Healthcare system improvement project management: making a big team work

It's tough chairing meetings, tougher chairing a big meeting (10-15 people), and tougher yet chairing a big meeting that's supposed to last an 8-hour day, one day a week for 6 months. A lot of planning goes into making such a day work with team members varying from the analytical kind to the "feeling" kind, from the surgical kind to the managerial kind. I'm slowly to get a hang of it having done it for a couple months now. The following is a lot of common sense, but if one doesn't have the chance to go through this kind of work with big teams, one may not think it so obvious as an approach. Thought I'd share for whatever it's worth.

• Make sure everyone is doing something - feeling of usefulness in the group, or else people will feel disengaged.



• Assuming natural progress of project is from problem discovery, to analysis, to design and implement, and assuming that everyone in a team needs to participate in all phases, then keep telling self that as soon as we get through to design, things will become more exciting. Analysis phase is not everyone's cup of tea, even though geeks like me find it most interesting.



• Spend the time and create a big poster out of rolling parchment paper. It becomes a live document of all work done on the project to remind team in every meeting of key aims and work accomplished so far. It is a pat on the shoulder for work well done, as well as always showing the direction for the team. Sometimes, one can't see the forest for the trees.



• Big team, big scope - recipe for getting lost or losing sight easily; remind team of aims frequently; relate how current tasks contribute to the aims.



• Identify one lead for each main task to be done in the implementation phase. Give team members enough time to develop own plans on how to implement, and write the document themselves to instill ownership from the start (do not use admin resources to do this). Sometimes it takes 2-3 days just to write and re-write the implementation plans, but the time is worth while, not because we need to have a perfect plan as that is unrealistic, but because it forces people to think of all nitty gritties of how get things done and how they would get around specific change management problems. Provide a good example from a colleague of theirs (real examples from real people = trust), but encourage and give them room to be creative. Then everyone on the team should peer review each other's plan with specific review criteria.



• Once you have all of the above done, engagement level should be pretty high by now, as a healthy amount of sweat and tears will have gone into the implementation plans. I bet anything that you won't be able to hold people back on actioning out those implementation plans.
  

There you have a much happier and motivated team. There is no sure recipe. This isn't one by any means, but it is working for me so far.

Healthcare system improvement project management: how not to manage projects

Lately, I am finding it difficult to not do the work myself in the projects I'm leading/managing. The excuse I've been using is "well, it's just easier to do it myself than asking someone else for it". However, I end up paying for it with way too many late nights working around the clock. I'll be the first to admit: this is the wrong way to manage projects. I end up feeling burned out and tired doing work that should have been done by others in the team, leaving me without enough energy or time to actually 'manage' the projects. Ultimately, if I continued this way, it would be both bad for me and the projects.


However, I used to lead projects like this before, and it worked charmingly. What changed?

Well...

Here I talked about the Master of Management in Operations Research program that trained me as an OR professional (great program by the way). During this master's program, each student is a project lead on a 4-6 months project with a real company doing real projects. The students are fully capable of carrying out all tasks within the project, but have data analysts to help out, because there is just too much analysis work for one person usually. A project lead in this scenario is both the leader and largely the doer - what I'm used to do at work both before and after the master's program.

Why isn't it working now? In my humble opinion, leading 2 projects with relatively large project teams is quite a busy job. One simply doesn't have the time to both lead and do. I did, so I paid for it. Then I learn. I guess in this case, it would probably be overall easier to ask someone else to do it than doing it myself.

Got any tips to share with me? Comment here or email me at dawen [at] thinkor [dot] org

Healthcare system improvement project management: what's the right balance?

I now live in London, UK, and work for a rather famous hospital, renowned for its medical reputation internationally. My role is a project manager on 2 system improvement projects. Such projects are also labeled as "transformation" or "modernisation" projects, depending on where you work. The idea is to work with doctors, nurses, managers, clerical and administrative staff, as well as patient families, who live and breathe the hospital, so that this group of people take ownership of the problems and solutions. We meet one day a week for a full day, and project managers like me and lean improvement facilitators are thrown into the mix to try to help the projects move along. The key is all about implementation, which may make some external management consultants jealous, since they almost never get to implementation. It is a luxury as one can see one's work flourish.

Great idea, isn't it?

Is the team too big?

• 20% of 8-12 people's time is huge! On paper, the staff are 'back filled' for that 20% of work, but in reality, finding the right people with the right skill mix to do 1 day's work is quite difficult. Therefore, these people often end up working 120%. Commitment to the team starts high but then lacks off a bit.
• With the amount of time invested, people outside the group have very high expectations. They want to see things getting churned out from the team quickly, and often ask "when are you going to deliver what". When in reality, such projects have a research nature to them. There may be the best of project plans, but research will always take as long as it does until you can move onto solutions.
• Keeping 8-12 people 'entertained' and interested in the same topic is challenging. Some people are very detailed. Some want to talk big concepts. Some just want to start getting into the issues and start tearing it apart. Keeping everybody happy is never easy.
  
• Big groups also suffer from democracy. It takes time for everybody to have their say, and one person can dominate the whole discussion and shut others up. The good facilitators will still find this difficult.

But is the team too big? I've definitely experienced the same group, but with fewer people, and we were very productive for the small group days. True, everybody in the team should be there because of their functions within the hospital, but perhaps they don't need to all be there every week.

Ideas on how to tackle the big group:

• We are now trying to break the team into smaller groups to be efficient, and to break the group dynamic. Each sub group also has a sub lead, so more people can feel true ownership within the team. We then reconvene after half a day to update each other on progress. It seems to be working so far.
• Send team members out to the hospital to observe, collect information, shadow someone else, and then update. It breaks the 'classroom' feeling when in a meeting room.
• Of course there are many facilitative ways to deal with it as well when they are all doing this: :)
  

I find these projects are shaping like way more people talking than actually doing the work. It is especially frustrating for the ones who actually joined up for doing the work. I've definitely done successful projects in the past that didn't involve such an elaborate set up. This way of working should make implementation easier. I am waiting and seeing.

Introducing variability, flow and processes in a funny video to anyone

I'm leading on two variability & flow management projects at the hospital right now, and the terms "variability" and "flow" are certainly not something the medics hear much about. I needed a quick way of explaining what the projects are about, what these terms mean, and what kind of problems we are trying to resolve. A colleague suggested this video from the ever popular "I Love Lucy" TV series, episode "Chocolate Factory". It does a wonderful job of making people laugh, as well as acting out some strong parallels to a process, and the variability and flow within the process. Take a look at the video (it's a funny one!) and read on for the parallels to the operation of a hospital. The doctors, nurses and patients on my team all found the video not only hilarious but also made it clear to them what we are trying to do in the variability & flow management project.



The parallels:

• Process: the chocolates can be patients coming into the hospital 'conveyor belt'. Lucy and her friend Ethel can be the nurses, for example, (or the various clerks, doctors, pharmacists, radiographers, etc.) handling the patients, 'dressing' them up or giving them care to make them better so they can go on to the next hospital professionals, i.e. the pharmacists to receive medications in the next room down the conveyor belt. The patient traveling through the conveyor belt is a process. Similarly, Lucy and Ethel picking up the chocolate from the conveyor belt, taking the wrapping paper, wrapping up the chocolate nicely, placing the wrapped chocolate back onto the conveyor belt, and returning to the position to be ready for the next chocolate, is a process. Lucy and Ethel are the 'servers' within the process. The things they do to the chocolate are 'steps' within the process. The girls feeding the chocolate onto the conveyor belt for Lucy & Ethel in the previous room are the servers of the upstream process to Lucy & Ethel's wrapping process. Similarly, the girls boxing the chocolates in the next room, perhaps, are the servers of the downstream process.
  
• Flow: The chocolates going through the Lucy & Ethel's wrapping process is a flow.
• Variability: The speed the chocolates are placed onto the conveyor belt is a source of variability, because the speed changes, and so is the speed that Lucy & Ethel wraps the chocolate, as they have very different styles of wrapping. This results in the variable speed of the wrapped chocolates flowing out of the Lucy & Ethel wrapping process.
• Queuing & waits - When Lucy & Ethel were running behind and when they started to collect the chocolates in front of them and in their hats, so that they can wrap them later, that's queuing the chocolates, and those chocolates are experiencing 'waits'.
  
• Mis-communication: When the supervisor meanie lady shouted to the upstream girls to "let it roll" and nothing happened so she had to go to the previous room to sort it out, that's mis-communication or signal failure. :)

The video also shows some classic examples of problems around processes:

• Isolated processes and working in silos – what is going on 'upstream' and 'downstream' is absolutely unknown to Lucy & Ethel.
  
• Lack of issue escalation procedure - when the chocolates are coming too fast for Lucy & Ethel to handle, they had no way of letting the upstream or the manager know (but of course, the meanie supervisor lady didn't allow them to leave one chocolate behind).
  
• Performance management - the meanie supervisor lady did not have realistic expectations on Lucy & Ethel's performance, or maybe she simply didn't have any clue about the variability of the sometimes very high demand placed on Lucy & Ethel from the upstream.
  
• Reactionary management - When the supervisor lady came into the room and saw that Lucy & Ethel had no chocolates on the belt and therefore ordered the upstream to feed faster is very reactionary. She simply made the decision based on one observation / data point, and did not ask any questions about why it is that way.

Hope you find the video useful in your work as well. I'm sure you can draw parallels to other industries aside from health care. Please feel free to share it with me. Things are often best explained by humour.

Healthcare and OR in Canada: selected talks at INFORMS 2008

The Ontario Ministry of Health in Canada would like to reduce the delay in transfer of care from the ambulance to hospital emergency department. The delay usually occurs when the ambulance is at the hospital site waiting to transfer patients to the emergency wards. The ministry would like to use alternative sites, UCCs (Urgent Care Centres), to accommodate the ambulance patients who would typically be discharged on the same day, so as to free up time at the ED needed to deal with these type of patients. The good news is that the ministry has the smarts to research the feasibility of this solution before doing anything. However, the bad news is that the two databases necessary (EMS & hospital databases) for doing this study do not have identifiers for patients. What’s new, right? Healthcare and bad data almost always go hand in hand. Therefore, the team lead by Ali Vahit Esensoy at the University of Toronto cannot identify the same patient in both databases. However, using accurate GPS timestamps and various triage indicators, the team was able to come up with an algorithm to match over 90% of the patients in the two databases. Then with the help of the physicians and staff, the team was able to devise a set of decision rules to filter out the patients that would be candidates for UCC. The result of the study is that the proposed UCC solution is in fact not a good idea, because there are simply not enough such patients. This is a classic case illustrating the importance of quantitative analysis for informed decision making.

On the west coast of Canada, two groups within the CIHR (Canadian Institutes of Health Research) team in Operations Research to improve cancer care, are making impacts in the BC Cancer Agency. They would like to call out to the OR community to help them join in their efforts of establishing an online community to share resources among the OR people working in cancer care.

The British Columbia Cancer Agency (BCCA) is the sole cancer treatment provider for the entire province. The problem to be resolved at the facility is a lack of space (examination rooms and space for physicians to dictate) at the ambulatory care unit (ACU). However, again, the process flow related data was not available. The BCCA OR team, Pablo Santibáñez and Vincent Chow mapped the patient flow process, and then manually collected time and motion data to track the movement of patients and physicians. The data was used for a simulation model to evaluate different what-if scenarios: different appointment scheduling methods and room allocation methods. As a result, the team was able to achieve up to 70% reduction in patient appointment wait time with minimum impact on the clinical duration. They were also able to free up to 26% of the exam rooms to accommodate for other physician duties.

On the academic front, a Ph.D student at the Sauder School of Business in the University of BC, Antoine Sauré, has been helping BCCA in another department: Radiation Therapy treatment units. This research is motivated by the adverse effect of delays on patients’ health such as physical distress and deterioration of the quality of life, and the inefficiencies in the use of expensive resources. Rather than maximizing revenue, the main purpose of our work is to identify good scheduling policies for dynamically allocating available treatment capacity to incoming demand so as to achieve wait time targets in a cost-effective manner. Currently, the number of urgent treatments that would start after the recommended target time is significantly below the target. This goal involves the development of a Markov Decision Process model and simulation models for identifying and testing different types of policies. This is still an on-going research. No results are currently available. However, the team is ready to test algorithms for determining the optimal scheduling policy based on an affine approximation of the value function and a column generation algorithm to tackle the otherwise very large MDP problem.

The papers for the above two projects are available online at http://www.orincancercare.org/cihrteam/ if you wish to obtain more information.

Credits: These 3 talks were given at the INFORMS 2008 conference in Washington DC. The track sessions were TB21, TB34, and TB34. Speakers are Ali Vahit Esensoy, University of Toronto, Canada; Pablo Santibanez, Operations Research Scientist, British Columbia Cancer Agency, Canada; and Antoine Saure, PhD Student, Sauder School of Business, University of British Columbia, Canada. The talks were titled "Evaluation of Ambulance Transfers into UCCs to Improve Ambulance Availability & Reduce Offload Delay", "Reducing Wait Times & Improving Resource Utilization at the BC Cancer Agency’s Ambulatory Care Unit", and "Radiation Therapy Treatment Scheduling".

Doing Good with Good OR: Health Policy, AHQR

Dr. Steven Cohen, Director at the Center for Financing, Access & Cost Trends at the Agency for Healthcare Research & Quality (AHRQ) in the Department of Health & Human Services, USA, shared with the audience the various topics that the Center has been working on.


The Center employs statisticians, economists, social scientists and clinicians to achieve their motto, “advancing excellence in healthcare”. They also monitor and analyze the current trends in healthcare, ranging from cost, to coverage, to access, and to quality of care. Some figures that were shared were:

• Every $1 out of $6 of the US GDP (or 16%) goes to healthcare – largest component of federal and State budget
  
• Other western European nations spend less than 10% on healthcare.
  
• In 2006, $2.1 trillion was spent on healthcare. That is $7,026 per capita, which is a 6.7% increase over 2005.
  
• At this kind of growth, it is projected that $4.1 trillion will be spent in healthcare in 2016 (1/5 of GDP). 
  
• 5% of the US population account for 50% of the healthcare expenditures.
  
• Prescribed medication expenditures almost doubled from 12% to 21% in ~10 years. 
  
• The largest expenditure is on inpatients (over 30%).
  
• The second largest is on ambulatory care (over 20%). 
  
• Chronic diseases (heart, cancer, trauma-related disorders, mental health, and pulmonary) account for a majority of the expenditures.
  
• Medical errors accounted for 44,000 avoidable deaths in hospitals a year.
  
• Americans are less healthy: 33% obesity rate and high rate of chronic diseases.
  

AHRQ aims to provide long term system wide improvement for healthcare quality and safety. To provide policy makers with informed recommendations, surveys, simulations , forecasting models, and other OR tools are often employed to answer difficult questions. Through these methods, AHRQ is able to establish evidence and assess risks associated with alternative care choices.

AHRQ’s focus on efficient allocation of resources and structuring of care processes that meet patient care needs aids policy makers to establish the necessary high-level strategies and policies. Especially in dire times like these, issues of rationing become the center of discussion. It is AHQR’s responsibility to have the right information to help policy makers make the right trade offs.

Credits: The talk was given at the INFORMS 2008 conference in Washington DC as a plenary in the series of Doing Good with Good OR).

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".