end of it, get only a rough estimate. Further, both our services and inputs are dynamic, meaning there are changes in them often enough. So the study itself would become redundant soon thereafter. It served its purpose to an extent but left the organisation wanting for more. In the previous situation, microanalysis was just not possible with any accuracy or regularity.
Q3). How did you address those challenges?
It was not easy. Firstly, we must understand, there is no easy formula itself available in healthcare to plug into and use for so many permutations and combinations that we have. And we were not going to be satisfied that gave rough approximations either. Hence, the first task was to create a financial model itself. To begin with, being at multiple locations, we had to standardize nomenclature to start with. Fortunately, some of it we had already done a couple of years ago both in the clinical and the procurement side as a separate exercise. To create a model, we hired an external cost accountant and his team. And we put together a focus team including from Finance and IT. This was put together in the first half of 2015. A hypothesis was put together for model that was expected to be near accurate. The team gathered real data for a subset of the use case and validated the results. It required iterations to get to match the real values. With each small success, the scope was expanded and the process iterated and model tweaked. Finally, we were able to demonstrate the model to work for one of units as a whole for the full scope of services offered. This was a major breakthrough, at least so we thought then.
Q4). What sort of technology innovation was done in-house to automate the costing analysis? Which software solutions/technology was leveraged during the project?
The model and simulation that I talk of above was all manual. It would all turn out to be a nought if we could not automate the entire process and then provide it in a presentable manner. Thus started the real IT heavy lifting. We had to solve two major problems that were related. To provide all the required data as an input to a platform along with an engine that would assimilate the large data set, be able to incorporate the model and then provide the output. Thus began an extensive search of a BI platform that would provide the best fit for our requirements and also our meet our budgetary constraints. This we initiated in January of 2016. After a lot of vetting, conducting a blind POC among vendors and negotiations, we finally made a platform selection. Post that, the real data science and related work began. We have couple of different HIS ERPs, SAP, a separate payroll system besides other sources of semi-structured and unstructured data. The exercise included de-duplication and normalization besides extensive validation. For many we had to create various forms of templates that then became the input and thereafter the entire automation process. Beyond this, it required ETL process in the BI application. Only thereafter we could visually see some first lights. This was the most exciting as well as the tiring phase. This too went through iterations and modifications. And the model also needed some rework as we moved from one unit to next to add more use cases.
Q5). How and when this entire project was implemented? Can you brief about it.
Well, we went in to the production environment towards the end of 2016, but we went through an extensive training and user acceptance testing prior to it. That is a prerequisite for any large IT rollout. When all looked good, we were able to transition quietly first as pre-live phase for a short check in the production environment before letting all authorized users in to start making use of this new platform.
Q6). Lastly, what has been the core benefit of this project and what learnings would you like to other CIO’s?
Firstly, CIO’s must understand that it is their fiduciary responsibility in this digital world to provide real-time information to the rest of the organisation. And for that, they will have to be planning much ahead of a need and must have a longer horizon. Get into as many functions in your organisation as possible so you are in tune of their inner workings. After all as a CIO, we have to serve the needs of all departments of the organisation. Coming to data analytics, understand the data science field and plan ahead. Spend a lot more time designing a solution right with a longer perspective. Get expert help whenever necessary. Manage change in a collaborative manner as analytics not only throws new light and gives fresh insight, it is relatively new. But they will find many new facets hidden inside the mountain of data they already manage. Rest I think they can hold on their own.
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