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The Future of Hospital Operations Looks like 'Air Traffic Control' | @ThingsExpo #IoT #BigData #Analytics

Predictive analytics is key for healthcare and aviation

The Future of Hospital Operations Looks like "Air Traffic Control"
By Mohan Giridharadas

Explosion of data volumes. Interoperability of systems. Large servers in the sky that can analyze enormous amounts of data, compute complex algorithms in real time, and communicate in microseconds. Mobile communication through devices that patients, providers and staff all carry all the time. What does this all mean for hospital operations? Based on working with dozens of hospitals and conversations with 100+ others, we think the near future of hospital operations is quite exciting. Call it what you will - "Hospital 2.0," "No Waiting Rooms," "Hospital Operations Center" - the basic building blocks to enable the future of hospital operations are already here.

Today, two major shifts are putting pressure on hospitals to rethink how they deliver care: (a) increased demand for care from the Affordable Care Act and the growing number of people with chronic illnesses and (b) the move toward value-based care.

These shifts have big implications across the board, but most importantly in operations. Hospitals are under constant pressure to do more with less. Every day, they face an operational paradox: Scarce resources are both overbooked and underutilized within the same day. This leads to several undesirable outcomes: long patient waiting times, overworked staff, millions of dollars of unnecessary operational costs, and an insatiable appetite for expanding existing facilities or constructing entirely new ones. For specialty services like chemotherapy, it could take days or weeks for a new patient to be given a slot - yet, the typical infusion chair is occupied less than 60 percent over the 7 a.m.-7 p.m. time horizon. The same is true of operating rooms; study after study shows that hospitals don't utilize their resources optimally.

Historically, process improvement efforts in hospitals worked with small, historical snapshots of data from which the core operational issues were identified. From this, strategies were developed, implementation plans executed and the disciplines for continuous improvement were established. This was the best approach when all that was available was rear-view mirror data snapshots and Excel as the analytic engine of choice. Today, there's a lot more data to learn from - on average, health systems produce up to 2 terabytes of data per patient every year. Combined with the explosion of smart devices, computational power in the cloud and the growing pervasiveness of data science and machine learning algorithms, an entirely different realm of operational optimization suddenly becomes possible. It is similar to the realization that decades ago, general surgeons did the best they could from the insight they could glean from grainy X-ray images. Today, armed with high-resolution MRI/PET images and fiber-optic cameras, the same surgeon can execute surgeries an order of magnitude more complex than they could have imagined being able to do when they were surgical residents a few decades ago.

Consider the following scenarios on how predictive analytics is already optimizing patient pathways within hospitals:

- Optimizing access to treatments such as chemotherapy: By looking at historical demand patterns, and operational constraints, sophisticated forecasting algorithms can predict the daily volume and mix of patient volume and orchestrate appointment slots such that there are no "gaps" between treatments. This radically improves chair utilization, lowers patient waiting times and reduces the overall cost of operations. Doing this without sophisticated data science is hard - for example, just arranging the order in which 70 patients can be slotted for their treatments in a 35-chair infusion center is a number exceeding 10^100, as this analysis shows. Trying to solve this problem with pen, paper or Excel is a pointless exercise.

- Operating rooms are key resources within the hospital. Study after study shows that the OR utilization at most large hospitals is at best 50-60 percent. In most hospitals, operating rooms are allocated to surgeons using "blocks" - for simplicity, the blocks are often either half-day or full-day blocks. Even the most prolific and productive surgeons often don't fully utilize the blocks they are given, and the process for reallocating blocks on a monthly basis or even for last minute block swaps is cumbersome and manual. Using data science and machine learning, hospitals can monitor utilization, identify pockets for improvement, automatically reallocate underutilized blocks, and improve overall operating room utilization. A 3-5 point improvement in block utilization is worth $2 million per year for a surgical suite with just four operating rooms.

- In-patient bed capacity is a constraining bottleneck in most hospitals, yet virtually every hospital solves this problem with an arithmetic-based "huddle" approach that reviews the patient census from the overnight stay in each unit, adds known incoming patients, subtracts known discharges and then decides if the unit is flirting with the limits of its available capacity. This cycle repeats itself, often several times per day, with a planning horizon of the day at hand. On the other hand, Google completes the search bar while we are typing because it has analyzed millions of search terms similar to the one you are entering and automatically presents the four or five highest probability queries that you intend to submit. Imagine looking at each overnight patient, finding the 1,000 prior patients over the last two years who entered the hospital with a similar diagnostic or procedure code and reviewing their "flight path" through the hospital (i.e., # days spent in each of the units prior to discharge); then, an aggregate probabilistic assessment of the likely occupancy of each unit could be developed. Not only would it provide a better answer for today, it would also help anticipate the evolving unit capacity situation over the next 5-7 days, thereby leading to smarter operational decisions on transfers, elective surgery rescheduling, etc.

- A similar machine learning approach can help orchestrate patient flows at clinics, labs, the pharmacy and any unit within the hospital network that struggles with the operational paradox of being overbooked and underutilized at the same time.

An interesting metaphor for the future of hospital operations is how the air traffic control and sophisticated scheduling and airport operations have transformed air travel for passengers. They too have enormous complexity and the mission-critical requirement of passenger safety in the face of challenging external conditions. Three direct parallels:

- In order for a single flight to transport passengers safely from point A to point B, it requires "above the wing" services (boarding, food, crew) and "below the wing" services (baggage, fuel, tire check and other inspections) to come together seamlessly. Similarly, in order to perform even a routine surgery, services like labs, pharmacy, the clinician, the surgeon and the supporting team all need to come together to be able to safely and successfully treat the patient.

- At any busy airport, tens of thousands of passengers each day navigate their personal journey across connecting flights while relying on "invisible supporting services" such as their bag transfers and rebookings in the case of delays, weather systems, etc. Similarly, in a busy hospital, on any given day, thousands of patients navigate their personal journey across a continuum of care while relying on the supporting services of labs, pharmacy, etc. to be timely and accurate.

- The volume of airline passengers has grown from a few thousand to a few million per day, and airports and airlines have been forced to do "more with less." Similarly, the Affordable Care Act and a growing and aging population combined with the increased incidence of chronic disease will require hospitals to do "more with less".

The aviation industry has diligently invested in the required technology, systems and processes to monitor, measure, collaborate and orchestrate. Similarly, hospitals are also beginning to invest in the technology, systems and processes to maximize patient access at each "node" and to streamline the linkages across nodes. Just as the advent of air traffic control and fine-grained scheduling transformed airports like JFK from handling only a few hundred flights per day in the 1960s to managing thousands of take offs and landings per day within the same airspace, modern technologies and predictive analytics will lead to the creation of a similar "air traffic control" capability for hospitals. Assets like the OR, inpatient beds, clinics, infusion chairs and MRI machines will be far better utilized throughout the day; many more patients will be treated within the same facilities; and they will need to wait far less between "legs of their flight" across the continuum of care.


Mohan Giridharadas is an accomplished expert in lean methodologies. During his 18-year career at McKinsey & Company (where he was a senior partner/director for six years), he co-created the lean service operations practice and ran the North American lean manufacturing and service operations practices and the Asia-Pacific operations practice. He has helped numerous Fortune 500 companies drive operational efficiency with lean practices. As the founder and CEO of LeanTaaS (a lean and predictive analytics company), Mohan has worked closely with dozens of leading healthcare institutions including Stanford Health Care, UCHealth, UCSF, Wake Forest and more. Mohan holds a B.Tech from IIT Bombay, MS in Computer Science from Georgia Institute of Technology and an MBA from Stanford GSB. He is on the faculty of Continuing Education at Stanford University and UC Berkeley Haas School of Business and has been named by Becker's Hospital Review as one of the top entrepreneurs innovating in Healthcare.

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