5 Ways Data Has the Potential to Help a Hospital’s Bottom Line
by Heather Hitchcock, on May 26, 2020 9:33:00 AM
The COVID pandemic has exposed many deficiencies in the industry of healthcare, including the ability to adapt to meet clinical needs and still support revenue. As our brave clinicians battle on the front lines, risking their own lives in the process, the hospitals where they provide that life-saving care are duty-bound to do what they can to ensure viability and sustainability.
Traditionally elective procedures and preventive care not only help keep communities healthy, but also contribute to the bottom line impact to a hospital’s revenue and overall mission of providing quality care for all. Slowdowns or cancellations of these procedures, uncertainty of reimbursement for care performed, and the need to act fast without knowing what will happen next have greatly impacted hospitals’ financial strength. So, while no one can argue that this virus has left a path of death and destruction, it has also led to hospitals facing a financial crisis, desperately looking for new ways of leveraging technology to save more lives, drive more impact, and support new cost saving and revenue generating opportunities.
Access to patient data is a key component to supporting hospitals on all levels. Specifically, unlocking data from disparate bedside devices in especially-important critical care areas, where the most crucial data is generated.This is one of the key components of creating a new standard of data-driven medicine and patient-centered care. And while saving lives is priority one, many health systems are already thinking ahead to what technologies they need, and what they can live without, to continue delivering high-quality care while supporting an overall infrastructure that enhances viability for the months and years ahead.
Here are five ways that data could possibly impact a hospital’s bottom line today and well into the future:
1. Impacting Length of Stay
The average ICU length of stay has been reported as 3.3 days, and for every day spent in an ICU bed, the average patient spends an additional 1.5 days in a non-ICU bed. Optimizing and reducing length of stay improves financial, operational, and clinical outcomes by decreasing the costs of care for a patient, not only in facility expenses and supplies, but also in staffing and premium pay. It can also improve patient outcomes by minimizing the risk of hospital-acquired conditions.
Too often, patients end up in the hospital longer than needed because care teams don’t have access to the data needed to determine the root cause of a problem. Take a patient with hypotension, for example: What if providers had the ability to access bedside data on the unit (or remotely via any web-enabled device) along with other patient data? What if having that data would give them the ability to more quickly determine which drug is causing hypotension? And what if once that is determined, the provider would then be able to adjust medications and remotely monitor blood pressure improvement--and potentially get that patient out of the unit and home faster?
If hospitals unlock the physiologic data from the bedside, and integrate it with other data including labs and medications, clinicians could possibly have the information they need to more quickly determine the root cause of someone’s illness—and act faster to provide appropriate treatment.
2. Impacting Readmissions to the ICU
Critical care beds, while most profitable from a revenue perspective, can also be the most costly to a hospital if they aren’t able to be reimbursed. Readmissions back to the ICU can be one reason for declined reimbursements, and are also a major performance indicator of a hospital’s quality of intensive care. Readmissions are also associated with higher mortality and longer hospital stays than for patients without readmission, and therefore introduce higher costs.
When patients are discharged from the ICU to a step-down unit, they are typically not monitored as highly as they were while in critical care, especially during overnight hours when staffing ratios are lower. So, while a transfer out of the ICU traditionally marks an improvement in someone’s condition, it can put these patients at higher risk, resulting in possible readmissions back into the ICU or potentially life-threatening complications. What if clinicians had the ability to remotely monitor these patients on PCs from offices or their homes? What if they could avoid the expense of central stations that typically enable that monitoring? What if remote access could get more eyes on patients and potentially give?
Could giving care teams access to the data needed to help them detect a possible decline possibly support earlier intervention and reduction in readmits back into critical care?
3. Doing More With Less (or, doing more with more data)
Cash flow is a big strain on many hospitals, resulting in the furloughing or lay-offs of many employees. Hospitals are looking for ways to continue delivering high-quality care, but with fewer resources.
Consider this: What if one cardiac surgeon could monitor 20 high-risk, post-op patients from his office or home, no matter what facility they’re recovering in? What if a patient crashes in the middle of the night and that same physician could log in at home to provide consult, saving precious minutes and avoiding the need for a frantic drive to the hospital? What if one respiratory therapist could create a virtual central station of ventilator data and remotely monitor all vented patients across units in the hospital, across different ventilator vendors, in the same view?
Remote, flexible monitoring at scale, across all devices connected to a patient, enables one provider to watch beds across units and across facilities, with access to more information about a patient integrated into a single view than they can even get at a bedside. By doing more with more data, could the hospitals save on FTEs, get provider orders in faster for procedures that could otherwise be delayed or not billable, and possibly save more lives that could be lost due to delays in informed decision-making?
4. Capturing Charges and Saving Manual Labor Time
Proving that care was provided, and exact details on when, is often required for reimbursement as payors seek data to back up those claims. This is already a challenge for hospitals needing to prove a patients’ time on a ventilator to receive reimbursement from payors like CMS, or having to provide copies of ECG strips to prove continuous monitoring orders. The list is long, expensive, and time-consuming, and as new treatment technologies come online, the burden on healthcare systems to prove care provided through data to receive reimbursement is expected to intensify.
What if charge capture and strip charting could be done automatically to reduce manual labor time and expedite reimbursement? What if quality reports could be more automated to expedite reporting and increase rankings?
What about the opportunity for innovation that presents itself with access to data? For example, what if researchers and data scientists could go from idea to publication in a day, rather than spending months or years gathering data to test and prove their theories? What if they could publish faster and increase access to grant funding? What if algorithms could be developed and deployed at scale within an institution to expedite care? What if those same ideas could then be commercialized to provide additional revenue for the hospital and enhance recognition for the institution for the ground-breaking research that AI helped create? By capturing charges at time of service, saving manual labor, and allowing clinicians to harness innovation, hospitals could improve the bottom line and increase opportunities for revenue.
5. Impacting Time to Intervention
Provider shortage is an issue growing in intensity as the gap between the numbers of those skilled in providing care and an increasingly older population needing that skilled care grows. Data published by the Association of American Medical Colleges shows that the U.S. will see a shortage of up to nearly 122,000 physicians by 2032 as demand for physicians continues to outpace supply. Particularly worrying is the lack of specialists: A shortage of somewhere between 24,800 and 65,800 is predicted. The shortage is even more acute in rural areas. How will we reach the physicians needed to quickly act on patient care needs? Finding and accessing specialty care seriously impacts a hospital’s time to intervention, the potential for good patient outcomes, and, ultimately, revenue.
Take, for example, hospitals in less populated areas, where there may be one cardiac specialist in a 200-mile radius. While that physician is struggling to get up and dressed in the middle of the night to make that long drive to see a patient, not only are the patient’s chances of a good outcome decreasing, so is the opportunity for reimbursement. Without provider’s orders for treatments like intervention in a cath lab, many facilities can’t receive reimbursement. However, providers can’t even put in those orders without having the back-up information to justify the procedure. And because most of the bedside data isn’t stored or accessible beyond the bedside, the delays in care and patient risk increase minute by minute, not only impacting patient outcomes but also cost and reimbursement.
Instead, consider the opportunity: What if that same cardiac surgeon that was remoting in from home to see patients in the middle of the night could also access the patient’s history from unit to unit over the past 12 hours, across all connected devices? What if the specialist could quickly and easily access other crucial patient data such as labs and meds, to build a trend, determine root cause expeditiously, and send orders with a click of a button? What if that physician could share that trend, via one click, with another specialist to support remote consult? And what if a surgeon in the O.R. could review all data from the ED, or any other unit, before the patient arrives to prepare for surgery and begin intervention right away?
Time to intervention could be impacted when care teams have remote access to all of the data needed from the bedside along with other patient data from the EMR integrated in the same view for an entire length of stay. Think of the cost of one procedure or one surgery that a facility might recover--and also think of the cost of the additional life that might have just been saved.
Remember this: data, if available and used correctly, could possibly help decrease costs and drive revenue, and help care teams save more lives.
Knowing the most we can about a patient’s condition, both in real-time and for his or her entire length of stay, reduces the chances for a sentinel event. Early prediction and intervention saves lives, each of which is utterly invaluable. But this same life-saving standard of care, supported through as much patient data as care teams need, can also help support hospitals’ bottom lines. What is the cost of one life? Or one hospital? What if having access to more data helped to save more lives, more jobs – perhaps even facilities or systems – that couldn’t be saved before?
MIC is collaborating with Intel on the Scale to Serve program to help hospitals by providing funding for the rapid deployment of Sickbay to help put the power of your data back into your hands and create the foundational architecture you need to create a new standard of care. Apply now.
Want to discuss your data challenges and initiatives for COVID and beyond? Schedule time with one of our clinical advisors today.