Tag Archives: pharmacy

On Air Traffic Control and Health Costs

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 our work with dozens of hospitals and conversations with 100-plus 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.

See Also: 5 Trends in Health IT

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% of the time between 7 a.m. and 7 p.m. 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 were executed and disciplines for continuous improvement were established. This was the best approach when all that was available were rear-view mirror data snapshots and Microsoft Excel as the analytic engine of choice. Today, there’s a lot more data to learn from. On average, health systems produce as much as two 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 has suddenly become possible. It is similar to the realization that, decades ago, general surgeons did the best they could with the insight they gleaned from grainy X-ray images. Today, armed with high-resolution MRI/PET images and fiber-optic cameras, the same surgeons can execute surgeries an order of magnitude more complex than those 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:
  • Hospitals are working on 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 can orchestrate appointment slots so 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%. 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 three to five 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 a 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 it automatically presents the four or five highest probability queries you intend to submit. Imagine looking at each overnight patient, finding the 1,000 patients over the last two years who entered the hospital with a similar diagnostic or procedure code and then reviewing their “flight path” through the hospital (i.e., number of 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 help anticipate the evolving unit capacity situation over the next five to seven 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 airport operations, air traffic control and sophisticated scheduling 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:

  • For a single flight to transport passengers safely from point A to point B, it requires the “above the wing” services (boarding, food, crew) and “below the wing” services (baggage, fuel, tire check, other inspections) to come together seamlessly. Similarly, 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.
  • Every day, at any busy airport, tens of thousands of passengers  navigate their personal journey across connecting flights while relying on “invisible supporting services” such as bag transfers and re-bookings in the case of delays, weather systems, etc. Similarly, on any given day in a busy hospital, 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 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 each day in the 1960s to managing thousands of takeoffs and landings a 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 the “legs of their flight” across the continuum of care.

This post was written by Mohan Giridharadas, the CEO of LeanTaaS. 

How to Reach Millions With Life Insurance

The availability of rapid diagnostic technology and the dramatic growth of retail healthcare has converged to create opportunities for the life insurance industry to attract and serve millions of consumers who are uninsured. Increasingly, consumers are visiting retail locations for healthcare. Life insurers stand to benefit in both the short and long term by taking advantage of the convenience of retail healthcare and the availability of rapid testing to speed underwriting.

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Rapid Tests Meet Consumer and Insurer Needs

In the past five years, minimally invasive rapid diagnostic testing has been revolutionized. Its accuracy, speed and ease-of-use have made it a perfect fit for the retail health environment. Rapid tests require only a small drop of blood or an oral swab, deliver accurate results in minutes and meet stringent FDA guidelines. Tests, such as A1c for diabetes or cotinine for smoking detection, can be combined into one kit for ease of use and distribution. And, because results can be seen immediately, rapid tests meet consumer expectations of speed by eliminating the delays inherent in the central lab process.

Faced with declining sales, forward-thinking insurers and reinsurers are using these new tools and processes to enable rapid issue of insurance. And, when combined with more traditional measurements such as height, weight and blood pressure, rapid tests provide insurers with the information they need to make accurate and quick decisions on a life insurance application. The data can be electronically transferred from the retail site to the insurer to enable immediate, rule-based decisions. As a result, an insurance offer can quickly be delivered to the consumer—often by the time he or she arrives home—delighting consumers and shrinking the life insurance underwriting process considerably.

Growth of Retail Healthcare Creates Reach into Neighborhoods

Retail pharmacies and urgent care clinics are quickly becoming neighborhood clinics. They are able to provide a broad range of services, with the majority offering health screenings and wellness services to fulfill the growing consumer demand for affordable, accessible healthcare in a convenient and professional setting.

This trend is one that we can expect to grow and broaden. According to Accenture’s recent analysis, “Walk-in retail clinics, located in pharmacies, retail chains and supermarkets, will add capacity for 25 million patient visits in 2017, up from 16 million in 2014.” The Urgent Care Association of America reports similar growth. There are now 7,000 urgent care clinics in the U.S. that see three million patients each week.

A New Process for a New Generation

The availability of rapid diagnostic testing in retail settings offers a unique opportunity for life insurers to address several challenges in the application process that are cumbersome to today’s consumers. Many of these consumers simply disappear because the insurance process takes too long. Rapid testing speeds the delivery of results to the insurer so it can quickly make an offer to the consumers. Consumers are able to complete testing in a convenient and professional setting.

In an age where speed of information is not only expected but demanded from consumers, this new paradigm provides insurers and reinsurers with a process that consumers will applaud with their loyalty and their life insurance dollars.


Medical Marijuana’s Growing Pains

Since California led the way in 1996, 23 states and the District of Columbia have legalized medical or recreational marijuana sale and use. In 2016, several states are considering bills that would legalize medical marijuana, reduce jail time or fines for possession and amend existing marijuana laws. In 2014, Congress even put its support toward medical marijuana and hemp growers in the omnibus bill.

As the medical marijuana (MMJ) industry grows beyond infancy, so does the scrutiny of its business liabilities. It seems every week brings a new growing pain for the industry. Here are three important liability concerns that you and your clients should be considering.

Product Liability

Product liability insurance is typically excluded from general liability policies for MMJ dispensaries and grow operations. This is for a couple of reasons: (1) the illegality of the product on a federal level and (2) lack of FDA approval for marijuana for consumption.

Product liability is an essential coverage for MMJ operations as it protects them in the event of claims because of illness or injury from cannabis products. These claims are on the rise as more individuals are exposed to MMJ, particularly when those individuals experiment with various ways of consuming THC.

A class action filed in Colorado in 2014 (Coombs v. Beyond Broadway) alleges that people became ill after eating THC-infused chocolate samples at an event. The class action is open to all attendees who may have been served at the event, so the demand and settlement could be dramatic.

This claim would be handled under the product liability policy. This coverage is available as a stand-alone product, though some carriers may be willing to package it back in with the general liability and rate it separately.

Product Recall

In the Wild West that is the cannabis industry right now, a trend is emerging: product recall.

Cannabis products are being recalled at an alarming rate. Denver alone has recalled 13 products in 13 weeks, including a vape pen oil containing a dangerous, banned pesticide. In October 2015, a number of products were recalled because of banned pesticide content.

Product recall is expensive, and none of those expenses are covered by product liability insurance. In fact, in nearly all of the product recall cases in Denver, no one was sickened by the pesticide-laden products. Cannabis purchased to make the products was independently tested by the manufacturer and voluntarily recalled.

Independent third-party testing is important for quality control, especially in the marijuana industry. When every media outlet and government organization has their eyes on your clients, they need to be one step ahead, so testing product before shipment or sale should be part of any risk management plan.

Product recall insurance is becoming essential. This coverage is written on a manuscript basis to fit the needs of your client and can cover everything from retrieval and shipping costs to destruction costs and even provide public relations help to rebuild and maintain the insured’s reputation.

Professional Liability

With medical cannabis, the dispensary takes on the responsibility of a highly regulated pharmacy. Insureds may be compliant with all state and local rules and regulations, but mistakes do occur. The most common are:

  • Failing to give the correct product to the patient or an authorized caregiver.
  • Failing to confirm the identity of the patient or caregiver before dispensing.
  • Failing to protect patient privacy.

All of the above and more can be covered with a properly written professional liability or E&O policy. Protecting patient privacy can also fall under cyber liability, which your clients should also be concerned about.

MMJ business owners have the same concerns as any other business: profitability, legality, providing a valuable service to the community. As insurance professionals, not only must we look beyond the nature of the business to see the similarities, but also the industry-specific concerns.


The PBM vs. the Drug Manufacturer

In today’s American healthcare system, employers can’t order Lipitor directly from Pfizer fortheir employees. Instead, employers and employees are forced to buy drugs through a middleman, the pharmacy benefits manager (PBM).

Fingers have long been pointed in both directions to blame the other for the high cost of prescription drugs. The PBMs blame the drug manufacturers, and the drug manufacturers blame the PBMs, not unlike two children arguing on the playground.

Eli Lilly, one of the world’s largest drug manufacturers, recently claimed that the average price increase on Humalog, its injectable insulin used to treat diabetes, has only been a modest 1% to 2% annually over the last five years. Tim Walbert, the CEO of small drug manufacturer Horizon Pharmaceuticals, said in a recent interview, that he expects the company’s actual price increases to be 4% or less over the next year.

PBMs, on the other hand, portray the drug manufacturers as greedy price gougers that fail to keep prescriptions costs under control. Anthem, one of the nation’s largest health insurers, works hard to convince its employer clients to leverage the buying process by joining Anthem’s negotiated PBM program with Express Scripts Inc. (ESI) instead of negotiating a direct deal with a PBM. This month, however, Anthem came out swinging, accusing its partner ESI of more than $3 billion in overcharges – all of which were passed along and paid by clients.

Who should the employers believe is at fault? Employers are aware of their prescription benefit bills. They clearly see that costs are escalating at an unprecedented rate. What can they do about the problem? How can they succeed if a buyer as large as Anthem failed for its thousands of employer clients?

Today’s healthcare market only permits employers to buy the employee drugs from two different platforms. They can choose to buy through a PBM partnership (Anthem partnered with ESI) or a large benefits broker’s partnership with a PBM. Secondly, they can choose to work with a consultant for high-level advice and contract directly with a PBM.

Regardless, the employer always gambles that it knows more about the PBM’s 120-page contract, pricing calculations and methodology than Anthem apparently did. It is a monumental sign of the times that Anthem publicly blamed ESI for its failure to contract effectively with the company, leading to overcharges for its clients.

Our healthcare system today is broken by design – not necessity – and virtually everyone in the chain lacks the incentive to fix it. In fact, people are financially motivated to maintain the status quo. Until drugs can be purchased directly from the manufacturers for a direct discounted price, employers are trapped in our national prescription benefit system.