Omnia Health is part of the Informa Markets Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them. Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

A Healthcare C-Suite Powered by Artificial Intelligence

Article-A Healthcare C-Suite Powered by Artificial Intelligence

Artificial Intelligence.jpg
A look at the advantages an Artificial Intelligence empowered CEO gets when it comes to running healthcare organisations.

Using Artificial Intelligence (AI) to enable predictions related to diagnosis and prognosis of patients is fast becoming an accepted reality. But can AI be used to make predictions for the C-Suites that run and manage healthcare organisations?

The numerous factors that determine commercial outcomes in healthcare can be mapped over time, whereby creating an evident case for AI-empowered C-Suite. We have reasons to believe that AI will separate the proverbial wheat from the chaff when it comes to CEO’s in the coming years. Predictability is perhaps the biggest challenge that any business faces when it comes to sustenance and growth. AI addresses just that.

Think of this as a crystal ball that a CEO can gaze into and get a glimpse of the times to come. Then compare this with another CEO who is still making decisions on gut feeling and depends upon prehistoric tools to grapple with business challenges.

What is the undue advantage that an AI-empowered CEO gets when it comes to running healthcare organisations? Here are a few examples:

Prediction of revenue

AI algorithms can, over a period of time, predict the earnings of an organisation. AI takes it beyond the usual approach of historical data analysis. The predictions can be across various cross-sections of revenue. For example, AI can be used to predict the revenues from different departments and services.

Calculating payor rejections and payments in advance

The revenue cycle is a crucial component for all healthcare providers. Hospital management is always better off if the claim realisation can be predicted both in terms of value and time. There are numerous gains to be made if the management team knows what kind of money is expected from the payors in which month. Additionally, providers can run an AI tool to predict the outcome of claims that they are submitting each month. Imagine if a hospital CFO can know that out of the 5,000 claims being submitted for a particular amount an ‘x’ amount will get a first level rejection. Further, the CFO can also get to know which claims will be sent back by the payor and for what reasons. AI can do this faster and at larger quantities than the claim scrubbers.

Patient mix and volume forecasts

A multi-geography healthcare group, as well as a single hospital, can benefit a lot if the team running it knows the kind and numbers of patients that will walk through the doors. Knowledge of the medical conditions for which there will be more demand can help in planning the services much in advance. Inventories can be stocked accordingly, and capacities can be made ready. With advanced AI algorithms, it will also become possible to forecast the geographies and ethnicities of future patients for a medical condition. This helps the healthcare provider to be future-ready.

Moreover, the kind of revenues that the group of the hospital will make can be known in advance.

Manpower requirement

A spin-off from forecasting patient mix and volumes is the enhanced ability to plan manpower and resources. When the leadership team is near certain about the future, it can plan the manpower required well in advance. This advantage is bigger in areas where the hiring process for the technical staff is longer owing to licensing and other regulatory requirements.

Like all new endeavours, C-Suite AI has a learning curve too. It will be a while till the accuracy of predictions improves. However, there are some factors that can contribute towards a steeper learning curve.

Sufficiency of variables

It takes sufficient, if not a comprehensive list of variables to predict a business outcome. For AI to work better for the CXO’s, the data variables will need to be listed in totality. Missing out on key factors that can influence an outcome can compromise the AI prediction.

The accuracy of data

Algorithms are based on data. It is imperative for the input data to be accurate in order to get forecasts that are closer to reality. C-Suites investing in AI will need to assure that they have a system of obtaining and cross verifying data that goes into creating the sets of rules.

Timeliness of data procurement

Sometimes crucial information and data come in too late for it to be valuably used. The usefulness of AI is amplified if the predictions can be made earlier. This is possible only if the data is obtained well in time and is sanitised quickly.

How many variables can be directly influenced?

The CEO and the management team can take preventive steps and manage the business outcomes proactively only if they can control the variables that influence those outcomes. The usefulness of C-Suite AI is directly linked to the ‘influenceable’ variables in the mix. The CEO’s will need to expand their span of control over time and start controlling more elements so that they can make better use of the AI predictions.

Overall, though C-Suite Artificial Intelligence is new to the world, it has all the potential to enable a powerful and proactive C-Suite. We can safely conclude that an AI-powered CEO will have a decisive edge over a traditional one.

OH mag issue 3_small.jpgThis article appears in the March/April edition of Omnia Health Magazine. Other topics include AI in healthcare, patient safety, mobile healthcare and further updates around on COVID-19 from the healthcare industry.

Read the magazine online today >>

Hide comments


  • Allowed HTML tags: <em> <strong> <blockquote> <br> <p>

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.