{"id":1164,"date":"2019-02-15T10:32:00","date_gmt":"2019-02-15T10:32:00","guid":{"rendered":"http:\/\/kusuaks7\/?p=769"},"modified":"2023-08-25T11:31:34","modified_gmt":"2023-08-25T11:31:34","slug":"the-googol-reasons-your-doctor-never-sees-you-on-time-how-data-science-has-the-answers","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/the-googol-reasons-your-doctor-never-sees-you-on-time-how-data-science-has-the-answers\/","title":{"rendered":"The Googol Reasons Your Doctor Never Sees You On Time: How Data Science Has The Answers"},"content":{"rendered":"

Ready to learn Data Science? Browse courses<\/a>\u00a0like\u00a0Data Science Training and Certification<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n

Why is it that every time I go to a doctor, I am given an appointment for a precise time and then every time, every single time, the doctor shows up at least 20 minutes late? Does the healthcare system hate me? Do they not want to fix the problem? Or are they all just simply incompetent?<\/p>\n

To dig deeper into the question, I\u00a0dove into the operations of more than 50 healthcare providers this past year. I\u00a0looked at resource utilization profiles at three different types of clinics \u2014 cancer infusion treatment, oncology and hematology \u2014 to understand the problem and how best to solve it.<\/p>\n

The truth is that most healthcare providers have the patient’s best interests at heart and are trying their level best. However, \u201coptimal patient slotting\u201d is a lot more complex than might appear on the surface \u2014 in fact, \u201cGoogol<\/a>sized\u201d in complexity. The good news is it\u2019s a problem solvable with advanced data\u00a0science,<\/a>\u00a0the sobering news is it\u00a0must\u00a0<\/em>be solved if we are to handle the incoming onslaught of an increasing, aging patient population all carrying affordable insurance over the next 20 years.<\/p>\n

The Doctor Will Be Right With You\u00a0–\u00a0Not<\/h2>\n

There are few things I take for granted in life, and waiting to see a doctor is one of them. The average wait time for a routine visit to a physician is 24 minutes. I am sure I am not the only one who has sat in a doctor\u2019s waiting room thinking, \u201cYou said you would see me at 3:00 p.m. – why am I being called at 3:24? This happens every time; I bet you could have predicted it. So, why didn\u2019t you just ask me to come at 3:24 instead?\u201d\u00a0A\u00a0Press Ganey study<\/a>\u00a0of 2.3 million patients at 10,000 sites nationwide found that a five\u00a0minute wait can drop patient satisfaction by 5%, a 10 minute wait by 10%, and more than 10 minutes by 20%.<\/p>\n

\"experfy-blog\"<\/p>\n

That 24-minute stat is, in fact, not so bad compared to anyone who has had to get an infusion (chemo) treatment, visit a diabetes clinic, prepare for surgery or see just about any specialist. That wait time can be hours. Just visit any hospital or infusion center waiting room, and you will see the line of patients who have brought books, games and loved ones along to pass that agonizing wait time before the doctor sees them.<\/p>\n

Healthcare Providers Feel The Pain Of Schedules That Don\u2019t Stay On Schedule<\/h2>\n

><\/p>\n

I spent this summer researching this problem on behalf of LeanTaaS, a Silicon Valley healthcare operations startup, and saw for myself just how overworked and harried nurses and doctors operating across the healthcare system are. I spoke to several nurses who have had days they were not able to take a single bathroom break. Clinics routinely keep a\u00a0\u201cmissed meal metric\u201d<\/a>\u00a0– how often nurses miss lunch breaks – and most of the ones I spoke to ring that bell loudly every day. I even heard of stories of nurses suing hospitals for having to go a whole day without breaks or meals.<\/p>\n

The fact is that long patient wait times are terrible for hospitals, too. Patients waiting for a long time is symptomatic of chronically inefficient \u201cpatient flow\u201d through the system and that has serious negative impact on the hospital\u2019s economic bottom line and social responsibility:<\/p>\n

\u2022 Lower Access and Revenue:\u00a0<\/em>A natural corollary to long patient wait times is that the hospital sees fewer patients than it could. In financial terms, low asset utilization and throughput \u2014 fewer surgeries than could have been done, fewer infusion treatments than could have been performed, and so on.\u00a0The Medical Group Management Association<\/a>found that the average utilization of operating rooms at large hospitals in 2013 was only 53%. Less patients served directly implies\u00a0reduced access to care, lower revenues, and higher unit costs.<\/p>\n

\u2022 Rising Labor Costs and Declining Nurse Satisfaction:<\/em>Nurses are an expensive and scarce skill set. Because of the \u201cpeaks and valleys\u201d caused by inefficient scheduling during the day, hospitals have to staff for the \u201cpeak\u201d and simultaneously experience periods of low activity while still needing significant overtime hours from nurses.<\/p>\n

\"experfy-blog\"<\/p>\n

Figure 2: Average U.S. Operating Room Utilization<\/em><\/p>\n

Hospital leaders know this well. Every administrator I spoke to in my research – CEO, COO, CIO, CAO,\u00a0CNO – has some kind of transformation effort going on internally to improve patient flow – \u201clean\u201d teams, 6-sigma teams, rules for how to schedule patients when they call into various clinics, and so on. They know that if patients could be scheduled perfectly in a way that doctors could see them on time, the resulting \u201csmoothing of patient flow\u201d throughout the system would making their facilities, staff and the bottom line much better off.<\/p>\n

It\u2019s not for a lack of motivation that the system is broken.<\/p>\n

The Real Why –\u00a0It\u2019s Just A\u00a0Hard, Complex Math Problem<\/strong><\/h2>\n

><\/p>\n

The system is broken because hospitals are using a calculator, standard EHR templates, and a whiteboard to solve a math problem that needs a cluster of servers and data scientists to crunch.<\/p>\n

To illustrate why this is such a complex problem, let\u2019s take the case of a mid-sized infusion (chemo) treatment center I studied during my research.<\/p>\n

This infusion center has 33 chairs and sees approximately 70 patients a day. Infusion treatments come in different lengths (e.g., one to two\u00a0hours, three to four\u00a0hours and five-plus\u00a0hours long) and the typical daily mix of patients for these three types are 35 patients, 25 patients and 10 patients, respectively. The center schedules patients every 15 minutes starting at 8:00 a.m. with the last appointment offered at 5:30 p.m. So there are 39 possible starting time bands: 8:00 a.m., 8:15 a.m., 8:30 a.m., etc., ending at 5:30 p.m. The center can accommodate three simultaneous starts due to the nursing workload of getting a patient situated, the IV connected, and other prep. That makes a total of 39 *3 = 117 potential \u201cappointment start slots.\u201d<\/p>\n

In order to come up with a quick estimate on the number ways the center can slot these 70 patients into the 117 slots available, we can do the following:<\/p>\n

\u2022 First look at the first block of 35 patients and how we can fit them into the 117 potential slots. This can be done in 117C35 = 8.08 * 10#k8SjZc9Dxk29 unique ways.<\/p>\n

Quick refresher: nCk = \u00a0n! \/ (k! * (n-k))<\/p>\n

To get a lower bound on the possibilities, we can ignore the specific permutations of the 35 patients for now.<\/p>\n

\u2022 Having placed the first 35 patients into the 117 slots, there are now 82 slots remaining in which to place the next group of 25 patients. This can be done in 82C25 \u00a0= 7.56 * 10#k8SjZc9Dxk20 unique ways.<\/p>\n

\u2022 Finally, the last group of 10 patients needs to be placed \u2013 and there are 57 slots remaining. Therefore, they can be placed in 57C10 \u00a0= \u00a04.3 * 10#k8SjZc9Dxk10 unique ways<\/p>\n

For the purposes of a quick lower bound estimate, multiplying all three parts provides the rough number of ways in which the schedule for a typical 70 patient day could be accommodated at 15 minute intervals with three simultaneous starts at this center.<\/p>\n

The size of the solution space is of the order of 2.6 * 10#k8SjZc9Dxk61.<\/p>\n

Now, add to this the reality of a hospital \u2013 some days nurse schedules are different from others, the pattern of demand for infusion services varies widely by day of week, doctor\u2019s schedules are uneven across the week, special occurrences like clinical trials or changes in staff need to be considered, and very quickly, you are looking at a problem that\u2019s very hard to solve with simple heuristics and rules of thumb.<\/p>\n

How Today\u2019s \u201cPatient-Centric\u201d Scheduling Often Works – And Backfires<\/h2>\n

><\/p>\n

Very few hospitals I spoke to understand or consider this math. Rather, in trying to \u201cmake the patient happy,\u201d most providers have been trained to use a \u201cfirst come, first serve\u201d approach to booking appointments. Sometimes they use rules of thumb based on their knowledge of busy times of day or week e.g., start long appointments in the morning and shorter ones later. As far as possible most hospitals will do what they can to accommodate a patient who wants a specific appointment slot, based on their schedule or because for example they want to minimize the gap between their clinic and infusion appointment. Typical EHRs provide the equivalent of a\u00a0Google<\/a>\u00a0GOOGL -0.08%<\/strong><\/a>\u00a0Calendar for appointment scheduling with no real intelligence to guide the decision making.<\/p>\n

The problem is every time a scheduler uses this kind of first-come-first-serve method to scheduling, they are in effect creating one of the 10#k8SjZc9Dxk61 possible patterns above. If they were scheduling patients for one chair, one nurse, and the same treatment type some of their rules of thumb could work. But reality is a lot more complicated – the right schedule would need to consider varying treatment times across patients, include multiple treatment rooms\/chairs, varying staff schedules, lab result availability and so on. So without sophisticated tools, there is an almost zero chance a scheduler can arrange appointments so treatment durations fall like Tetris blocks that align perfectly over the course of the day, and seamlessly absorb patients as they arrive, orchestrating doctor, nurse and room availability, while accounting for all the other constraints of the operation. In effect, they are scheduling \u201cblind,\u201d not taking into account the effect if appointments already scheduled before, during or soon after that slot. It\u2019s like adding traffic to rush hour and almost always results in a \u201ctriangle shaped utilization curve\u201d \u2013 massive peaks in the middle of the day and low utilization on either side. Figure 3 below illustrates this for infusion chairs in a cancer center with 63 chairs.<\/p>\n

\"experfy-blog\"<\/p>\n

Figure 3: Typical utilization of infusion chairs in an infusion treatment center with 63 chairs<\/em><\/p>\n

Each of the 50 hospitals I spoke to this summer identified precisely with this utilization curve. In fact, they identify with \u201cthe midday rush and slower mornings and evenings\u201d so well that they have given them affectionate names \u2013 one called it their \u201cMount Everest,\u201d another \u201cMount Rainier.\u201d From a cancer center\u2019s standpoint, this chair utilization curve has several issues even beyond long patient wait times:<\/p>\n

\u2022 The center can only see a fraction of patients they could have with a \u201cflatter\u201d utilization curve.<\/p>\n

\u2022 Nurse scheduling has to be done for the peak and the treatment center typically deals with lots of overtime issues.<\/p>\n

\u2022 Nurses find it hard to take lunch breaks because of the midday peak, while half the time the chairs are empty<\/p>\n

\u2022 On any day, given the number of interdependent moving parts, a small perturbation to the system (e.g., a patient\u2019s labs are late, another didn\u2019t arrive on time) creates a domino effect throughout, further exacerbating delays, not unlike a fender bender in rush hour traffic that delays everyone for hours.<\/p>\n

In effect, when hospitals think they are scheduling in patient-centric ways they are doing exactly the opposite.<\/p>\n

They are promising patients what they cannot deliver \u2013 instead of giving the patient that 10:00 a.m. Wednesday appointment, an 11:40 a.m. appointment may have been much better for the patient and the whole system. As we will see in my further analysis, the patient could have had a 70% shorter wait time, the hospital could have seen 20% more patients that week, every nurse could have taken a lunch break every day, and a lot less (if any) overtime would have been required.<\/p>\n

So How Do You Solve This \u201cGoogol Sized Patient Slotting\u201d Problem? Use Data Science<\/h2>\n

The solution to the patient slotting problem lies in data science and mathematics, using inspiration from lean manufacturing practices\u00a0pioneered by Toyota decades ago<\/a>, such as push-pull models,\u00a0production leveling<\/a>,\u00a0reducing waste, and just-in-time production.<\/p>\n

In mathematical terms, it means taking those 10#k8SjZc9Dxk61 possibilities detailed earlier and imposing the right set of \u201cconstraints\u201d \u2013 demand patterns, staffing schedules, desired breaks, and whatever is unique to the hospital\u2019s specific situation \u2013 to come up with a much tighter set of possible patient arrangements that solve for maximizing the utilization of hospital resources and therefore the number of patients seen. In the case of the infusion center, the algorithm optimizes utilization of infusion chairs making sure they occupied uniformly for as much of the day as possible as opposed to the \u201cpeaks and valleys\u201d in Figure 3. In essence, \u201crearranging the way the Tetris blocks (patients) come in” so they appear in the exact order they can be met by a nurse, prepped and readied for a doctor whose schedule has been incorporated into the algorithm.<\/p>\n

The first step in doing this is mining the pattern of prior appointments in order to develop a realistic estimate of the volume and mix of appointment types for each day of the week going forward. The second step is imposing the real operational constraints in the clinic (e.g., the hours of operation, doctor and nurse schedules, the number of chairs, various \u201crules\u201d that depend on clinic schedules, as well as patient-centric policies such as treatments longer than four hours should be assigned to a bed and not a chair). Finally, constraint-based optimization techniques can be applied to create an optimal pattern of \u201cslots,\u201d which reflect the number of \u201cappointment starts\u201d of each duration. In the case of the infusion center, that means how many one-hour duration, three-hour duration, and five-hour duration slots can be made available at each appointment time (i.e., 7:00 a,m., 7:15 a.m., 7:30 a.m. and so on).<\/p>\n

\"experfy-blog\"<\/p>\n

Figure 4: Optimized shape of utilization curve for the same center as in Figure 1. 20% lower peak, much smoother utilization of resources, significant capacity freed up<\/em><\/p>\n

Doing this optimally results in moving the chair utilization graph from the \u201ctriangle that peaks somewhere between 11:00 a.m. and 2:00 p.m.\u201d in Figure 3 to a \u201ctrapezoid that ramps up smoothly between 7:00 a.m. and 9:00 a.m., stays flat from 9:00 a.m. until 4:00 p.m., and then ramps down smoothly from 4:00 p.m. onwards\u201d in Figure 4. \u00a0Coming up with realistic slots that keep patients moving smoothly throughout the day cuts patient waiting times drastically, reduces nurse overtime without eliminating breaks, and keeps chair utilization as high as possible for as long as possible. Small perturbations in this system are more like a fender bender at midnight, a small annoyance that causes a few minutes of delay for a small number of people instead of holding up rush hour traffic for hours.<\/p>\n

Smoothing Patient Flow – A Large Economic Opportunity<\/h2>\n

><\/p>\n

The above graphs are sanitized versions of real data from a cancer infusion treatment center at a real hospital that used these techniques to solve their flow problems. The results they achieved are staggering and point to the massive economic and social opportunity optimal patient flow presents. Post-implementation of a product called \u201cLeanTaaS iQueue<\/a>,\u201d they now experience:<\/p>\n

\u2022 25% higher patient volumes<\/p>\n

\u2022 17% lower unit cost of service delivery<\/p>\n

\u2022 31% decrease in median patient wait times<\/p>\n

\u2022 50% lower nurse overtime<\/p>\n

\u2022 Significantly higher nurse satisfaction – no missed meals<\/p>\n

Imagine applying this kind of performance improvement to every clinic, hospital, surgery suite in the country and the corresponding positive impact it will have on population\u00a0health<\/a>\u00a0with the increased patient access to the system.<\/p>\n

It\u2019s Going To Get Worse Unless Providers Address This Problem Now<\/h2>\n

This problem is going to get a lot worse for a simple reason \u2013 the demand for medical services has never been stronger, and it\u2019s only going to increase. Just looking at the U.S. market:<\/p>\n

\u2022 Population Growth:\u00a0<\/em>By 2050 there will be over 438 million Americans, up from 320 million in 2015.<\/p>\n

\u2022 Demographics:\u00a0<\/em>By 2030, over 20% of the country is expected to be over 65, up from 15% in 2015 – increasing the demand for chronic clinical therapies. In raw numbers, the Census Bureau estimates that by 2030, when the last round of baby boomers will hit retirement age, the number of Americans above 65 years age will hit 71 million, up from 41 million in 2011, a 73% increase. When this happens, one in five Americans will be above 65 years old. Not surprisingly, by 2025, 49% of Americans will be affected by a chronic disease and need corresponding therapies.<\/p>\n

\"experfy-blog\"<\/p>\n

Figure 5: Access to Healthcare is a looming crisis – multiple drivers of significant demand growth<\/em><\/p>\n

\u2022 The Affordable Care Act<\/em>:\u00a0The Affordable Care Act<\/a>\u00a0will add another 30 million Americans to the health care system by 2025. That means more demand for health care \u2013 more doctor visits, more hospital visits, more emergency emergency room visits, and more need for resources (e.g., surgery rooms, MRI\/CAT scans). Reimbursements will increasingly depend on outcomes and efficacy, quality of care and patient access. Unless providers become a lot more efficient in how they process and treat patients, they will need to spend billions in capital spending on new infrastructure – clinics, operating rooms, infusion centers and the like.<\/p>\n

\u2022 In\u00a0an online poll conducted by the American College of Emergency Physicians<\/a>\u00a0(ACEP), 86% expect emergency visits to increase over the next three years. \u00a0More than three-fourths (77%) say their ERs are not adequately prepared for significant increases.<\/p>\n

\u2022\u00a0The Commonwealth Fund<\/a>, a New York-based fund that tracks health care performance, projects that primary care providers will see, on average, 1.34 additional office visits per week, accounting for a 3.8% increase in visits nationally. Hospital outpatient departments will see, on average, 1.2 to 11.0 additional visits per week, or an average increase of about 2.6% nationally.<\/p>\n

\u2022It is estimated\u00a0that the U.S. will face a shortfall of 90,000 physicians and 500,000 nurses by 2030.<\/p>\n

The Good News<\/h2>\n

Most healthcare providers are waking up to the fact that their operations need a data-driven, scientific overhaul much the same way as auto manufacturing, semiconductor manufacturing and all other asset-intensive, \u201cflow\u201d-based systems have experienced. The good news is that there are tools, software and resources that can be used to bring about this transformation. Companies at the forefront of this thinking are applying complex data science algorithms to help hospitals solve these problems. Hospitals that are serious about solving patient flow issues and the related problems now have access to the best computational minds and tools at their disposal.<\/p>\n

I see a world in which our healthcare system can see every patient on time without imposing hardship on care providers, disruption on current processes or increasing cost of services. Here’s to that world!<\/p>\n

Originally published on Forbes<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Ready to learn Data Science? Browse courses\u00a0like\u00a0Data Science Training and Certification developed by industry thought leaders and Experfy in Harvard Innovation Lab. Why is it that every time I go to a doctor, I am given an appointment for a precise time and then every time, every single time, the doctor shows up at least<\/p>\n","protected":false},"author":90,"featured_media":4340,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[94],"ppma_author":[1640],"class_list":["post-1164","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-data-science"],"authors":[{"term_id":1640,"user_id":90,"is_guest":0,"slug":"sanjeev-agrawal","display_name":"Sanjeev Agrawal","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Agrawal","first_name":"Sanjeev","job_title":"","description":"Sanjeev serves as the President of Healthcare and Chief Marketing Officer at LeanTaaS, the leading healthcare predictive analytics company based in Silicon Valley. He was Google’s first Head of Product Marketing. "}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1164","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/90"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1164"}],"version-history":[{"count":3,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1164\/revisions"}],"predecessor-version":[{"id":28414,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1164\/revisions\/28414"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/4340"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1164"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1164"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1164"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1164"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}