Past Trends and Future Forecasts in a Volatile Healthcare Market

Past Trends and Future Forecasts in a Volatile Healthcare Market Amy Lebrecht, Department of Business & Network Development, Lehigh Valley Health Network Abstract There is a large inpatient decline occurring across the nation today. As a commercialized population demands a convenient healthcare option, inpatient stays are becoming a thing of the past. The evidence of this is extremely prevalent in Pennsylvania, as an 11% decline in inpatient discharges has occurred since 2006. Hospitals need to understand the reasons for this decline in order to survive the profit loss from treating fewer inpatients. The best ways to thrive in the current market are to emphasize overall population health, expand into various outpatient practices and to increase the value, not the volume, of inpatient visits. The Lehigh Valley Health Network has been implementing these solutions, and has grown almost 30% since 2006. In the future, inpatient discharges within Pennsylvania are going to continue to decrease. The decline will slow and level out eventually, but it is impossible to know when that will occur. LVHN inpatient volumes have a more uncertain future, but it appears the overall market decline is catching up, and the growth has slowed. Background Beginning in approximately 2006, the quantity of inpatients seen within the United States began decreasing. Understanding the cause of this change is an important part of being able to thrive within it. The best explanations begin with a fundamental consumer change. As the population becomes more and more commercialized, patients demand convenience. This leads to a shift from inpatient to outpatient treatments as new technology allows former inpatient surgeries to be completed more easily and in a shorter time period. In the words of Brian Silverstein, M.D., vice president of Sg2, a Chicago research and consulting firm, “Many hospitals have put outpatient services on the front burner. The hospital profit base 10 years ago was 64 percent inpatient and 35 percent out. Today that’s flipped,” (Olson, 2015, para. 3). The focus of healthcare is also changing as a new emphasis is being put on quality instead of quantity. Hospitals are moving “from a system that thrives on illness to one that rewards health and wellness,” (Macfarlane, 2014, para. 13). Unfortunately, none of these changes are inexpensive. The new technologies that allow for this evolution are costly, making it difficult for isolated hospitals to survive on inpatients alone. The healthcare market today is very different than what it was even five years ago, so the methods to thrive in it must change as well. A new focus needs to be placed on population health, and keeping people healthy instead of curing the sick. Singular hospitals will not flourish alone; support is needed throughout the entire healthcare system. For years, the trend was hospital-to-hospital mergers and acquisitions. Today, it\u27s vertical integration. It\u27s, \u27Let\u27s buy the whole value chain\u27 — from home care to hospice to skilled nursing — and manage the entire ecosystem,” (Vesely, 2014, Managing the entire ecosystem section, para. 5). Within hospitals themselves, a new dedication to value needs to be seen. Value essentially is “delivering the best possible outcomes at a given level of cost,” (Smith & Ricci, 2015, Cost Savings vs. Quality Care section, para. 3). The goal should be for a health network to include more “covered lives” (Adamopoulos, 2014). Each of these lives can be taken care of throughout all stages of life, in hopes of identifying, and solving, health problems before they would reach an inpatient level. Essentially, an ideological change is occurring throughout this industry. The new business model is “one that is transforming the delivery system from hospital-centric sick care to a super outpatient model that will emphasize community-based care,” (York, Kaufman & Grube, 2015, para. 1). The only way to thrive in the modern day healthcare market is to embrace these ideals, and change with the times. Past Trends Pennsylvania Nationally a decrease in inpatients is occurring, and this decline is extremely evident within Pennsylvania hospitals. Between 2006 and 2013, an 11% decrease in the quantity of inpatient discharges and a 13% decrease in the utilization rate occurred throughout the state. This decline is seen in all age groups, and almost all product lines. The exceptions to the rule are Rehabilitation and Orthopedics, both of which have shown positive movement in recent years. This decline is fueled by those ages 65 and older. This age group accounts for almost half of all Pennsylvania patients, but has a utilization rate that is decreasing rapidly. It dropped from 41% to 32% in the span of eight years, resulting in approximately 20,000 less patients each year. This older age group is predicted to grow significantly in the coming years as the Baby Boomer generation ages. This leads to a greater pool of people to draw from, but less and less of them are having inpatient stays. Lehigh Valley Health Network This network, unlike the rest of the country, is growing. As the state of Pennsylvania experienced an inpatient decline of 11%, LVHN inpatient bed utilization grew by 29% between CY06 and CY13. The growth is seen throughout all age groups, though a slightly different age distribution is seen in LVHN compared to the state. 38% of LVHN patients are 65+, which is lower than the state average. Not all product lines have seen such a steady pattern of growth. Some have grown, some have decreased, and many experience large jumps or drops in quantity from one year to the next. Though the individual product lines are not extremely smooth and predictable, the total of them all creates a curve with steady growth. Future Forecasts Future Direction In general, Pennsylvania inpatient quantities are going to continue to decline. Since it reached its maximum of 1,891,905 inpatient discharges in 2006, the decline has not slowed. The lowest quantity of inpatients since these records began occurred in the most recent year, 2013, at 1,683,097 discharges. While making predictions from this very smooth curve is not difficult, knowing when the curve will change is. The decline cannot continue forever because there will never be a day where there are zero inpatients in Pennsylvania hospitals. At some point the decline will slow, and the curve will level out. Unfortunately, there is no way of predicting when this will happen. Because of this, it’s very difficult to give any forecasts for more than a year or two into the future. LVHN inpatient discharge quantities have a more uncertain future. Though the network has experienced significant growth in recent years, it did experience its first decline since FY01 in FY14. This being said, the decline was by only 82 patients, or 0.1% of the total volume, so it could be an abnormal year and the upward trend could continue within FY15. It also could be a sign that no one is immune to the market’s decline forever. If the state of Pennsylvania is any indicator, the quantity of inpatient discharges should begin decreasing soon. This decline should not be seen as a bad thing though because there is more to the network than just the inpatients. The quantity of same day surgeries, an outpatient classification where many inpatient surgeries are shifting to, increased 28% between FY11 and FY14. This shows that the hospital is changing with the times. A decline may be occurring in one area, but a different area is growing in return. Methods of Forecasting In order to forecast future volumes for both the state of Pennsylvania and LVHN, many methods are used. These include averaging the distance between values on different intervals, different polynomial regressions, and using the last change that occurred in the data. These calculations then provide approximately six different predictions for the next data point. By looking at an average and a median of these values, two realistic numbers are obtained. Accuracy can be evaluated for these forecasts, even though they are in the future. For the state of Pennsylvania, predictions are made for calendar year 2014 and 2015. Three of four quarters of 2014 data are available for analysis, so by multiplying the yearly forecast by the percent of all inpatient discharges that normally occur in the first three quarters, we have a number we can compare to an actual. This allows percent error to be calculated, and accuracy to be quantitatively evaluated. All of these calculations are worked out for both the average and the median. The values utilized in the end are the values that had the lower percent error for 2014, whether it was the average or the median. The first two quarters of LVHN FY15 data is available as well, so a similar method of comparison can be used to end with one single value. Forecasts For Pennsylvania: All Inpatients: CY13 Actual: 1,683,097 CY14 Forecast: 1,642,581 CY15 Forecast: 1,564,351 By Age Group: Ages 18-44*: CY13 Actual: 229,662 CY14 Forecast: 221,529 CY15 Forecast: 210,934 Ages 45-64*: CY13 Actual: 435,486 CY14 Forecast: 427,053 CY15 Forecast: 407,670 Ages 65+*: CY13 Actual: 674,764 CY14 Forecast: 656,570 CY15 Forecast: 612,899 By Product Line: Cardiac Services: CY13 Actual: 205,345 CY14 Forecast: 194,685 CY15 Forecast: 179,330 ENT: CY13 Actual: 21,520 CY14 Forecast: 21,642 CY15 Forecast: 20,043 General Medicine: CY13 Actual: 594,055 CY14 Forecast: 581,946 CY15 Forecast: 551,491 General Surgery: CY13 Actual: 122,803 CY14 Forecast: 119,496 CY15 Forecast: 113,417 Gynecology: CY13 Actual: 18,800 CY14 Forecast: 16,176 CY15 Forecast: 13,506 Neurology CY13 Actual: 83,304 CY14 Forecast: 81.263 CY15 Forecast: 76,885 Neurosurgery: CY13 Actual: 12,769 CY14 Forecast: 12,595 CY15 Forecast: 12,151 Ophthalmology: CY13 Actual: 2,213 CY14 Forecast: 2,046 CY15 Forecast: 1.793 Orthopedics: CY13 Actual: 122,600 CY14 Forecast: 122,693 CY15 Forecast: 118,517 Rehabilitation: CY13 Actual: 25,802 CY14 Forecast: 26,725 CY15 Forecast: 26,744 Spine: CY13 Actual: 43,041 CY14 Forecast: 42,070 CY15 Forecast: 39,359 Thoracic Surgery: CY13 Actual: 13,133 CY14 Forecast: 12,787 CY15 Forecast: 12,016 Transplant: CY13 Actual: 2,827 CY14 Forecast: 2,813 CY15 Forecast: 2,800 Trauma: CY13 Actual: 17,821 CY14 Forecast: 17,327 CY15 Forecast: 16,191 Urology: CY13 Actual: 25,497 CY14 Forecast: 24,210 CY15 Forecast: 22,258 Vascular Services: CY13 Actual: 34,221 CY14 Forecast: 32,416 CY15 Forecast: 19,813 For the Lehigh Valley Health Network: All Inpatients: FY14 Actual: 73,362 FY15 Forecast: 74,130 FY16 Forecast: 74,115 By Age Group: Ages 0-17: FY14 Actual: 8,682 FY15 Forecast: 8,802 FY16 Forecast: 8,922 Ages 18-44: FY14 Actual: 15,549 FY15 Forecast: 15,755 FY16 Forecast: 15,993 Ages 45-64: FY14 Actual: 20,185 FY15 Forecast: 20,374 FY16 Forecast: 20,495 Ages 65+: FY14 Actual: 28,946 FY15 Forecast: 29,318 FY16 Forecast: 29,267 By Product Line: Cardiac Services: FY14 Actual: 7,779 FY15 Forecast: 6,619 FY16 Forecast: 4,782 ENT: FY14 Actual: 943 FY15 Forecast: 933 FY16 Forecast: 913 General Medicine: FY14 Actual: 18,594 FY15 Forecast: 17,810 FY16 Forecast: 16,747 General Surgery: FY14 Actual: 6,871 FY15 Forecast: 7,025 FY16 Forecast: 7,207 Gynecology: FY14 Actual: 368 FY15 Forecast: 250 FY16 Forecast: 142 Neurology: FY14 Actual: 2,754 FY15 Forecast: 2,452 FY16 Forecast: 2,150 Neurosurgery: FY14 Actual: 481 FY15 Forecast: 481 FY16 Forecast: 475 Ophthalmology: FY14 Actual: 77 FY15 Forecast: 70 FY16 Forecast: 58 Orthopedics: FY14 Actual: 4,482 FY15 Forecast: 4,567 FY16 Forecast: 4,711 Spine: FY14 Actual: 1,711 FY15 Forecast: 1,662 FY16 Forecast: 1,647 Thoracic Surgery: FY14 Actual: 492 FY15 Forecast: 490 FY16 Forecast: 472 Transplant: FY14 Actual: 368 FY15 Forecast: 393 FY16 Forecast: 432 Trauma: FY14 Actual: 1,434 FY15 Forecast: 1,336 FY16 Forecast: 1,223 Urology: FY14 Actual: 6,500 FY15 Forecast: 6,692 FY16 Forecast: 7,287 Vascular Services: FY14 Actual: 1,114 FY15 Forecast: 1,084 FY16 Forecast: 1,064 Note: A forecast for PA Ages 0-17 is not included. While a general downward trend is present for this curve, there is no real pattern from one data point to the next, making it difficult to have any sort of accurate prediction. *Does not include normal newborns or deliveries. Accuracy Discussion The Pennsylvania forecasts were made with a high degree of accuracy. When the percent error was calculated comparing actual values to predicted values, for 14 of 17 product lines the percent error was less than two percent. The percent error for all inpatient totals was approximately 0.2%. Unless a large change occurs within Pennsylvania’s inpatient market in the next two years, the forecasts made will be relatively accurate. Lehigh Valley Health Network forecasts are harder to state with certainty. The percent error for these forecasts tends to be much higher than the forecasts for the state. The percent error for LVHN forecasts average around 10%, but can be as high as 44%. LVHN data is much more irregular than the state data. Even just one doctor switching locations can add or subtract thousands of patients from observed inpatient discharge volumes. These large changes are common, and can make accurate forecasting very difficult. Conclusion: As the healthcare market changes, it is essential that hospitals and health networks change with it. The quantity of inpatients being discharged from hospitals is declining nationwide, and the only way for hospitals to remain profitable is to realize this, alter their policies, and adapt. In order to grow in the future, hospitals need to emphasize population health, expand into outpatient practices, and emphasize the value of each inpatient visit they do receive. These techniques will not stop the decline, but will give hospitals other ways to earn money. They need to shift their business instead of losing their business. The inpatient numbers seen within the state of Pennsylvania are going to continue to decrease, but that’s not a bad thing. As long as hospitals, including the Lehigh Valley Health Network understand this and plan for it, the healthcare industry can continue to be a growing one. References Adamopoulos, H. (2014, September 29). Hospital ratings: What the shift from inpatient to outpatient means for performance measurement. Becker’s Hospital Review. Retrieved from http://www.beckershospitalreview. com/finance/hospital-ratings-what-the-shift-from-inpatient-to-outpatient-means-for-performance- measurement.html Brimmer, K. (2014, February 24). The future of hospital inpatient volumes. Healthcare Finance. Retrieved from http://www.healthcarefinancenews.com/news/future-hospital-inpatient volumes Brown, B. Top 7 Healthcare Trends and Challenges for 2015: From Our Financial Expert. HealthCatalyst. Retrieved from https://www.healthcatalyst.com/top-healthcare-trends-challenges 2015 Butterfield, S. (2014, August). For hospitalists, the times are changing. American College of Physicians Hospitalist. Retrieved from http://www.acphospitalist.org/archives/2014/08/changingtimes.htm Grube, M., Kaufman, K., & York, R. (2013, March 8). Decline In Utilization Rates Signals A Change In The Inpatient Business Model. Health Affairs. Retrieved from http://healthaffairs.org/blog/2013/03/08/decline- in-utilization-rates-signals-a-change-in-the-inpatient-business-model/ Kaufman, K. (2012, April 13). Bending The Health Care Cost Curve More Than Meets The Eye. Health Affairs. Retrieved from http://healthaffairs.org/blog/2012/04/13/bending-the-health-care-cost-curve-more-than- meets-the-eye/ Macfarlane, D. (2014, September 3). The squeeze is on for U.S. hospitals. Medshpere. Retrieved from http://www.medsphere.com/billings-blog/the-squeeze-is-on-for-us-hospitals Munroe, D. (2015, January 4). U.S. Healthcare Spending On Track To Hit $10,000 Per Person This Year. Forbes. Retrieved from http://www.forbes.com/sites/danmunro/2015/01/04/u-s-healthcare-spending-on-track-to-hit-10000-per-person-this-year/ O’Dell, G. J., Aspy, D. J., & Jarousse, L. (2015) AHA Environmental Scan. B. E. Smith. Retrieved from https://www.besmith.com/thought-leadership/articles/2015-aha-environmental-scan Olson, K. (2014). Outpatient Outlook. HealthLeaders magazine. Retrieved from http://www.healthleadersmedia.com/content/LED-86466/Outpatient-Outlook Smith, D. & Ricci, C. (2015). Healthcare Trends 2015. B. E. Smith. Retrieved from https://www.besmith.com/thought-leadership/white-papers/healthcare-trends-2015 Vesely, R. (2014, March 11). The Great Migration. Hospitals & Health Networks Magazine. Retrieved from http://www.hhnmag.com/Magazine/2014/Mar/cover-story-great-migration Wise, W. (2012, January 14). Thoughts on Future Healthcare Trends. Western Pennsylvania Healthcare News. Retrieved from http://www.wphealthcarenews.com/thoughts-on-future-healthcare-trends/ York, Kaufman, & Grube. (2014, January 6). Where Have All The Inpatients Gone? A Regional Study with National Implications. Health Affairs. Retrieved from http://healthaffairs.org/blog/2014/01/06/ where-have- all-the-inpatients-gone-a-regional-study-with-national-implications

On the road for change: An in-depth analysis and future recommendations

In the 2016-2017 school year, James Madison University\u27s Dux Leadership Center in collaboration with the Madison Collaborative: Ethical Reasoning In Action program created the On the Road for Change Alternative Spring Break Program. This innovative experience focused on growing ethical leadership skills within 15 student participants through six workshops, an alternative break and a re-orientation session. In order to understand if this program was successful in helping students become better ethical leaders, an in-depth assessment program was implemented including learning objectives, pre and post tests, journal entries and interviews. This thesis expands upon the creation, content included, results of and overall effectiveness of the On the Road for Change program. Through examination of the gathered data, the researcher found that almost all learning objectives were met in the 2016-2017 iteration of the program. The researcher also noted places where improvements can be made, and makes several recommendations on how to implement the program more effectively in future years

Response Time Approximations in Fork-Join Queues

Fork-join queueing networks model a network of parallel servers in which an arriving job splits into a number of subtasks that are serviced in parallel. Fork-join queues can be used to model disk arrays. A response time approximation of the fork-join queue is presented that attempts to comply with the additional constraints of modelling a disk array. This approximation is compared with existing analytical approximations of the fork-join queueing network

Queueing network models of zoned RAID system performance

RAID systems are widely deployed, both as standalone storage solutions and as the building blocks of modern virtualised storage platforms. An accurate model of RAID system performance is therefore critical towards fulfilling quality of service constraints for fast, reliable storage. This thesis presents techniques and tools that model response times in zoned RAID systems. The inputs to this analysis are a specified I/O request arrival rate, an I/O request access profile, a given RAID configuration and physical disk parameters. The primary output of this analysis is an approximation to the cumulative distribution function of I/O request response time. From this, it is straightforward to calculate response time quantiles, as well as the mean, variance and higher moments of I/O request response time. The model supports RAID levels 0, 01, 10 and 5 and a variety of workload types. Our RAID model is developed in a bottom-up hierarchical fashion. We begin by modelling each zoned disk drive in the array as a single M/G/1 queue. The service time is modelled as the sum of the random variables of seek time, rotational latency and data transfer time. In doing so, we take into account the properties of zoned disks. We then abstract a RAID system as a fork-join queueing network. This comprises several queues, each of which represents one disk drive in the array. We tailor our basic fork-join approximation to account for the I/O request patterns associated with particular request types and request sizes under different RAID levels. We extend the RAID and disk models to support bulk arrivals, requests of different sizes and scheduling algorithms that reorder queueing requests to minimise disk head positioning time. Finally, we develop a corresponding simulation to improve and validate the model. To test the accuracy of all our models, we validate them against disk drive and RAID device measurements throughout

Validation of Large Zoned RAID Systems

Building on our prior work we present an improved model for for large partial stripe following full stripe writes in RAID 5. This was necessary because we observed that our previous model tended to underestimate measured results. To date, we have only validated these models against RAID systems with at most four disks. Here we validate our improved model, and also our existing models for other read and write configurations, against measurements taken from an eight disk RAID array

Modelling and Validation of Response Times in Zoned RAID

We present and validate an enhanced analytical queueing network model of zoned RAID. The model focuses on RAID levels 01 and 5, and yields the distribution of I/O request response time. Whereas our previous work could only support arrival streams of I/O requests of the same type, the model presented here supports heterogeneous streams with a mixture of read and write requests. This improved realism is made possible through multiclass extensions to our existing model. When combined with priority queueing, this development also enables more accurate modelling of the way subtasks of RAID 5 write requests are scheduled. In all cases we derive analytical results for calculating not only the mean but also higher moments and the full distribution of I/O request response time. We validate our mode