Lab Soft News

Private Equity Firm Acquires Orchard Software; What's the Future for LISs?

I am not overly optimistic in the long run about the future of what I will refer to here as classic LISs, sometimes also called best-of-breed systems (see: Defining a "Best-of-Breed" LIS). The fundamental challenge to their future success is that Epic, with its popular EHR marketed suitable largely for large health systems, has gained substantial popularity in hospital C-suites. Its LIS product, Beaker, has also improved substantially in recent years. CIOs would rather interact with a single company that provides both EHR software and "departmental" software like LISs and RISs. Hence, Beaker continues to gain market share.

Now comes news that Orchard Software has been acquired by a private equity firm Francisco Partners (see: New Player in Market for Laboratory Information System Products Acquires Orchard Software). Below is an excerpt from this article:

Orchard Software, founded in 1993, has grown steadily over the past 20 years, primarily by serving physician office laboratories, community hospital labs, and independent clinical laboratory companies. With each stage of growth, Orchard added functionality to its LIS and related software offerings and moved up-market to serve larger hospitals and larger labs....What makes the purchase of Orchard by a multi-billion-dollar private equity company noteworthy is the fact that it is the first significant transaction in the LIS sector probably since the mid-2000s, which saw several significant mergers and acquisitions.....However, it is accurate to say that many professional investors will be watching to see whether...[Francisco] Partners succeeds with its investment in Orchard Software. If Orchard’s revenue and operating profits increase substantially in the next few years, that may encourage other investors to look for LIS companies and products that they can buy. If this were to happen, that would be a positive development for both clinical laboratories and anatomic pathology groups, because these investors would have a motive to add new functions and capabilities to their LIS products. 

Some pathology health IT observers are seeking to understand the underlying strategy of the purchase of Orchard in light of the increasing dominance of the LIS market by Epic and Cerner. The answer, as noted in the excerpt above, is that Orchard's market focus is cloud-based, clinical laboratory support for physician office laboratories (POLs), community hospital labs, and independent clinical laboratory companies. Not large health systems. It currently is said to have 1,500 customers (see: Francisco Partners to Acquire Orchard Software). So, what is the future for these lab environments? One might respond negatively that many smaller hospitals are going out of business (see: 22 latest hospital bankruptcies, closures) or being acquired by larger health systems. Similarly, many physician private practices are being acquired by large health systems. 

Contrariwise, I am very bullish on the emergence of small reference labs focusing on wellness and genomic testing. They will need software support and the ability to interface with hospital walk-in retail clinic EHRs and perhaps even home-based wearable health ecosystems. Orchard software could support such business models. Although walk-in clinics like CVS's MinuteClinics could theoretically be potential clients for Orchard software, they are currently working with Epic (see: CVS Health expands use of Epic in its operations). Moreover, these walk-in clinics do not currently offer a wide lab test menu but this is about to change as patients with greater disease acuity are being seen. One factor being overlooked in recent articles about the Orchard purchase by Francisco is that it provides software to support point-of-care testing (POCT) which is showing great gains in large health systems, bringing testing close to the clinicians (see: Point-of-Care Diagnostics: Recent Advances and Trends). . 

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. 

A Major Change Now Occurring in Both Clinical Pathology and Anatomic Pathology

I recently returned from the Digital Management Team Conference hosted by Dr. Michael Laposata at the University of Texas Medical Branch in Galveston. It occurred to me during the conference that a major change is now occurring in the fields of anatomic pathology (AP) and clinical pathology (CP). The major change in AP is the deployment of automated tissue processing integrated with digital pathology (see: Integration and Interoperability Are Essential for Growth of Digital Pathology; Possible New Strategic Initiatives for Best-of-Breed LIS Vendors). This automation of surgical pathology will reduce the cost and increase the speed of surgical pathology diagnoses, a requirement in the shift to value-based care.

The major change that is occurring in clinical pathology is the rapidly growing interest by pathologists in collaborating with test-ordering clinicians to arrive at a better and more rapid diagnosis for complex patients. This was only uncommonly the case in the past, partly because pathologists did not want to impinge on the clinicians' turf. This attitude began to shift with the introduction of much more complex testing such as in molecular pathology and genomics. The diagnosis management team (DMT) concept, pioneered by Dr. Laposata, is an example of how to engage experts from pathology in clinical diagnostics. However, one of the barriers to the work of DMTs is the lack of a suite of apps to automate the process. This is essential as the demand for lab consultations from clinicians increases.

The two types of apps that are urgently required to promote the work of DMTs are the following: (1) an app that can query the EHR and LIS for relevant data and create a work sheet, having been supplied with a patient name and the problem precipitating the consult (e.g, liver disease, clotting diathesis, etc.); and (2) an app to auto-order a DMT consultation in specific cases such as the order of an expensive/esoteric lab test(s) or a major order of blood products indicating a surgical or medical emergency. Related to the second type of DMT app is a case-finding software tool that can search for a particular type of patient. One example of this would be a patient with a long hospital stay and no working diagnosis, indicating a diagnostic conundrum. A DMT consult might then enable a faster path to the diagnosis and discharge of the patient. 

Cerner Offers AI Tool to Combat Physician EHR Burnout

The terms EHR and physician burnout have become somewhat synonymous lately (see: 6 stats on EHR-related physician burnout and 7 tips to combat it; WRITE SHORTER EHR NOTES, FIGHT PHYSICIAN BURNOUT). You may have been wondering how the major EHR vendors would respond to such criticism? We now have at least part of Cerner's response as announced at the recent HIMSS conference (see: At HIMSS19, Cerner unveils AI tool to fight physician burnout. Below is an excerpt from the article:

[Cerner has release new software called] Chart Assist, a new AI-enabled workflow, to join a suite of systems designed to reduce physician burnout, enhance the clinician’s experience and increase productivity. Cerner’s suite of AI-enabled systems goes beyond traditional processing and focuses on user efficiency to identify gaps and inconsistencies within the patient record....These advancements will help providers address patient care and validate diagnoses, ultimately helping to reduce the physician workload, while supporting the financial strength of the health system, the vendor contended.....The new AI-driven advancement will complement Cerner’s existing workflows to enhance the physician experience with the goal of bringing real change to the industrywide challenge of physician burnout.

The response, as as one might have predicted, is to develop an AI front-end to the Cerner EHR to hide some of its inefficiencies and workflow entanglements of the product. Interestingly and predictably, Cerner's response does not suggest that the problem rests with its EHR but rather cites "gaps and inconsistencies within the patient record" as the fundamental problem. However, let's put this news aside and ask the next reasonable question: Does Cerner (or Epic for that matter) have the talent and expertise to develop an AI front-end to make physicians' interactions with its EHR less stressful and thus ameliorate MD burnout? I have my doubts but time will tell. Here are some articles for additional reading on this topic: Next up for EHRs: Vendors adding artificial intelligence into the workflow; How Front-End Speech Recognition Adoption Impacts EHR Usability; Epic Clinical Documentation Module Utilizes AI Solution; AI can improve EHRs and analytics, but physician input is a must; Using AI to Improve Electronic Health Records).

Which Healthcare Jobs Are Safe from Replacement by Automation or Robots?

One facet of modernity that is getting more attention is that many of today's jobs will be encroached upon (i.e., replaced) by automation and robots. As one current example, you will not find many people working in modern hospital clinical labs because much of this human labor has been replaced by automated equipment. That's why lab testing is one of the biggest bargains in healthcare today. A recent article addressed the question of whether healthcare jobs in particular are safe from replacement by AI (See: Are healthcare jobs safe from AI?). Below is an excerpt from the it:

Across the country and across industries, workers are nervous that automation and artificial intelligence will eventually take over their jobs. For some, those fears may be grounded in reality. Healthcare, however, looks like it will be largely safe from that trend, a new report from the Brookings Metropolitan Policy Program finds. Examining a chunk of time from the 1980s to 2016, the piece tracks the historical evolution of the technology and uses those findings to project forward to 2030. The verdict? AI will replace jobs in various industries, but not so much in healthcare.... Overall, though, only about 25 percent of U.S. jobs are at a high risk of replacement by automation. That translates to about 36 million jobs, based on 2016 data. A higher percentage, 36 percent, are at medium risk (52 million jobs) while the largest group is the low-risk group, at 39 percent (57 million jobs). Most of healthcare belongs in the medium-to-low categories, largely driven by the complexity of healthcare jobs....Medical assistants have what the report calls "automation potential" of 54 percent, but home health aids have just an 8 percent automation potential. Registered nurses sit somewhere in between, at 54 percent.

The emerging growth areas in pathology and the clinical labs, from my perspective, will be based on the conversion to digital pathology (see: Seeking Funding from Hospital Executives for Digital Pathology Platforms; Integration and Interoperability Are Essential for Growth of Digital Pathology), the automation of anatomic pathology, and the explosion of analytics in all of the clinical labs. The topic of analytics in healthcare was discussed in more detail in a recent post (see: Diagnostic and Predictive Analytics and Their Possible Link to the Future of the LIS). Analytics was described there as bring comprised of the following: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. I have also blogged about what I envision as the future of LISs as being highly related to analytics such that I think they should be referred to LIS-A's (see: Predicting the Future Functions of the LIS-Analytic (LIS-A)).

Proposed Classification for Health Information Systems: Provider and Individual

Recent events and some of my recent blog notes have caused me to question whether we need a new, high-level classification that encompasses all health information systems. I am going to give this idea a try. The goal of such a large scale systematic plan is to understand the functionality of current and future health information systems. Details are listed below:

1. Provider health systems (PHS's): The common feature of PHS's is that they are designed as tools for use by healthcare providers. The largest and most complex of these systems are EHR's which are deployed in hospitals and health systems. Also included in this category are office health systems (OHS's) designed to support the office functions needed by physicians and other providers like nurse practitioners. PHS's provide all the necessary IT functionality in health systems ranging from hospital inpatient units to the PCP's managing ambulatory patients.

Subcategories of PHS's include departmental systems deployed in hospitals such as LIS's, RIS's, and PAC's. In recent blog notes, I proposed that standalone LIS's, as opposed to LIS's that are part of the software suites of EHR vendors, could perhaps evolve into LIS-A's (see: Predicting the Future Functions of the LIS-Analytic (LIS-A); Continuing Discussion about the LIS of the Future; Analytics as a Key Feature?). Such systems would change from classic, best-of-breed LIS's to systems that support descriptive, predictive, and diagnostic algorithms. By developing such LIS-A's, LIS companies may cede ground to the LIS's provided by large EHR vendors as part of their enterprise-wide offerings. 

2. Individual (or personal) health systems (IHS's): These are systems designed primarily for consumers. I blogged recently about the Rally system of UnitedHealth Group that initially provided coaching and other health services (see: UnitedHealth Group to Launch Its Own Variant of an EHR in 2019). It's similar to Apple Health which is an app that runs on iPhones and the Apple Watch. Analytics will be a major thrust in the consumer healthcare market and delivered via IHS's (see: The Future of the LIS: Reflections about Consumers Ordering Their Own Lab Tests) particularly when PHS/EHR clinical data can be downloaded by consumers from their EHR accounts. Apple is already providing such a feature for iPhones in collaboration with selected health systems (see: Apple Has Plans to Copy EHR Records to iPhones and Apple Watches).

3. Hybrid health systems (HHS's): These would be systems such as the one being developed by UnitedHealth Group mentioned above that span physican office health systems (OHS's) and individual health systems. The advantage of such systems is that they fulfill the desire of patients to share health measurements obtained in their homes such as EKG tracings recorded by their smart watches or home lab testing performed by devices that plug into their iPhones. 

As to the future of health information systems, most of the development (and consumer market) is going to be related to IHS's and perhaps relatively simple hybrid systems. This is where consumers will be operating as well as health wearables and home testing devices. EHR's are expensive and accessed only via complex user interfaces. Hospitals will continue to use them because they have no substitute products. Consumers will never use them directly because of their complexity and because of the security concerns of the health systems that own and operate them.

New CMS Reimbursement Guidelines: Support for Provider-to-Provider Consults

Here's a link to a high quality webinar from AmericanWell about telehealth (see: A Guide to Integrating Cross-Continuum Telehealth into your Health System). Starting at about 11:00 minutes in the recording, the discussion turns to new CMS telehealth reimbursement guidelines for FY2019 that include new services such as remote patient monitoring (see: CMS to Reimburse Providers for Remote Patient Monitoring Services). Another new type of service that will be reimbursed is provider-to-provider consultations (see: CMS wants to expand telehealth coverage. Here's what that means for you). Here's the key passage from this latter article relating to provider-to-provider consults:

Internet-based inter-professional consultations: New reimbursement for provider-to-provider consultations via online messaging applications or portals. Both the initiating physician and the consulting physician would be eligible for reimbursement.

I have previously covered diagnostic management teams (DMTs) in Lab Soft News (see, for example: Deploying a DMT at the Front-End of Lab Orders for a Complex Patient). One of the challenges for DMT's in the past has been the lack of reimbursement.There is now a ray of light with CMS-approved reimbursement for provider-to-provider consultations which accurately describes the DMT process. A team of expert diagnosticians provides advice to a clinician caring for a patient about the ordering and interpretation of complex laboratory tests. There are, of course, additional benefits attributable to such consults such as decreased medical errors and decreased length of stay but at least some degree of reimbursement can provide an incentive for launching a DMT team in a health system. 

Does a DMT consultation fall under the general rubric of telemedicine? First of all, I think that collaboration between the members of a DMT, even within the confines of a single hospital, will often take place via telephone or a video call. Secondly, discussion of the case between members of the team and with the consult-ordering clinician may often be more effective using video calls. Plans are now underway in collaboration with Sunquest and Epic to develop an app that will work in the following way after a DMT consult has been ordered: (1) enter the name of a patient into the LIS; (2) select the organ and/or disease state driving the DMT consultation; (3) the app will then interrogate the lab and EHR databases and generate a report with all of the relevant lab test results and clinical findings for the patient that will form the basis for the DMT report.

The Future of the LIS: Reflections about Consumers Ordering Their Own Lab Tests

In three previous blog notes, I have been musing about the future of the LIS (see: Diagnostic and Predictive Analytics and Their Possible Link to the Future of the LIS; Predicting the Future Functions of the LIS-Analytic (LIS-A); Continuing Discussion about the LIS of the Future; Analytics as a Key Feature?). I have raised two major points thus far: (1) a major function of the LIS of the future will be to manage diagnostic and predictive analytics; and (2) it's not clear at this time which type of vendor (LIS, EHR, other) will dominate this new LIS landscape. I have also been referring to the LIS of the future as the LIS-A because of the emphasis on analytics.

In this note, I want to emphasize that consumer preferences will be a factor in lab analytics testing. For about two decades, consumers have been allowed to order their own lab tests using the direct access testing (DAT) option. Although DAT has never accounted for a substantial percentage of total lab testing, it has set a precedent regarding consumer choice in the lab world. Ironically, the ability for consumers to order their lab tests for themselves in Arizona was the result of lobbying by Elizabeth Holmes, the now discredited CEO and founder or Theranos (see: New Arizona Law Expands Number of Lab Tests Consumers Can Order on their Own, Fuels Debate). It's only a matter of time before the majority of states allow DAT because of the growing power of consumerism. Currently, approximately 24 states allow total or limited consumer lab test ordering (see: Direct-Access Testing: A State-by-State Analysis).

A telling point was made in my recent note about consumers ordering a predictive analytics test  (see: Continuing Discussion about the LIS of the Future) and I quote it below:

We already know of one instance where researchers from Massachusetts General are developing a web site that will calculate a disease risk score after a consumer has uploaded his or her DNA analysis results (see: Upload Your DNA to a Web Site to Generate Risk Scores for Five Diseases). Such risk scores certainly fall into the family of diagnostic/predictive analytics.

The important point here is that consumers will continue to be allowed to order their own DNA tests from web sites like 23andMe and AncestryDNA. They then will be able to upload their DNA test result to web sites like to one mentioned above and order, for example, the calculation of a risk score for five diseases. Look for other academic centers to begin to provide such risk scores based on consumer orders. Such options will undoubtedly increase.

The direction that I am headed here is that the lab testing market including analytics will be divided into two sectors: consumer-oriented and physician-oriented. The reason that I think that this is important is that I believe that the Big Tech giants like Apple, Google, and Microsoft (see: What Are the Consequences of Big Tech Entering the Healthcare Market?) who may jump into the lab testing market will surely head for the consumer side. Contrariwise, the comfort zone for companies like EHR and LIS vendors, and even the in-vitro diagnostic (IVD) companies, will be the healthcare/professional side.

Predicting the Future Functions of the LIS-Analytic (LIS-A)

In a recent blog note, I stated that the LIS modules of EHRs such as Epic's Beaker are gaining traction in the market and thus performing the functions of the previously dominant best-of-breed LISs like Sunquest or SCC. I went on to suggest that the vendors of these latter systems should be aggressively working on a new type of LIS to protect their market share. Such a product would undoubtedly include support for lab-initiated diagnostic and predictive analytics (see: Diagnostic and Predictive Analytics and Their Possible Link to the Future of the LIS). In this and future blog notes about this topic, I will use my personal term to refer to such a future LIS -- the LIS-Analytic (LIS-A).

One of the first things that comes to mind regarding the functionality of the LIS-A of the future is that it will be both interdepartmental and collaborative. This is by way of contrast with the best-of-breed LIS's of today which are intra-departmental. Most lab test results are generated currently within the clinical labs without knowledge of the clinical findings of the patients being tested. These results are then been reported back to the clinicians without any feedback from them. I do not say this in any critical way -- the nature of the lab test transaction has always been that clinicians order lab tests and pathology responds to these orders. The clinicians are then responsible for interpreting the results in the context of a particular patient.

Diagnostic and predictive analytics, which I think will be one of the major functions of the LIS-A of the future, will need to take into consideration test results but will also require the monitoring patient outcomes subsequent to a testing episode (see: Appraising laboratory quality and value: What's missing?). This consideration of clinical outcomes is important in order to measure the value of lab testing. It is logical to assume that the acquisition of patient outcome data will be based on an interface with the EHR. I might also add that, in this era of precision medicine, the clinical labs will also be able to generate outcome data. One example would be the absence of circulating cancer DNA in a patient post treatment (see: Circulating tumor DNA).

My crystal ball is cloudy at this time as to how and when this interdepartmental and collaborative data will be collected and integrated into the LIS-A. I do understand, however, that the process will be highly automated and reliant on AI because for the following reasons: (1) test-ordering clinicians will be too preoccupied with patient care to provide such data to the LIS-A; and (2) there may be a substantial delay from the time that any particular test result is generated and the subsequent knowledge of outcome data for that same patient. As a result, I suspect that the LIS-A will be collecting and analyzing patient data for a period after the patient is discharged from the hospital. 

Diagnostic and Predictive Analytics and Their Possible Link to the Future of the LIS

I have posted a number of previous notes about analytics (see, for example: Lab Analytics Emerges as Hot Area for Software Development; Relevance of Lifestyle Analytics for Healthcare Organizations; Much of the Future for Pathology and Lab Medicine Rests with Analytics; Successfully Screening for Lung Cancer Based on Predictive Analytics). I believe that the future of clinical pathology, which is to say the mission of hospital-based clinical labs, will be significantly intertwined with analytics. I had begun to categorize the various types of analytics in previous posts. However, Gartner has come up with a very good schema for understanding medical analytics, which they refer to as the analytics continuum (see: Attending to value and sophistication degree, Analytics is organized in 3 levels) and I show their analytics diagram below:

The Gartner term of descriptive analytics in the diagram above corresponds to what I have referred to in past notes as organizational analytics (see: Much of the Future for Pathology and Lab Medicine Rests with Analytics). By this I mean the collection of data concerning the daily operations of the labs with, for example, the goal of improving such processes as lab staffing or deciding which esoteric tests to send out to reference labs. The Gartner use of the term of descriptive analytics is broader than mine. Labs interested in taking advantage of this type of data can purchase a LIS module such as Sunquest Analytics or a system from a specialized analytics and business intelligence vendor such as Visiun. However, we are now also on the verge of deploying a wide range of diagnostic analytics in pathology, which is to say moving up the slope of the Gartner diagram above.

For various reasons, some of which have been described in previous notes here, much of the hospital market, particularly large health systems, now tends to favor enterprise-wide EHR solutions like that provided by Cerner and Epic. This means that they will often install an EHR and a lab module from the same vendor. As a result of this, such lab modules then perform the standard LIS functions such as order-entry, result reporting, data storage, and billing. What "work" then remains for a classic LIS? Is it to slowly evolve as a type of middleware? My opinion in response to this question is that the classic LIS will function, in part, as a specialized lab system to support diagnostic and predictive analytics. To use Gartner's vocabulary, it will provide diagnostic insight and foresight in the delivery of healthcare. How this will work is speculative at this time but I will try to delve into these ideas in upcoming notes.