Link to original article from HealthcareDive

Dive Brief:

  • Reductions in federal payments to hospitals will total $252.6 billion from 2010 through 2029, reflecting the cumulative impact of a series of legislative and regulatory actions, according to a new study from Dobson DaVanzo & Associates, a health economics and policy consulting firm.
  • The American Hospital Association and the Federation of American Hospitals, which commissioned the study, denounced the impact of the payment reductions on hospitals’ financial health.
  • The report quantified the cumulative impact of payment reductions mandated in 12 legislative acts and numerous regulatory changes from CMS to reach the total dollar amount, which included federal payments for inpatient and outpatient acute care, freestanding inpatient rehabilitation, long-term care hospitals, inpatient psychiatric units, hospital-based rehabilitation, skilled nursing and home health.

Dive Insight:

Hospital advocacy groups have long argued that cuts in Medicare and Medicaid payments hurt the fiscal well-being of their members, and executives reiterated those concerns Tuesday.

“Hospitals are nearing the tipping point we have predicted for so long. The disruptions that come with Medicare and Medicaid cuts of this magnitude have a real-world impact on our ability to deliver the vital services to the patients and the local communities that depend on us,” Chip Kahn, FAH president and CEO said in a statement.

AHA CEO Rick Pollack echoed that concern.

But recent analyses of hospital finances paint a more complex picture. In addition to Medicare spending cuts, hospitals also face declining service volumes, rising costs, and a shrinking payer mix as more baby boomers shift from commercial insurers to Medicare.

A report earlier this year from Fitch Ratings predicts the third straight year of declining profitability for the nonprofit hospital sector. However, the ratings firm also said the decline in profitability won’t be as steep as the previous two years.

Fitch Ratings said that hospital managers who increase operational efficiencies and reduce costs could fare well under Medicare’s reimbursement structure, particularly as the number of older patients — and their typically higher service utilization — increases over the next decade.

A Kaufman Hall flash report earlier this year said that while overall hospital profitability declined for the first time this year in June, some hospitals did well. Hospitals with 500 beds or more saw an increase in pre-tax profit margins for the third consecutive month, which the report attributed to increased revenues. Smaller hospitals (fewer than 25 beds and 200-299 beds) also had improved margins, which was connected to increased inpatient volumes. However, mid-sized hospitals (300-499 beds) saw the biggest decline in profitability, while those in the 100-199 bed range also struggled.

The Dobson DaVanzo study analyzed the total impact of a series of budget acts mandating across-the-board cuts in federal spending, including a 2% reduction in Medicare payments; reductions in Medicare reimbursement to hospitals for bad debt; payment reductions in post-acute care, limitations on inflation-based payment increases in home health; adjustments to Medicare Severity diagnosis‐related groups (MS‐DRGs) payments; reductions in Medicaid disproportionate share payments; and other legislative changes.


What are What-If Scenarios?

What-if Scenario Analysis is a industry term for modeling and simulation techniques used to yield various projections for an outcome, based on selectively changing inputs. A hospital can use what if scenario analysis to see how a given outcome, such as adding/removing a fast track or adding a mid-level provider in lieu of another physician, might affect changes in particular variables, such as patient flow bottlenecks in the ED/Radiology or a decrease in revenue loss from high patient wait times.

Traditional What-ifs are generally compiled through actual human re-enactments, via. spreadsheets or what is endearingly called “the football coach method” which takes place on a story board, light board or worse, the sticky-note on a wall version. Imagine being able to test What-if scenarios in a virtual way before impacting the lives of the people in your department(s) with changes in staffing and patient flow or being able to accomplish in minutes what normally takes hours to months of work with pen, paper, spreadsheets and sticky notes.  Recommendation insights produced by optimization engines show exact solutions, given your current constraints, without creating another bottleneck down the line. Drag and drop to make changes as often as needed or model various design versions without interrupting previous changes.

There are an unlimited number of use case scenarios.  As an example, unexpected gaps in patient flow can have financial ramifications for a hospital while also promoting provider idle time.    More commonly we hear that being over-utilized is the biggest contributor to provider burnout, however, idle time also has a significant impact.  Predictive analytics have proven effective in identifying patients likely to skip an appointment without advanced notice and can now also forecast when that is most likely to occur based on your historic data.

A Virtual “What-if” solution, like the one inside of Bernoulli Optimizer, can be used to model and simulate a hospital’s customized environment, showing where the bottlenecks are going to occur and exactly what to do about them. For instance, deciding on whether to add a mid-level provider or an RN for 6 hours during the busiest times vs. adding a physician or extending the fast track by 1 hour. Each “what if” scenario will predict the change to an emergency department’s performance such as Length Of Stay, Wait Times and Left Without Being Seen. As a nice bonus, the same simulation and modeling engine can continuously look at the last 24 hours of patient inflows to provide a real-time early warning system for potential bottlenecks.

The Data Science Behind Compelling Results:  AI-driven optimization algorithms provide real-time advice on how to schedule staff and other resources to avoid problems. Systems empowered by Machine Learning, AI, Queuing Theory mathematics and Predictive Analytics can also analyze historical patterns (including seasonal) and offer optimization advice down to the hour of the day instead of periodically. Knowing when and exactly how to move or adjust resources reduces wait times and allows more patients to be served. Results that are delivered instantly, help improve patient safety and enhance patient satisfaction in a more efficient manner.

A perfect example: Problem- Reduce LWOT & Length Of Stay for admitted & discharged patients for a hospital with an LWOT stat above the national average.

The optimization engine recommended that the ED transition to a mixed acuity model to allow for pooling of resources.  It recommended a fast track concept and a dedicated fast track team for certain hours and days of the week.  It included specific start/end times for Providers and APPs along with the recommendation for the number of additional beds needed. A suggested a shift change during a month with historic elevations in specific ESI levels was also recommended. Results- The simulation suggested that providers [not nurses] were the bottleneck. Adding a single APP shift change 4 days of the week, significantly improved LWOT% and LOS.  With additional recommendations that were made, the optimized outcome predicted that the LWOT would be reduced to the national average of 2% and the annual cost savings was approximately $1,624,396…taking into account the cost of the flow changes…More info to come about Length Of Stay reduction.

Early Warning Systems: As a nice bonus, Platforms such as Bernoulli OptimizerTM, using the same simulation and modeling engine to continuously look at the last 24 hours of patient inflows, also provide a real time early warning system for potential bottlenecks. They use advanced simulation and flow optimization techniques originally developed for factories and the service industry. Using historical data, as well as, a department flow model, create the perfect schedule to meet each individual department’s needs. Start with the Emergency Department, expand to other departments and continue the optimization.

Demand Intelligent Platforms
 Dashboards are great for at-a-glance reporting and to keep track of how performance evolves, however, it takes a great deal of time to interpret data sets on a deeper level to avoid making operational changes which may cause another chain reaction down the line. Other process optimizations need to happen in communion to reach optimal results that would trickle down to the patient level.  Data is left to interpretation based on best practices, lengthy trials and educated guesses for the future. Spreadsheets, mock-ups and other tools used for these “other optimizations” are rarely tied into the same platform.

This is time-prohibitive when balancing patient care with back-end process and metric refinement.

Often, basic dashboards give one-off visual reports based on data sets requested from an EHR.  These basic benchmarking dashboards are flat and rarely interactive. Traditional dashboards and reports are designed, in theory, to expand the capacity of the human brain but are proving to no longer be enough.  Healthcare leadership needs more than just a dashboard to make sense of the data in a useful way if they are expected to focus on better patient care rather than focusing on how and where the data is coming in and then how to use it. 
Bernoulli Optimization Engine

Traditional Dashboards Now Need A Team To Turn Data Into Wisdom

A consultative team approach is necessary to further expand our brain’s capacity to interpret data in a  more meaningful way.  Ideally, that team  would include a Data Architect to define how the data will be stored, consumed, integrated and managed by different data entities; IT systems and applications, a built-in Data Scientist and an Analytics Consultant or Translator to bridge technical expertise with the operational experts to identify and sharpen the project assignment for business processes.  If we incorporate all of these roles and actions into one solution to support an intelligent dashboard, you would have a very powerful tool that front-line healthcare leaders can use with recommendations for continual, up-to the minute process optimization. Through technological advances in healthcare software solution platforms, these dreams of innovation and data interpretation have become an affordable reality for internal or external lean teams. By automating these roles and processes all in one platform, users can interact on a deeper level with intelligent interpretations of their data through a simple click for additional refinements.

Cropped Bernoulli Optimizer - laptop w office background-1

Artificially Intelligent Platforms Significantly Help Reduce LWBS & Length of Stay 

Innovative and intelligent optimization platforms incorporate advanced analytics and interactive dashboards within their optimization engines to garner unlimited insights based on small reports or large lakes of data.  This can help healthcare leaders navigate a better path in patient flow such as consolidating patient time and acuity data from registration, for continual intelligence reporting of interdepartmental system performance.  An optimization platform like Bernoulli OptimizerTM adds in the capacity to model simple to complex hospitals including multi-track, acuity, mid-levels, scribes and alerts of possible bottlenecks hours before they happen, enabling the Emergency Department to prepare well in advance with workflow improvements.

Leadership can model and simulate the department environment in an intelligent way considering variables such as weather or events and enable virtual “What-if” scenarios with changes to flow for optimal staffing recommendations based on resource restrictions. Insights from sources like this can uncover resolutions to hidden issues. For example, by moving the start time of the midnight shift by 2 hours, you may not see that patient bottleneck mid-morning and may not actually need that agency contractor you were about to hire.  Heatmaps and other analytic reports located in the same system help with the determination of who is coming into the ED and when and can even break it down by ESI level, gender etc. Optimization Engines recommend the best options for refining and streamlining processes to reduce key metrics such as Length Of Stay for admitted or discharged patients, and how to prevent patients from walking out the door before treatment is complete.  For Healthcare leadership and providers this translates to better patient outcomes, streamlined processes and greater satisfaction overall.

Twitter Word Cloud – September 7th-13th

The Top Trending Keywords For Healthcare Related Twitter Searches.