Quest Diagnostics: From factory for testing to big data company
When a health care company resembles much more a factory or a big data firm
Quest Diagnostics is company with a remarkable reach out to both patients and physicians. According to their own numbers, they serve 1 in 3 American patients and half of physicians and hospitals. They have one of the biggest, fully automated clinical laboratory facilities in the world. The level of automation, digitization, innovation and scale allowed them not only to be of one the leading clinical diagnostic company, but also positioned them well to venture into the space of big data – the ever-discussed topic, especially in health care.
To give a little bit of background, Quest Diagnostics provide number of clinical tests, ranging from simple blood tests to more sophisticated genetic testing. They serve physicians, hospitals, carry out employer but also wellness testing. Apart from their clinical laboratories, they have more than 2 thousand clinical testing centers all around the US.
Good clinical diagnosis is all about speed and quality. Speed because certain specimens have limited durability; quality as clinical testing often decides literally about life or death of individuals. Quest Diagnostics’ operational model seems to be committed to both, especially when looking into to some of the examples of their efficiency and quality measures:
Logistics: Delivering specimen in a timely manner is crucial. Quest Diagnostics use mostly traditional courier services such as FedEx to do so, but they also have their own flight monitoring system to screen in real-time all the flights carrying their own specimens and conditions on the way that may cause potential delays. If delays are imminent, Quest Diagnostics monitoring team arranges for alternative routing to guarantee on-time arrival of the sample. It can use one of their own 30 aircrafts to do so.
Here is an interesting video of how it works in practice: https://www.youtube.com/watch?v=i58QNRviDUE
Laboratory operations: Laboratories themselves are run as almost fully automated factories. Literally. Blood and other specimens travel on conveyer belts through the laboratory from one testing/control point to another. Laboratories run 24/7, 365 days a year. One of the largest facilities processes 17k specimens a day. Quest Diagnostics use a barcode system to track their samples which allows them simpler storage of data but also tracking of specimen within the testing facility at any point in time.
Scale: Through both organic growth and M&A, Quest Diagnostics have become one of the largest clinical testing providers in the world. The scale of its operations allows Quest not only to lower costs and thus gain competitive advantage in an industry where cost pressure is omnipresent, but also helps guarantee consistency of results. The large database of the previous results also enables the company to accompany the test results with further insights with respect to what is typically seen among relevant population.
With the strong operations and scale in the background, Quest Diagnostics have recently started a next level of thier business diversifying from diagnostics into consumer applications and big data. It, for instance, launched a MyQuest application that allows patients to get results online or on their mobile phones, set up a meeting to get the testing done or track their personal health records. 1.4M patients visited the website/application beginning 2015. The Quest Diagnostics also operate an electronic interface for the physicians and hospitals to facilitate the administration around ordering tests and receiving results.
The big data is yet another step. Quest Diagnostics announced a cooperation with a data company Inovalon in 2015. The idea behind the partnership is to leverage Quest Diagnostics large database of past test results to get a real-time patient specific data and to address gaps in quality and medical history insights. It allows physicians, through Quest Diagnostic platform, to order on-demand advanced diagnostics about a specific patient. In this sense, Quest Diagnostics’ operating model is allowing them to be well positioned to explore new areas of business and is effectively helping start transformation of the company from a lab diagnostic into much more data focused organization.
Finally, it is important to note that while Quest Diagnostics are quite the pioneers in the operational efficiency and big data in health care, they also faced and are facing number of controversies. They were previously charged for insurance fraudulent reporting and most recently are being investigated for a patient data breach. Going forward, it will therefore be very important for them, maybe more than for other companies given the sensitivity of data they are handling, to focus on maintaining the highest integrity in this regard.
Sources:
Quest Diagnostics website
Inovalon website
Fortune magazine
Newman Ferrara LLP
Marie,
Very interesting read! I was quite surprised to learn that Quest Diagnostics models its operations like a factory would, utilizing common process improvement methodologies like Six Sigma to deliver on their business value metrics of quality and speed. I would believe that a very large portion of their expenses are devoted to logistics in order to ensure specimens and samples are delivered to various testing centers in a timely manner. It would be useful to understand its cost structure in terms of maintaining large facilities with testing equipment and experienced personnel and logistics costs and how these various costs play out in Quest Diagnostics value creation process. I found an article which addresses some of the recent challenges Quest Diagnostics and some of its competitors are facing in keeping up sales and generating profits given high pricing pressures set by insurers and government services like Medicare and Medicaid (http://www.fool.com/investing/general/2014/04/14/laboratory-service-providers-should-profit-from-th.aspx). I wonder if Quest can mitigate some of these external pressures by implementing cost-cutting measures to develop even leaner processes within its operations. Also, it is good to know that Quest is considering alternative revenue streams through its ‘big-data’ proposition. I agree that this particular service will do wonders for this business given the advantages in terms of convenience and cost that both physicians and patients can reap. Although, I would be concerned about partnering with third-parties given data-breach issues Quest has faced in the past. Moving forward, I think Quest really needs to think about whether or not it needs to outsource data analytics and if it does how can it make sure that the integrity of data is maintained.
Great post! I liked your discussion on the direction in which the company is moving and how their operating model enables them to pursue business opportunities in adjacent sectors. The market is in sore need of reliable and streamlined electronic interfaces for the ordering of tests and delivery of results. However, I am somewhat skeptical about the potential value of mobile applications targeting patients given the low frequency with which the average person visits any medical diagnostics clinic (once every 1-2 years?). I’m also curious as to your thoughts on what kind of big data insights Quest can derive from its databases, and what kind of monetizing opportunities might lie therein.
Not not interesting. It sure seems that their “fully automated” facilities can operate at a scale that provides significant cost and speed advantages (high throughput allows for investment in technology). However, you note that they also have 2 thousand clinical testing facilities around the country. How do these smaller facilities relate to the larger “factories”? Do they serve merely as places to receive specimans that are then sent to the factory, or do they also test specimans? I’d be interested to know how they determine what is suited for the “factory.” I also like how you mention the recent patient data breach. The main question I often ask when thinking about companies that rely on monetizing data is “who owns the data”? It seems their business model moving forward may be at risk if data ownership is ever legally challenged. Idk though, just a thought I had.