The layers of tech and data used by an institution such as Franciscan Health, a 12-hospital system in Indiana that also has a presence in suburban Chicago, can need a bit of decluttering for sake of efficiency.
The path to sort out data and other aspects of observability led the health system to observability platform Pantomath.
Sarang Deshpande, vice president of data and analytics for Franciscan Health, says when he joined three years ago, he saw that — much like other healthcare providers — they operated with a combination of tools and technologies stacked together. That approach may have served in the moment, choosing the best tools available at the time, he says. It also piled up a bit of confusion.
Diagnosing the Problem
As with other types of long-running institutions, hospitals might not move swiftly when it comes to technology adoption. “The maturity typically you’ll see on the provider side around technology … digital adoption is lower than you would find in manufacturing or even on the healthcare side if you think of pharmaceuticals or medical devices,” Deshpande says.
On the nonprofit side, he says, the main focus is patient care with most capital investments going into buildings, hospitals, and clinics rather than new tech. At least that may have been the case until the pandemic put the world on different footing. “Technology tends to lag a little bit, but after COVID that has changed quite a bit,” Deshpande says.
Prior to COVID, Franciscan Health tended to purchase technology tools based on what was needed at the time, he says, and largely on-premises. Compounding the complexity, Deshpande says there is a plethora of ways data is collected and ingested in the hospital system. “Our electronic medical record system is the biggest of all where most of our patient data comes from,” he says.
On top of that, he says there are also billing and ERP systems, ITSM ticketing systems, and time-keeping systems to account for. Further, there are regulatory requirements around the hospital system’s reporting, he says.
Assessing the Tech Ailment
Information that Franciscan’s system ingests, Deshpande says, includes flat file datasets, as well as data from a CMS, third-party payers, or ancillary third parties. With so many formats and inputs, he says there was not a very clear-cut way to access data. Franciscan Health must also be accountable for sending information out, Deshpande says.
The varied tech tools Franciscan Health collected over the years meant there was no standardized data pipeline. “That problem was very obvious to me from the get-go,” Deshpande says. “We have tried to solve it through people and process to a large extent, but there’s only so much you can do when there are siloed teams that are accountable for one piece of the data flow.”
With so many pieces and layers in play, tech challenges were inevitable. “Whenever there was a failure or a data quality issue or a job didn’t run on time or got delayed, the downstream impact of that was very localized,” Deshpande says.
Being accountable for accuracy, timeliness of the data, he says the issues became apparent to him. “That’s where we realized we had a big problem where the non-standardized set of tools, processes, and people in their jobs were making it very difficult for us to have any level of accuracy that our leadership demands of us,” he says.
In the digital transformation era, with migrations to the cloud and more automation, Deshpande says post-COVID resources were extremely limited and most every health system seeks to do more with less. “Labor costs are off the charts,” he says. “I think that’s where most people are realizing that we need to leverage not just technology at the frontlines for our patients, but also for optimal work internally.”
Prescribing a Strategy
That’s where the observability platform Pantomath came into play to help transform Franciscan Health’s data operations. Deshpande says use of the platform introduced automation with the intent to reduce human error and dependency in the equation. “We will always need eyeballs on things to validate, verify, and to fix,” he says, “but basic monitoring, observation, alerting and things of that nature should be very easy to automate. Things are never as easy it seems.”
Use of the platform let Franciscan Health repurpose their labor force to work smarter through AI and LLMs, Deshpande says. “We wanted a more consistent way of monitoring and solving the problem of data accuracy, data currency, and data validation.”
Franciscan Health’s system comprises five different regions, he says, that historically were separate entities that came together through mergers and acquisitions. They still operate relatively independently from a daily workflow perspective, says Deshpande. That includes management of staff and patient population.
Deshpande says one measurement for success of the observability effort is whether his team can conduct business, grow, and transform at the same time without additional labor — and still deliver. He says the work continues, with at least two years out in terms of migrating all on-prem infrastructure while also building new use cases on the data platform. “The next couple of years will be all about migration, consolidation, and how can we get to a point where this modern data platform in the cloud will be up and running and we can reduce our footprint in the data center and the cost that comes with it,” Deshpande says.