The Trumpcare dilemma: Delivering growth during uncertainty
Given the uncertainty and financial pressures hospitals face, leadership must invest in resources that are going to drive massive returns– regardless of changes that may come down the line.
The inability of Congress to pass the new "Trumpcare" plan, formally known as the American Health Care Act, has injected an unknown element for healthcare providers and patients alike. Estimates suggested that 24 million Americans could lose their insurance under the proposed policies, but these plans seem to be on hold– for now. What remains is the urgent need to deliver quality healthcare at lowered costs, while ensuring patients have the best experience possible.
With or without new federal mandates, providers and care organizations need to provide healthcare to the millions of uninsured and underinsured patients that need access to these crucial services. Similarly, organizations must find ways to provide optimum care while facing uncertainty about reimbursements. Because of these factors, the push for higher efficiencies in the acute space is more prescient than ever, and hospitals are racing to find the most actionable ways to achieve these goals.
Two such opportunities currently exist to achieve this aim: 1) focusing on the basic elements of care delivery to add efficiencies to how care is provided to patients and 2) applying artificial intelligence (AI) to the terabytes of data that already exist to support care, giving hospitals the opportunity to add efficiencies to the patient flow experience.
Cutting Down on Waste
Hospitals need to create a way to reliably and consistently execute on care delivery tasks. As an industry, we must move towards a smarter management style that prioritizes quality, data-validated processes, and above all else, keeps the patient in the forefront of decision-making.
In particular, the Emergency Department, Operating Room, and inpatient wards are all key areas that impact a hospital's costs and can significantly benefit from reduced waste– as demonstrated by hospital spending reaching nearly $1 trillion dollars and representing a third of all healthcare costs in the U.S.
This idea of optimized operations is wholly dependent on eliminating inefficiencies such as surgical no-shows, cancellations, and delays. To an outsider, these may seem like small inconveniences, but hospital management will attest that they are costly problems that greatly detract from the well being of both clinical staff and patients. Therefore, the push to identify and rectify these operational carepath challenges should remain top of mind, even as payment models continue to evolve.
Efficiency and the Care Path
Clinical teams are incredibly well educated and prepared to deliver outstanding care. However, the operational path that supports them is laden with inefficiencies. It's imperative that we define and achieve operational efficiency to improve the optimal care path: the movement of the patient from A (intake) to B (discharge).
Part of this is equipping caregivers with the insights needed to focus solely on their patients, while eliminating the "noise" and complications related to discrepancies in the care continuum– i.e. too few nurses on hand to manage an influx of patients in the ED.
Further, the ability to fix these issues must be achievable in real-time– without an intensive review process– to be effective. Longitudinal studies can point to the reason that certain surgical care teams take longer to perform a difficult procedure, but unless we're giving them the ability to improve now, it's ultimately an inefficient exercise.
By better understanding root causes behind problematic areas in the hospital– such as times associated with high-patient volumes, a lack of recovery beds, or patients at risk for falls– we can set corrective actions and make sure the entire system runs smoother.
From Practice to Action
Core to the operation of any hospital is information– each clinical interaction must be clearly documented, stored, and easily retrievable. Because hospitals see hundreds of thousands of patients per year, this means there are literally terabytes of useful information for our industry to examine and draw inferences from. The next step is taking what we've learned and applying it in real-time.
Hospital leadership should strive to achieve operational efficiency by not only mining this data, but applying these insights to their own priorities. This involves utilizing a real-time system to proactively address issues and cut down on negative and costly outcomes.
For example, one of the hospitals my company partners with saw significant reductions in misestimated surgeries and same day cancellations by focusing on pivotal areas and persuading staff to proactively contact patients prior to surgery. This allowed them to utilize the same fixed assets (their operating rooms) and enabled more patients to access treatments, in the process lowering their total cost of care.
Another of our partners reduced unnecessary testing in their ERs, but in the process lost short-term revenue on some of their contracts due to a reduction of total tests performed under these agreements. However, the lower utilization rates set them up perfectly to earn business from many self-insured employers in their area who care about lowering costs, and were eager to partner with a hospital who took a data-validated approach to curbing unnecessary testing.
Finally, two more of our partners have reduced falls-related safety events while improving patients' safety and experience– further reducing the total cost of care. They've achieved these efficiencies primarily by integrating data-driven insights into their falls prevention programs to provide real-time information directly to the care provider.
In each case, the hospital was able to leverage existing data to clearly identify what specific operational factors were hurting outcomes. This allowed them to enact changes that made a difference in weeks, if not days.
The differentiator between successful hospitals and those that struggle will be a focus on independently improving operations regardless of the policy noise. Part of this is using their data, the aforementioned terabytes of information, to consciously streamline the way they manage operational processes that impact patients and resources.
There is a reason that "AI" is such a buzzword in healthcare right now. Machine-learning technologies allow us to pull relevant, actionable insights from the terabytes of data that hospitals collect every minute of every day. It is through these technologies that we can recognize and achieve incremental improvements that solve real problems across the care experience.
Finding value in data will be the key to enhancing and thriving no matter what happens with the Affordable Care Act or AHCA. The right information already exists within many hospitals' systems. Operational efficiency and tools that tap into these systems to make data actionable will move the biggest needle on hospitals' bottom lines and patient experience.
As we move into 2017 and beyond, it will be clear which healthcare organizations have learned this lesson or not.
About Mudit Garg: Mudit Garg serves as the CEO and Founder of Qventus, one of the leading prescriptive analytics firm applying a software based "Air Traffic Control" for health systems and hospitals. Aimed at enhancing efficiency, outcomes and experience at the $900 Billion U.S. hospital market and backed by premier investors such as Y Combinator in Silicon Valley, Mudit leads a team of technologists and healthcare operators to build "closed loop" solutions that use machine learning and prescribe actions from real time data to the front line at leading hospitals such as Stanford Children's Hospital, Sutter Health, El Camino Hospital, and Mercy Health System
The views, opinions and positions expressed within these guest posts are those of the author alone and do not represent those of Becker's Hospital Review/Becker's Healthcare. The accuracy, completeness and validity of any statements made within this article are not guaranteed. We accept no liability for any errors, omissions or representations. The copyright of this content belongs to the author and any liability with regards to infringement of intellectual property rights remains with them.
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