ER Crowding, physician appointment and nursing care waits

Does Michael Moore’s SiCKO do the American healthcare any justice? Is the US healthcare riddled with delays? With the increasing exposure of healthcare wait time problems on TV and other media, and with healthcare being one of the central topics of political debates and strategies, what and where are the problems, and how do we deal with them? Professor Linda Green from the Columbia Business School talks about the three critical types of waits in healthcare and their impact on one another: emergency room (ER), physician appointment (or imaging tests) and nursing care waits. She sets out to prove that there are not “too many” beds in the system, physician appointments cannot be simply Just-In-Time, and the nurse staffing level doesn’t have a one-size-fit-all solution.

When physicians aren’t surprised of seeing patients suffering from heart attacks dying while waiting to get into a bed in ER (2006 news from an Illinois emergency room), when patients leave ER without being seen due to the frustration of long waiting hours, when ambulances have to be diverted because the ER is full, one needs to ask why. Why are the ERs always full when you need it? The number one reason for this is a lack of inpatient beds – the bottleneck of the healthcare system, which causes the inability to move patients in ER to an inpatient unit and therefore releasing beds in the ER. So, why are there not enough beds? The answer is relatively simple. Healthcare is expensive, and when governments need to stay on budget, healthcare gets cut. Healthcare also happens to be largely funded by government subsidization, so such cuts have reduced the number of hospitals from 7000 to 5000 (from 1980 to now), and the number of beds available from 435 to 269 per 100,000 persons in the US. Yet, politicians cry that there are “too many” beds. Why, you ask? Because important decision makers are looking at occupancy level (or bed utilization ratio), which for ER beds the utilization currently resides at around 66% (target is 85%). Utilization looks good, but it is the wrong measuring stick. Every OR person knows variability means extra capacity (more so than the average) is needed to deal with times of heavy demand with a reasonable level of customer satisfaction. Healthcare is one such process where demand varies greatly with the day of the week and the hour of the day. Aiming at and planning for averages is not going to work. A Green’s study showed that if New York state hospitals wanted to have a less than 10% chance of not having a bed available for up to 1 hour for an ER patient, then 58% of the hospitals would be too small. If they aim for a 5% chance of not having a bed available, then 74% hospitals would be too small; and if the aim were 1%, 90% hospitals would be too small. However, the good news is performance could be improved without increasing stuffing level at the hospitals, as Green has shown in her work.

Moving onto physician appointment and nursing care waits, these two factors both contribute to the overcrowding of ERs. When physicians are asked to treat patients in a Just-In-Time fashion, the major issue is that no one knows how to balance the physician capacity with the patient demand. When physicians have too many patients, not everybody can get seen in time, and patients cancel appointments if the wait is long. When patients cancel appointments, they also ask for another one because they still needs to be seen, so it goes back and adds to the backlog for the physicians. Study shows the shorter the backlog the higher the physician’s utilization. Green’s project of recommending the right number of patients for doctors has helped over 500 clinics and organizations to find that balance of capacity and demand.

It is widely acknowledged that there is a shortage of nurses, and that the lack of nursing care is directly correlated with increasing patient safety issues (mortality included) and patient satisfaction. California has passed a legislation to ensure a 1:6 nurse-to-patient ratio for general medical-surgical wards. It is good to see actions, but is this policy more disruptive than helpful? According to Green, varying unit sizes, the level of intensity of care for differing patient types, and the length of stay (and therefore turnover of patients) at different hospitals can all mean different optimal nurse-to-patient ratios. In larger units, the California legislation could result in overstaffing at a cost of $300,000 per year per unit, says Green. That is expensive.

Overall, healthcare certainly has a lot of problems. To name a few, there is the increasing cost, quality problems, and access problems. Operations Research is needed in this field. It could mean a matter of life and death.

Credits: The talk was given at the INFORMS 2008 conference in Washington DC. The track session was SC37. Speaker is Linda Green, Armand G. Erpf Professor, Columbia Business School. The talk was titled "Tutorial: Using Operations Research to Reduce Delays for Health Care".

Business Intelligence: Data Text Mining & Its Challenges

In the world of business intelligence (BI), data and text mining is a rising star, but it has a lot of challenges. Seth Grimes points out the importance of having structured data in relational databases, and the need for statistical, linguistic and structural techniques to analyze various dimensions of the raw text. He also shares with the audience some useful, open source tools in the field of text mining. John Elder, on the other hand, shares the top 5 lessons he has learned through mistakes in data mining, where he also reveals one of the biggest secret weapons of data miners.

Grimes took the audience on a journey the traditional BI work, where data miners take raw csv (comma separated values) files, and turn them into relational databases, which then gets displayed as fancy monitoring dashboards in analytics tools – all very pretty and organized. However, most of the data that BI deals with are “unstructured” data, where information is hiding in pictures and graphs, or in documents stuffed with text. According to Grimes, 80% of enterprise information is in “unstructured” form. To process the raw text information, Grimes says it needs to be 3-tiered: statistical/lexical, linguistic and structural. Statistical techniques help cluster and classify the text for ease of search (try ranks.nl). Syntactical analysis from linguistics helps with sentence structures to provide relational information between clusters of words (try Machinese from connexor.eu to form a sentence tree). Finally, content analysis helps to extract the right kind of data by tagging words and phrases for building relational databases and predictive models (try GATE from gate.ac.uk).

Elder’s list of top 5 data mining mistakes includes:
1. Focus on training the data
2. Rely on one technique
3. Listen (only) to data (not applying common sense to processing data)
4. Accept leaks from future
5. Believe your model is the best model (don’t be an artist and fall in love with your art)

In particular, Elder shares with the audience the biggest secret weapon of data mining: combining different techniques that do well in 1-2 categories will give much better results. See Figure 1. 5 algorithms on 6 datasets & Figure 2. Essentially every bundling method improves performance. Figure 3. Median (and Mean) Error Reduced with each Stage of Combination also illustrates the combinatorial power of methods for another example in his talk.





Data text mining is still in its early stage, and the “miners” have a lot of challenges to overcome. However, given the richness of information floating around on the internet and hiding in thick binding books in the library, data text mining could revolutionize the business intelligence field.


Credits: The talk was given at the INFORMS 2008 conference in Washington DC. The track session was SB13. Speakers are: Seth Grimes, Intelligent Enterprise, B-eye network; and John Elder, Chief Scientist, Elder Research, Inc. The talk was titled "Panel Discussion: Challenges Facing Data & Text Miners in 2008 and Beyond".

Forecasting Hollywood movie box office revenue with HSX trading history

Want to know what movies are going to make it or flank it at the box office? Is it going to be a hit, a fast decaying, or a sleeper movie – that is in terms of its box office revenue trend? Natasha Foutz, Wolfgang Jank and Gareth James have attempted to predict the revenue trend of Hollywood movies with 3 principle components (average/longetivty, fast decay, and sleeper effect) in conjunction with Hollywood Stock Exchange (HSX) trading histories. HSX is a virtual stock market of music, TV shows, and movies. The authors claim a high degree of forecasting accuracy using functional shape analysis and regression on the 3 principle components and early HSX trading histories for the individual 10 weeks box office opening revenue of Hollywood movies. If you are really good at this game, you may end up selling your billion-dollar HSX portfolio on ebay, who knows?

Hollywood movies have widely varying box office revenues, some much more profitable than others. Therefore, it is crucial to forecast movie demand decay patterns for movie financing, contracting, general planning purposes, etc. The forecast needs to be made long before the movie release, since planning happens much more in advance, sometimes years earlier. Most movies gain the majority of its revenue in the first 10 weeks of opening, so the model looks at the forecasting of demand decay patterns of the first 10 weeks of Hollywood movies. The use of HSX data is proven to provide more information for the revenue forecasting purposes. Virtual stock markets (VSM), the show of wisdom of crowds, are of no stranger to forecasting complicated issues ranging from election results, NBA championship winnings, to Al Gore’s 2007 Noble Prize winning. The results produced by VSM are very impressive and accurate. For example, the political VSM was said to be 75% more accurate than political polls.

Foutz, Jank and James identified 3 principle components to be used alongside the trading history of HSX: average/longevity, fast decay, and sleeper. Longevity captures the average box revenue over the lifetime of the movie where the trend is relatively smooth (a linear decreasing trend of a log transformation of the revenue figures), such as Batman Begins. Fast decay captures the movies that have great openings but quickly die out, such as Anchorman. Sleeper describes the movies that have a slow start, but with word of mouth (for example), it would pick up momentum in later weeks of the opening, such as Monster or My Big Fat Greek Wedding.

The authors tested out 5 different models of weekly revenue regression over a period of 10 weeks. Each model uses a combination (or the lack of) the three principle components and the trading histories from HSX. The results indicate that movies with higher level of trading activities on HSX at the very early stage (weeks in advance) would more likely have a higher weekend box office opening revenue. How could this finding be used for more meaningful purposes than betting with your friends? For example, theatre owners could better allocate screens and profit sharing, while movie producers could design different contracts for the slow burners than the fast ones. If you are a movie buff, maybe it’s time to get on the HSX for some trading fun instead of crying over the financial stock markets.

Credits: The talk was given at the INFORMS (Institute For Operations Research and Management Science) 2008 conference in Washington DC, in session SA68, by Natasha Z Foutz, Assistant Professor of Marketing, from the McIntire School of Commerce, University of Virginia. The title of the talk was "Forecasting Movie Demand Decay Via Functional Models of Prediction Markets".

The Numerati: casting OR folks in an evil light?

I think it is great that operations research is getting some publicity with The Numerati. However, there can be such a thing as a bad publicity. Is it just me or does it seem to everybody (OR folks) that this book is casting us in a rather negative light? I think the general notion is already that the numbers guys are not to be trusted (at least in certain health care places). Now this book may be saying how smart we are and all that, but with a bit of an evil undertone. Just the title itself, "how they will get my number and yours", is painting us as some kind of math hackers out to steal people's information, isn't it?

I have mixed feelings about this book, but I am curious to read it. I just hope we won't scare anybody more than now when us OR people walk down a hospital isle.

Feel free to voice you thoughts on The Numerati.

The Numerati (new book): Doing Business the Math Way

Most of the readers of this blog must have seen the announcement in the latest INFORMS eNews. I would like to share with the rest of the world the news of the release of a book that will make us famous. The book is called "The Numerati" in reference to mathematical modelers who are rocking the business world. It certainly sounds more catchy than "Operations Engineer" "Operations Researcher" or "Operations Scientist".

Links:
http://www.businessweek.com/magazine/content/08_36/b4098032904806.htm

http://thenumerati.net/

OR Career Path Talk by Jason Goto

Jason Goto came to the University of British Columbia and gave an informal talk & Q&A on career path in Operations Research as part of the INFORMS UBC Student Chapter event series. It was a very open dialogue appreciated by the audience. Here are some highlights from this talk.

Job Market:

Work opportunities in operations research locally in Vancouver is rather limited. It includes

• The big health authorities: Fraser Health Authorities, Coastal Health Authorities, BC Cancer Agency, etc.
• and maybe some engineering firms, such as Sandwell Engineering

Particularly, if an OR professional is looking for good job opportunities, one should consider relocation to the east coast or the States. However, we live in Vancouver because of a lifestyle choice, so if that's clear, prepare to sacrifice in pay and business opportunities. 

Jason stressed on the importance of critical mass of OR group to sustain an OR operation and presence within an organization. If an organization has only a few OR professionals working, they may not be able to achieve enough to show the importance of OR; and if someone leaves or goes on vacation, things grind to a halt and will take a long time to get back on track.

OR Consulting:

If an OR professional is thinking of going into consultancy by joining a consulting agency, then those companies will value the consulting/business/soft skills much more than they do about your technical OR skills. However, an OR professional should possess the following skill set:

• data skills
• consulting & communication skills (written & verbal)
• change management
• empathy - put yourself in other's shoes to help them understand your view

There are quite a few very small OR consulting companies with 1 to 3 people. Mostly they are academics doing consulting on the side. These small outfits don't tend to grow, because it is simply easier to do the work with only a few people, especially if you want the work to be done well, without much administration and supervision.

Jason's operations research consulting company, AnalysisWorks, incorporated in 2000, has been growing 25% a year. He has groomed it to an 8 person outfit - a steady, unaggressive growth since the start.

Starting a consulting company, the first year is the hardest. Everyone is against you if you have no credentials or portfolios to show. It is difficult to get the consulting projects because of that. Especially if you look too young (if you are starting out early), people don't take you seriously. You wonder if you are getting your market value. However, on the other hand, if you start the company when you are older, there are elements pressing against you as well: family, dog, house, pension, etc.  Some people may choose to start a company in groups. This requires careful consideration and an early agreement on who does what, just like in a marriage. If one partner is good at selling and the other good at doing, the two must agree on how they will operate together and the compensation scheme. Otherwise, break-ups could be very bad - again, just like marriages.

In general, it is difficult to get the OR consulting projects. If 10 companies are contacted, 1 may come back with some interest. Most people think the work is good, but do not think it absolutely necessary. The good clients are the ones that really, really need your help, because otherwise their jobs and the company's survival is on the line.

Over-delivery is a consultant's own loss. Clients may have been more than happy with the less than perfect solution, compared to a perfect solution which could have taken hours of the consultant's time - exhausting the budget that way.

Decision Making Model on Stroke Prevention: Warfarin or not

An interesting talk I attended at the CORS 2008 conference in Quebec City was by Beste Kucukyazici from the Faculty of Management of McGill University. The topic of the talk was “Designing Antithrombotic Therapy for Stroke Prevention in Atrial Fibrillation”.

Beste Kucukyazici showed the study of stroke patient data to see if a decision model could be derived to systematically decide on the commencing of warfarin treatment for stroke patient and its intensity. Now my question is: will OR decision models take a bigger and bigger foothold in the future of medical arena as we start to gather more useful patient data in well-planned studies? Medical doctors tend to argue that each patient has a different case, and need to be examined on an individual basis. However, if a model such as Kucukyazici’s can prove the accuracy of its decision given real patient data, then it would probably start to weaken the doctor’s argument and favour a more systematic approach. At least, such models might help reduce the complexity of doctor’s decision making process, or even reduce chances for human errors in diagnosis.

Atrial fibrillation, which is a common arrhythmia particularly common among the elderly, is one of the major independent risk factors of stroke. Several randomized control trials have shown that long-term antithrombotic therapy with warfarin significantly reduces the risk of stroke, however, it also increases the risk of suffering a major bleed. Given the potential benefits and risks of warfarin treatment, the decisions that need to be made by the clinicians are two-fold: (i) whether to start the therapy, and (ii) the intensity of warfarin use. The objective of this study is to develop an analytical framework for designing the optimal antithrombotic therapy with a patient-centered approach. The approach seeks to create a rational framework for evaluating these complex medical decisions by incorporation of complex probabilistic data into informed decision making, the identification of factors influencing such decisions and permitting explicit quantitative comparison of the benefits and risks of different therapies.

Jason Goto on Operations Research Career Path – July 18, 2008

Jason Goto, President of AnalysisWorks, is invited by the INFORMS UBC Student Chapter to give an informal talk on O.R. career path on July 18, 2008. The event will be held at the Penthouse in the Henry Angus building on UBC campus at 2:30-3:30pm.

Invited audience include current and incoming Master of Management in Operations Research (MM in OR) students & alumni of the Centre for Operations Excellence (COE), Sauder School of Business, University of British Columbia.

Wondering what career path in OR you would like to choose?

Wondering how OR consulting is done?

Want to meet the guy who started AnalysisWorks
– one of the only OR consulting firms in Vancouver?

If you happen to be in Vancouver, then join us on Friday, July 18, 2:30-3:30pm at the Penthouse of the Henry Angus building on UBC campus.

Jason Goto, BASc Engineering, MSc Management Science: President
Jason Goto has consulted in a wide variety of projects involving the application of analytic data-driven methods. He has worked with major health care organizations, market research firms, manufacturers, and other private and public organizations. He specializes in the effective application of Operations Research and Management Science techniques including scenario analysis, statistics, forecasting, simulation, and optimization. (From AnalysisWorks.net)

ORAHS 2008 in Toronto Canada

The 34th annual conference on Operations Research Applied to Health Services will be held in Toronto, Ontario, Canada, on July 28 - Aug 1, 2008. 

The ORAHS was formed in 1975 in Europe, and it is usually held in Europe as well. This year, however, Canada has the honour of hosting it.

Check here for more details on the ORAHS 2008 conference.

Mike Carter on New Challenges for OR Applications in Health Care

I had the pleasure of meeting Professor Michael W. Carter at the Canadian Operations Research Society conference (CORS) in Quebec City, and listening to his plenary talk on "New Challenges for Operations Research Applications in Health Care" - the kick-off talk for this year's CORS conference on May 12th, 2008.

Professor Carter is one of the Canadian leading experts in healthcare and operations research, with over 17 years of experience in OR applications in healthcare. He currently leads the Centre for Research in Healthcare Engineering, Mechanical and Industrial Engineering, University of Toronto. Click here for more information on Professor Mike Carter.

Mike has been very kind to allow me to publish his talk here on ThinkOR.org. Here are some key points to take away:

• Healthcare is North America's single largest industry; Canada spent $142 billion CDN in 2005; US spent $2 trillion.


• Canada's per-capita spending ($3,326 USD) was half of US ($6,401 USD), and this is how it's been growing:


• US & Canada are about the same in terms of quality of health care, access, efficiency , and equity (based on the Commonwealth Fund 2004 International Health Policy Survey)


• A new way of looking at the healthcare system's stakeholders (no wonder it's difficult to make decisions in a hospital):
  
• Challenges in healthcare system can be viewed as operations research challenges:
  
  
  
  • Patient flow - supply chain
  
  
  • Surgical wait list - better scheduling
  
  
  • Infectious diseases - logistics
  
  
  • Health human resources - forecasting

Mike also demonstrated the application of O.R. techniques in his own practice:

• Ontario Wait List Management


• Colorectal cancer screening


• Cancer treatment centre locations


• Health Human Resource Modelling

Thank you Mike for allowing me to write about your talk. It was delightful to see OR in action in the Canadian healthcare. We look forward to seeing the 30% potential waste of money spent in healthcare to shrink fast.