Lab Soft News

More Thoughts About HistoQuery: a Tool for Crowd-Sourcing Surgical Pathology Cases

In a recent note, I discussed HistoQuery, a crowd-sourcing web site to which surgical pathology case images can be uploaded (see: A New Tool for Obtaining Crowd-Sourced Diagnoses for Pathology Images). It was developed by Dr. Ulysses Balis and colleagues at the University of Michigan Medical School and intended as a pathology teaching resource. It seems to me that, in addition to such an audience, surgical pathologists from around the world could potentially use the site to obtain a consensus diagnosis for cases from his or her daily workload. The question then arises as to what type of cases would be uploaded to the web site to seek a crowd-sourced diagnosis for a case.

For me, it seems clear that such cases would most likely be those for which the submitting pathologist is leaning in a particular direction for the diagnosis and then seeking a validation, albeit crowd-sourced and therefore not attributable to any one named colleague or consultant. Contrast this with a challenging case encountered by a pathologist who then seeks a consultation from an eminent pathologist or pathology department. Here such a case would certainly be sent to specific consultant (see: "Eminence-Based" Surgical Pathology and the Digital Pathology Department). In return, the submitting pathologist receives a signed report from an expert (generally) with a specific diagnosis. On to the next case!

In addition, I need to give more thought to the question of the appropriateness of physicians in developing countries with an acute shortage of pathologists uploading surgical pathology images to HistoQuery. In such countries, there may be an insufficient number of qualified pathologists to whom such cases could be sent or it may take many weeks to obtain a surgical pathology report. Is a crowd-sourced diagnosis better than none at all? In addition, images submitted from another country would be beyond the reach of U.S. regulatory authorities. However, we need to focus here on what is ethically correct. Is a crowd-sourced consensus from unnamed parties superior to no diagnosis at all for a patient?

CVS's Grand Strategy for HealthHUBs and Aetna Becoming Apparent

In a previous blog note, I reported that CVS was launching a new type of retail health facility in Houston called a HealthHUB (see: CVS Expands Its Healthcare Presence with Its New HealthHUB Stores). The first store has now opened in Houston and was discussed in detail in a recent article (see: CVS Wants to Make Your Drugstore Your Doctor). These facilities are designed to offer a greatly expanded set of health services including wellness. Below is an excerpt from the Fortune article. It's very long so I can cover only a portion of it.

[CVS] CEO Larry Merlo is working to transform CVS into a wellness pioneer—far from its origins as a drugstore.Today the master plan is to combine Aetna’s claims data and analytics that identify which of its 22 million members are at risk for, say, developing diabetes or cardiac disease (see: CVS Health Completes Acquisition of Aetna, Marking the Start of Transforming the Consumer Health Experience), with CVS’s capacity to guide them to testing and treatment before the disease progresses, at the pharmacies and HealthHUBs. That formula is targeted to generate big savings by curbing readmissions to hospitals and getting patients to adhere to their drug regimens....[Part of the plan consist of] recruiting CVS’s biggest customers, mostly other insurers, to adopt a fresh model that gives CVS a share of the potentially giant savings to come from counseling and screenings at their corner clinics that keep patients out of operating and emergency rooms....

[I]n a May  1 conference call in which CVS announced better-than-expected results, Merlo discussed plans to make HealthHUBs the cornerstone of the new CVS..... [The company] operates 9,900 drugstores, standing in a virtual tie with Walgreens as the nation’s largest drugstore owner....The new CVS is focusing on where the money is: managing chronic conditions. Between 50% and 60% of all adults suffer from one or more of the top five: diabetes, hypertension, cardiac disease, depression, and asthma. Those conditions account for 80% of America’s $3.5 trillion in annual health care spending. The new model’s Rx is to hand Aetna’s data on every detail of its members’ health—including hospitalizations, lab tests, and doctors’ diagnoses—to the pharmacists and wellness specialists who encounter those folks in person far more often than anyone else in their health care orbit.

Stay tuned as to whether CVS and its CEO Larry Merlo can execute this grand strategy. On the face of it, this seems to be the company's new health model: (1) numerous convenient locations; (2) the analytics capability of a large health insurance company; (3) an emphasis on wellness; (4) an emphasis on the treatment of chronic diseases which some health systems treat piecemeal using multiple specialists and often without coordination. I don't think that most of the large health systems can or will be able to respond to this CVS challenge in a coordinated way. Most of them have engaged in only a half-hearted way to wellness (see: New National Patient Survey Reveals 84% Would Increase Provider Loyalty with More Holistic Health and Wellbeing Support).

A New Tool for Obtaining Crowd-Sourced Diagnoses for Pathology Images

Below is an announcement from Dr. Ulysses Balis, Professor of Pathology and Director of Pathology Informatics at the University of Michigan Medical School in Ann Arbor, Michigan:

The University of Michigan Department of Pathology, Division of Pathology Informatics is very pleased to introduce a new educational tool for the global Pathology Community: www.Histoquery.org.

With use of this new crowd-sourcing tool, following a simple and fast user registration process, users will be able to post educational cases to the site (comprised of one or more pathology images and an associated diagnostic question), and then be able to receive a crowd-sourced answer in a matter of seconds. Similarly, users can log on to the site as a crowd-sourcing participant, with the option of either providing a new answer or providing a vote on an already-submitted answer. Every respondent gets one vote, which may be changed as needed (but, no ballot stuffing is possible).

An example of how this educational tool might be helpful would be: seeking consultation from your colleagues on a particularly difficult case in a weekly slide teaching set. Simply post an image or two along with any salient history (but no protected health information, please) and simply wait for the flow of answers and their associated histogram to appear, in real time. Result answers and counts are tabulated in real time for all to see, as a dynamically updated histogram and consequently, no web page updates or refreshes are ever required.

Just as much fun as submitting questions is the opportunity to respond to them! Users may assist in the answering of as many questions as they would like, in the sub-specialty or specialties of their interest. In responding to submitted questions, you become an important and essential component of the crowd-sourced equation! Background: The site is designed with the latest audience-participation, real-time web architecture and will easily accommodate hundreds of concurrent users without skipping a beat. When registering, you will have the choice of selecting as many pathology sub-specialties as you would like, for case monitoring purposes.

Cases may be submitted at any time to any available sub-specialty board, independent of the monitoring board elections that you have previously logged. An all-board-monitoring category is also available, for those who feel confident in assisting with any type of inbound question. Please forward all user support questions to me directly, at: ulysses@med.umich.edu There is no fee to use this educational service and I very much look forward to hearing back from you on your initial experiences with the use of this new class of educational tool.

I think that this is an incredibly useful idea by Ul and his colleagues. We all know about the value of crowd-sourced knowledge. As noted in the note above, the web site will serve as a valuable education tool for residents and fellows as well as practicing pathologists.

Need for More Verbal Communication Between Pathologists and Radiologists

I first discussed the merits of collaboration (or even merger) between the specialties of pathology and radiology in October, 2006 (see: Ten Reasons for Merging Pathology/Lab Medicine with Radiology). Since that time, I have commented on this same topic from time to time as well as how to improve pathology/radiology reports (see, for example: Radiologists and Pathologists as Information Specialists; Merger of the Specialties?; Making Surgical Pathology and Radiology Reports More Patient-Friendly; Pathology and Radiology Collaborate with a Concordance Conference). I recently came across a longish article encouraging pathologists to communicate verbally with radiologists when certain findings are encountered. Below is an excerpt from it:

Discordance between radiology and pathology studies compounds...[the problem or arriving at the correct diagnosis]. As good practice, frequent verbal communications between pathologists and radiologists ensure that we recognize and minimize potential diagnostic confusion prior to issuing the pathology report. The radiologist-pathologist conversation may provide valuable feedback to the radiologist (because the pathologic findings may add to their understanding of imaging findings and improve interpretive skills), and to the pathologist (who may develop a better appreciation of the clinical question asked with the biopsy and provide a more relevant pathologic answer).....Importantly, the radiologist...[is] able to convey the degree of suspicion for malignancy much more thoroughly to the pathologist in direct conversation than in the biopsy requisition, which has only limited ability for nuanced communication.....

When should a pathologist contact the radiologist? We propose the following situations: (1) negative/benign biopsy result, (2) surprising/unexpected finding on the biopsy, (3) clarification of specific requests for the biopsy, and (4) insufficient material on the biopsy. We define a negative biopsy as one that “is negative for any pathologic process or contains only nonspecific findings.” In this setting, sampling during the biopsy may have missed the intended lesion, or the findings may reflect a process that is not the radiographic lesion of concern (for instance, inflammation adjacent to a malignancy). Additionally, the biopsy may have been performed on an atypical or unusual radiographic presentation of a typically benign process with a lower level of suspicion for malignancy, such as an apical cap or rounded atelectasis. Without an assured radiologic-pathologic correlation comment, the patient may be subjected to an additional, unnecessary needle biopsy or even a more invasive surgical biopsy....

Suggested Reading

J Sorace et al., “Integrating pathology and radiology disciplines: an emerging opportunity?”, BMC Med, 10, 100 (2012). PMID: 22950414.

AS Oh et al., “Imaging-histologic discordance at percutaneous biopsy of the lung”, Acad Radiol, 22, 481–487 (2015). PMID: 25601302.

National Academies of Sciences, Engineering, and Medicine, Improving Diagnosis in Healthcare. The National Academies Press: 2015.

The suggested readings above may be of value but, unfortunately, there is not a large literature on this topic. The most important steps to take to improve pathology-radiology communications would be to develop specific guidelines when oral communication is required for a case, as discussed above, and the launching of a periodic hospital diagnostic concordance conference (see: Pathology and Radiology Collaborate with a Concordance Conference).

Grail Picks Specific Method for Liquid Biopsy Clinical Trials

I have blogged about the liquid biopsy for about eight years (see, for example, Rapid Adoption of the Term "Liquid Biopsy" on the Web; Continuing Discussion about the "Liquid Biopsy"; "Liquid Biopsy" Used to Refer to Detection of Any Serum Cancer DNA). Initially, the test involved the isolation of circulating cancer cells from a blood sample, a daunting task due to their rarity, and the subsequent analysis of the DNA of them. Liquid biopsy research has evolved since then to the collection and analysis of cell-free circulating DNA and RNA. A recent article discussed how Grail, the apparent leader in the commercialization of the technology, has selected what it deems to be the best method for cancer detection in the blood (see: Grail, the liquid biopsy startup, picks an approach for its cancer test). Below is an excerpt from the article:

Grail, the company that has raised $1.6 billion to develop a blood test to detect cancer early, announced Monday that it has settled on a method for its test and enrolled 115,000 patients in two large clinical trials. A third study, with 50,000 patients, has already begun. The San Francisco-based startup, spun out of DNA sequencing giant Illumina in 2016, previously presented data on three different approaches to detect cancer using DNA circulating in the blood. One used DNA sequencing to look at specific regions of genes; a second used the technology to sequence the entire genomes of cells floating in patients’ bloodstreams; and a third looked at methylation, an epigenetic change across the whole genome. Grail said in a statement that it has settled on the third method, which uses methylation to detect cancer and identify where in the body it started.....The company also said that it had received a special “breakthrough device” designation from the Food and Drug Administration that could expedite the test’s eventual review. 

Here's a paragraph from the Grail landing page about how to analyze cancer signals in blood:

An increasing body of evidence suggests tumors release cell-free nucleic acids (cfNAs) into the bloodstream. cfNAs are small fragments of DNA and RNA, and reflect the genomic features of the tumor from which they originated. cfNAs are thought to be a direct measure of cancer and can be detectable in the bloodstream of people with cancer, potentially even before symptoms present.

All of this seems promising. Grail certainly has generous funding and is enrolling large numbers of patients in its clinical trials. However, I suspect that, even with its “breakthrough device” classification by the FDA, we won't have good answers about this technology for several years. Here's a brief description of the science of DNA methylation patterns (see: DNA Methylation and Its Basic Function):

In the mammalian genome, DNA methylation is an epigenetic mechanism involving the transfer of a methyl group onto the C5 position of the cytosine to form 5-methylcytosine. DNA methylation regulates gene expression by recruiting proteins involved in gene repression or by inhibiting the binding of transcription factor(s) to DNA. During development, the pattern of DNA methylation in the genome changes as a result of a dynamic process involving both de novo DNA methylation and demethylation. As a consequence, differentiated cells develop a stable and unique DNA methylation pattern that regulates tissue-specific gene transcription.

Watson's Patient Synopsis Tool, a Partial Solution for Physician Burnout?

As far as I can tell, IBM's Watson has not lived up to the expectations of many healthcare providers which was probably predictable, given the exuberance of its marketing campaign. Too many promises. However, a recent article about its use in creating a Patent Synopsis caught my eye. This would appear to be a potentially useful tool given the complexity of EHRs and the inability of users to often extract usable information from it (see: IBM Watson is taking on EHR qualms with a new AI tool). Below is an excerpt from this article:

AI-enabled programs that help parse through data have the potential to minimize physicians' headaches with EHRs and ultimately slash costs. Patient Synopsis simplifies data extraction, which can help minimize medical errors resulting from physician burnout. Patient Synopsis pulls out clinically relevant information from a patient's EHR in seconds — including current illnesses, medications, and medical history — and organizes it into 12 categories....By eliminating the need for doctors to dig through mounds of patient data, hospitals could avoid these errors and save a huge amount of money....The tool could also help doctors expedite treatment plans and clamp down on unnecessary testing. Patient Synopsis offers comprehensive, easy-to-access patient summaries, which could grant physicians a clearer view of the best plan of action for treatments and more quickly send patients onto the road of recovery. This could also help prevent doctors from ordering tests that might not be needed to guide effective treatment....

Watson...struggled to retain major hospital clients in 2018, citing softening demand....But its AI-enabled solution for EHRs could help the tech titan lay claim to more healthcare partners, considering 92% of regular hospitals in the US have implemented EHR systems and likely all share in the struggle of managing the burdensome content they contain. Landing Patient Synopsis in Hardin Memorial Health puts IBM in competition with other tech giants offering solutions to combat EHR-related troubles. Leading cloud vendors...have all jumped in to offer solutions to EHR woes: For example, Microsoft's Healthcare Next program seeks to mitigate the burden of inputting EHRs for physicians, and Google's Cloud for Healthcare platform is designed to simplify the process of pulling insights from patient data stored in the cloud to improve clinical decision-making....[IBM] joined forces with other tech leaders last August to tackle interoperability issues plaguing the healthcare system, which often stem from difficulties in sharing EHR data among providers. Now that IBM's doubling down on curbing EHR troubles, it will likely eye more hospital partnerships to emerge as a front-runner in the crowded market.

Although it may not be obvious to all observers, the goal of IBM's Patient Synopsis project, extracting clinically relevant information from the EHR "soup", is a daunting task. Clinical documentation with EHRs is commonly entered using drop-down menus with a controlled vocabulary, making computerized synopsis relatively easy. A similar story pertains to medications and numerical data from the labs. However, surgical pathology and radiology reports, although labeled with diagnostic codes, may harbor subtleties in text that can be important. Such textual reports require computerized natural language processing (NLP) to extract meaning. I have posted a number of previous notes about this topic (see, for example, Assessing Drugs Using "Real World Evidence" in Addition to Clinical Trials; AI Allows Computers to "Read" EHR Records and Make Predictions; Amazon Launches New Medical Record Language Processing Service). Because the EHR record for an individual patient can be complex, the risk of the creation and availability of a patient synopsis generated by Watson is that clinicians may come to depend on it and miss some of the information contained in the larger EHR record. Hence and at least initially, the Patient Synopses will probably be published with a warning label.

Six-Lead, Consumer-Facing ECG Device Close to Release to Market

I have previously blogged about AliveCor's three-lead ECG device in particular and cardiology in general as examples of cardiology being a "collision point" between consumer IT and the healthcare system (see: Three New Studies Confirm Clinical Utility of AliveCor’s KardiaMobile Device and AI Algorithms; Cardiology as a "Collision Point" between Consumer IT and the Healthcare System). The company is now testing a a six-lead ECG instrument (see: AliveCor previews next product: A 6-lead smartphone ECG). Here is an excerpt from this article:

AliveCor, which has offered an smartphone-connected ECG since 2012 and a wristworn version since 2016, revealed...the company’s next direction: a six-lead ECG compatible with a smartphone.....The device is not yet cleared by the FDA...[and] codenamed Project Triangle, looks similar to AliveCor’s existing Kardia Mobile device....But in addition to the two electrodes on the front of the device, there’s an additional one on the back. The user places on thumb on each of the top electrodes and places the bottom electrode on their left knee. This formation — known in cardiology as an Einthoven Triangle after Willem Einthoven, the inventor of the electrocardiograph — allows cardiologists to view electronic activity in the heart from six angles, or leads. With these, the device can potentially catch the warning signs of an upcoming heart attack.....Both AliveCor’s existing products...are single-lead ECGs, as are most similar devices on the market.

Here's a tweet from Dr. David Albert from May 10, 2019 about this new six-lead device:

I am proud that our new @AliveCor KardiaMobile 6L provides a new level of diagnostic cardiac rhythm information. Thanks to Dr. Topol for helping us evaluate and validate this new clinical capability.

This consumer-facing ECG instrument is a good example of the development arc of such products over the period of about seven years in the case of KardiaMobile. It's a story of increasing diagnostic functionality to the point, now or in the near future, when it will loop back to even be deployed by cardiologists in certain clinical settings. Cardiologists may find it more efficient to use the device in their office or ED rather than the standard instrument to obtain an answer quickly. In some developing countries, it may be the only ECG instrument available because of cost. A similar story applies to portable ultrasound devices that utilize the screen of a smart phone (see: Wireless Portable Ultrasound). Here's my note on the topic of portable ultrasound devices from almost thirteen years ago (see: Portable Ultrasound Devices Introduced into Medical School Curriculum). It should be emphasized that the KardiaMobile devices are equipped with AI to interpret the tracings for consumers. This AI feature obviously will not be required by the cardiologists who use it.

Sonic Use Lab Data and Analytics to Provide Important Information to Patients

Sonic Healthcare USA is the world’s third largest pathology/laboratory medicine company with operations in eight countries. A recent article discussed how the company is now using lab test data generated in its labs for predictive analytics focusing on patients with neglected diabetes. The various laboratories then directly contacted these patients, urging them to seek medical treatment  (see: Sonic Healthcare Uses Test Data to Create Shared Savings Opportunities for Clinical Laboratory and Providers). These actions, in turn, reduced medical costs by this early intervention. Below is an excerpt from the article:

Using integrated financial and clinical analytics, Sonic is developing technologies to build clinical decision support tools for its provider and health plan clients. The providers and health plans use those tools to engage patients and help them manage their health....Sonic uses lab test results and financial data to show providers how some low-cost patients over time can become high-cost patients....Sonic [then] uses lab test data to help physicians identify low-cost patients who may need certain interventions before they become high-cost patients. Providers have opportunities to intervene with patients who have renal failure, congestive heart failure, ischemic heart disease, diabetes, and other conditions....

Over several years, Sonic has worked with one client provider group to identify patients who would benefit the most from interventions to prevent their healthcare costs from rising sharply....Sonic found that about two-thirds of high-cost patients were in the low-cost group just one year earlier.....[T]he reason they were low cost the year before was not because they were healthy. Instead, it was because they did not engage with the healthcare system for their chronic disease management.....Sonic used its iMorpheus data analytics system to show that 26.5% of the low-cost patients had diabetes, but that they had not seen their physician in the previous 12 months....[T]he company developed an automated interactive voice response system to call the patients. Sonic recorded the patients’ physicians and then had the system place the calls. Of the patients who received the calls, 44% responded and visited their physicians.

I have posted a number of previous notes about predictive/diagnostic analytics (see, for example: Diagnostic and Predictive Analytics and Their Possible Link to the Future of the LIS; Three Examples of Predictive Analytics in Clinical Practice). However, I was pleasantly surprised to discover via the cited article above that a global laboratory company had both developed a proprietary data analytics system, iMorpheus, and was then using the system to predict/diagnose disease. Ultimately, the patients were alerted about this finding with an automated telephone system using their physician's voice.

All of this is both admirable and highly unusual. Although clinical labs enable clinicians to make myriad diagnoses with lab test results, they seldom take a proactive role in predicting and diagnosing disease in patients. Sonic's program provides early insight into the potential value of  various types of analytics software installed in the labs. In a recent note, I also discussed how analytics will play a critical role in the future for the professional career of pathology informaticians (see: The Future for Pathology Informaticians Inexorably Linked to Analytics). This fascinating news from Sonic reinforces this same idea. 

Dipstick Technology Approved by FDA for Urinalysis at Home

I seem to have lost track of advancements in dipstick technology understanding that it is well established in such areas as pregnancy testing. Home glucose testing for diabetics, of course, is growing increasingly sophisticated with the test strips being read by a meter. A recent article indicated that this meter technology can be replaced by the camera in a smartphone for recording and analyzing dipstick color changes when screening for kidney disease (see: FDA approves smartphone camera–based dipstick product). Below is an excerpt from the article:

Healthy.io, based in Tel Aviv, Israel, has obtained U.S. Food and Drug Administration (FDA) marketing clearance for its first digital dipstick kit offering. The Dip.io is advertised on the company’s website as having the accuracy of “the standard lab-based urinalysis analyzer.” ....The urinalysis dipstick includes 10 determinations, including indicators for possible CKD, on the strip....Dip.io home testing for protein, glucose, and blood in urine can be enormously helpful for patients. It is also a welcome tool helping improve diagnosis and awareness of chronic kidney disease....The dipstick uses “computer vision algorithms” and a “unique calibration method” to ensure accurate reading and interpretation of results, accounting for the many different smartphone cameras available and “infinite lighting conditions,” according to the company’s website. A photo of the dipstick with its color changes as test results will then be automatically sent to a patient’s electronic medical record so a clinical professional can follow up.

Given that there is now at least one way for consumers to accurately image and interpret dip stick color changes without an expensive and specialized meter, look for consumer-oriented dip stick technology to rapidly increase. Ultimately, I think that there will be some sort of monthly health monitoring regimen available to consumers with perhaps multiple types of dipsticks and also home-based mini-analyzers. The results, including the dip stick images, will be uploaded to the consumer's health record in the cloud. There will be a distinction made between consumer-generated test results and those generated by healthcare professionals. Computerized analytics will surveil both the professional and consumer portions of the e-health record and generate a warning when significant results are entered. Meanwhile, here are some interesting results about the incidence of chronic kidney disease in the U.S. (see: Kidney Disease Statistics for the United States):

• The overall prevalence of CKD [chronic kidney disease] in the general population is approximately 14 percent.
• High blood pressure and diabetes are the main causes of CKD. Almost half of individuals with CKD also have diabetes and/or self-reported cardiovascular disease (CVD).
• More than 661,000 Americans have kidney failure. Of these, 468,000 individuals are on dialysis, and roughly 193,000 live with a functioning kidney transplant.

The Future for Pathology Informaticians Inexorably Linked to Analytics

The growing popularity of LIS modules such as Epic's Beaker inexorably leads to hospital/health system EHR personnel assuming greater responsibility for most of lab computing. This is in contrast to the past three or four decades when a small group of pathology-based informaticians and their staff exercised control over the LISs running in pathology departments. This turn of events prompted the following question in my mind: what professional activities will occupy the time and mind-share of the growing body of pathology informaticians now?

Fortunately, there are two large challenges that will now be assigned in part to pathology informaticians. First will be the need to automate anatomic pathology to the same level as clinical pathology, accompanied by the deployment and LIS integration of digital pathology. The second challenge will be the development and testing of the four types of analytics tools that are now coming to the forefront. Here is an excerpt from a recent note on this topic (see: Healthcare Will Contribute a Sizable Portion of Future Earnings for Apple):

...Apple has launched various projects that will provide the company with broad access to anonymized patient EHR records....The value of these records for Apple is that they can be the basis for the development of four types of analytics research: descriptive, diagnostic, predictive, and prescriptive (see: 4 types of data analytics to improve decision-making). Descriptive analytics provides information about what has already happened to a patient, diagnostic analytics provides the root cause of what has happened, predictive analytics predicts what will happen in the future, and prescriptive analytics goes beyond predictive analytics by specifying both the actions necessary to achieve predicted outcomes and the interrelated effects of each decision.

I have previously blogged on what I am now calling descriptive analytics. In these older notes I referred to this category as operational analytics (see: vRad Actively Pursues Operational and Clinical Radiology Analytics). Here is a quote from that note four years ago:

I like to divide lab analytics into two components that I refer to as clinical analytics and operational analytics. The former is patient-centric and addresses questions about testing such as the who, why, when. Operational analytics, as the name implies, addresses operational issues such as workflow, distributed testing, and cost-per-test. The interest in operational analytics is currently greater than clinical analytics with the task usually assigned to supervisors and lab managers rather than pathologists.

There is one overarching reason for pathology informaticians to occupy a large portion of their time with lab-oriented descriptive analytics. Hospital labs are being continuously tasked with the pursuit of value-based care, one of the central pillars of which is to provide higher quality healthcare at a lower price. Lab testing has been one of the greatest bargains in healthcare for decades due largely to increasing lab automation. Descriptive analytics provides a continuous view of key aspects of lab operations such as workflow, labor contribution, and automated instruments. Hence and with various analytics tools, pathology informaticians will be able to make continuous recommendations about how to wring waste out of lab operations and thus pursue even greater efficiency and effectiveness.