News, Analysis, Trends, Management Innovations for
Clinical Laboratories and Pathology Groups

Hosted by Robert Michel

News, Analysis, Trends, Management Innovations for
Clinical Laboratories and Pathology Groups

Hosted by Robert Michel
Sign In

What is Swarm Learning and Might It Come to a Clinical Laboratory Near You?

International research team that developed swarm learning believe it could ‘significantly promote and accelerate collaboration and information exchange in research, especially in the field of medicine’

Swarm Learning” is a technology that enables cross-site analysis of population health data while maintaining patient privacy protocols to generate improvements in precision medicine. That’s the goal described by an international team of scientists who used this approach to develop artificial intelligence (AI) algorithms that seek out and identify lung disease, blood cancer, and COVID-19 data stored in disparate databases.

Since 80% of patient records feature clinical laboratory test results, there’s no doubt this protected health information (PHI) would be curated by the swarm learning algorithms. 

Researchers with DZNE (German Center for Neurodegenerative Diseases), the University of Bonn, and Hewlett Packard Enterprise (HPE) who developed the swarm learning algorithms published their findings in the journal Nature, titled, “Swarm Learning for Decentralized and Confidential Clinical Machine Learning.”

In their study they wrote, “Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. … However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking, and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning.”

What is Swarm Learning?

Swarm Learning is a way to collaborate and share medical research toward a goal of advancing precision medicine, the researchers stated.

The technology blends AI with blockchain-based peer-to-peer networking to create information exchange across a network, the DZNE news release explained. The machine learning algorithms are “trained” to detect data patterns “and recognize the learned patterns in other data as well,” the news release noted. 

Joachim Schultze, MD

“Medical research data are a treasure. They can play a decisive role in developing personalized therapies that are tailored to each individual more precisely than conventional treatments,” said Joachim Schultze, MD (above), Director, Systems Medicine at DZNE and Professor, Life and Medical Sciences Institute at the University of Bonn, in the news release. “It’s critical for science to be able to use such data as comprehensively and from as many sources as possible,” he added. This, of course, would include clinical laboratory test results data. (Photo copyright: University of Bonn.)
 

Since, as Dark Daily has reported many times, clinical laboratory test data comprises as much as 80% of patients’ medical records, such a treasure trove of information will most likely include medical laboratory test data as well as reports on patient diagnoses, demographics, and medical history. Swarm learning incorporating laboratory test results may inform medical researchers in their population health analyses.

“The key is that all participants can learn from each other without the need of sharing confidential information,” said Eng Lim Goh, PhD, Senior Vice President and Chief Technology Officer for AI at Hewlett Packard Enterprise (HPE), which developed base technology for swarm learning, according to the news release.

An HPE blog post notes that “Using swarm learning, the hospital can combine its data with that of hospitals serving different demographics in other regions and then use a private blockchain to learn from a global average, or parameter, of results—without sharing actual patient information.

“Under this model,” the blog continues, “‘each hospital is able to predict, with accuracy and with reduced bias, as though [it has] collected all the patient data globally in one place and learned from it,’ Goh says.”

Swarm Learning Applied in Study

The researchers studied four infectious and non-infectious diseases:

They used 16,400 transcriptomes from 127 clinical studies and assessed 95,000 X-ray images.

  • Data for transcriptomes were distributed over three to 32 blockchain nodes and across three nodes for X-rays.
  • The researchers “fed their algorithms with subsets of the respective data set” (such as those coming from people with disease versus healthy individuals), the news release noted.

Findings included:

  • 90% algorithm accuracy in reporting on healthy people versus those diagnosed with diseases for transcriptomes.
  • 76% to 86% algorithm accuracy in reporting of X-ray data.
  • Methodology worked best for leukemia.
  • Accuracy also was “very high” for tuberculosis and COVID-19.
  • X-ray data accuracy rate was lower, researchers said, due to less available data or image quality.

“Our study thus proves that swarm learning can be successfully applied to very different data. In principle, this applies to any type of information for which pattern recognition by means of artificial intelligence is useful. Be it genome data, X-ray images, data from brain imaging, or other complex data,” Schultze said in the DZNE news release.

The researchers plan to conduct additional studies aimed at exploring swarm learning’s implications to Alzheimer’s disease and other neurodegenerative diseases.

Is Swarm Learning Coming to Your Lab?

The scientists say hospitals as well as research institutions may join or form swarms. So, hospital-based medical laboratory leaders and pathology groups may have an opportunity to contribute to swarm learning. According to Schultze, sharing information can go a long way toward “making the wealth of experience in medicine more accessible worldwide.”

Donna Marie Pocius

Related Information:

AI With Swarm Intelligence: A Novel Technology for Cooperative Analysis of Big Data

Swarm Learning for Decentralized and Confidential Clinical Machine Learning

Swarm Learning

HPE’s Dr. Goh on Harnessing the Power of Swarm Learning

Swarm Learning: This Artificial Intelligence Can Detect COVID-19, Other Diseases

Cancer Researchers Use Astronomy Analysis Algorithms to Develop Platform for Locating and Examining Predictive Biomarkers in Tumors

Yet another example that technologies from non-medical fields continue to find their way into anatomic pathology and clinical laboratory medicine

Anatomic pathologists and medical laboratory scientists may soon have new tools in the fight against cancer, thanks to researchers at the Mark Foundation Center for Advanced Genomics and Imaging at Johns Hopkins University and Bloomberg-Kimmel Institute for Cancer Immunotherapy.

Using algorithmic technology designed for mapping the stars, the scientists have created an imaging/spatial location platform called AstroPath which may help oncologists develop immunotherapies that work best on specific cancers. Such a capability is key to effective precision medicine techniques.

Dark Daily has regularly pointed out that technologies developed in other fields of science will eventually be brought into anatomic pathology and clinical laboratory medicine. Use of the star-mapping technology in oncology and the diagnosis of cancer is one such example.

In “Analysis of Multispectral Imaging with the AstroPath Platform Informs Efficacy of PD-1 Blockade,” published in the journal Science, the multi-institution research team wrote, “Here, we present the AstroPath platform, an end-to-end pathology workflow with rigorous quality control for creating quantitative, spatially resolved mIF [multiplex immunofluorescence] datasets. Although the current effort focused on a six-plex mIF assay, the principles described here provide a general framework for the development of any multiplex assay with single-cell image resolution. Such approaches will vastly improve the standardization and scalability of these technologies, enabling cross-site and cross-study comparisons. This will be essential for multiplex imaging technologies to realize their potential as biomarker discovery platforms and ultimately as standard diagnostic tests for clinical therapeutic decision-making.

“Drawing from the field of astronomy, in which petabytes of imaging data are routinely analyzed across a wide spectral range, [the researchers] developed a platform for multispectral imaging of whole-tumor sections with high-fidelity single-cell resolution. The resultant AstroPath platform was used to develop a multiplex immunofluorescent assay highly predictive of responses and outcomes for melanoma patients receiving immunotherapy,” the researchers added.

Using Star Mapping Software to Fight Cancer

“The application of advanced mapping techniques from astronomy has the potential to identify predictive biomarkers that will help physicians design precise immunotherapy treatments for individual cancer patients,” said Michele Cleary, PhD, CEO of the Mark Foundation for Cancer Research, in a Johns Hopkins news release.

Although the universe we live in and the universe of a cancerous tumor may not seem related, the fact is the same visualization technology can be used to map them both.

“What should be pointed out is that astronomy is mapping the sky in three dimensions, so keeping the spatial relationships while also identify each heavenly body is the goal of these algorithms,” said Robert Michel, Publisher and Editor-in-Chief of Dark Daily and its sister publication The Dark Report.

“Both aspects of that information technology have value in surgical pathology, where the spatial relationship of different cells and cell structures is relevant and important while also having the ability to identify and characterize different types of cells and cell structures. This technology appears to also be capable of identifying multiple biomarkers,” he added.

AstroPath graphic

The image above, taken from the researchers’ Science paper, illustrates the “strong parallels between multispectral analyses in astronomy and emerging multiplexing platforms for pathology.” The researchers wrote, “the next generation of tissue-based biomarkers are likely to be identified by use of large, well-curated datasets. To that end, image analysis approaches originally developed for astronomy were applied to pathology specimens to produce trillions of pixels of robust tissue imaging data and facilitate assay and atlas development.” Anatomic pathologists may be direct recipients of new cancer diagnostic tools based on the AstroPath platform. (Photo copyrights: Johns Hopkins University/Mark Foundation Center for Advanced Genomics/Bloomberg-Kimmel Institute.)

AstroPath Provides 1,000 Times the Information Content from A Single Biopsy

According to the news release, “[The researchers] characterized the immune microenvironment in melanoma biopsies by examining the immune cells in and around the cancer cells within the tumor mass and then identified a composite biomarker that includes six markers and is highly predictive of response to a specific type of an immunotherapy called Anti-PD-1 therapy.”

This is where the use of AstroPath is truly innovative. Previously, researchers could only identify those biomarkers one at a time, through a painstaking process.

“For the last 40 years, pathology analysis of cancer has examined one marker at a time, which provides limited information,” said Drew Pardoll, MD, PhD, Director of the Bloomberg-Kimmel Institute for Cancer Immunotherapy and a Johns Hopkins professor of oncology, in the news release. “Leveraging new technology, including instrumentation to image up to 12 markers simultaneously, the AstroPath imaging algorithms provide 1,000 times the information content from a single biopsy than is currently available through routine pathology,” he added.

More information about a cancerous tumor means clinicians have more tools to combat it. Treatment becomes less about finding the right immunotherapy and more about treating it immediately.

“This facilitates precision cancer immunotherapy—identifying the unique features of each patient’s cancer to predict who will respond to a given immunotherapy, such as anti-PD-1, and who will not. In doing so, it also advances diagnostic pathology from uniparameter to multiparameter assays,” Pardoll said.

Big Data and Data Analysis Is the Future of Precision Medicine

The use of data in science is changing how researchers, clinicians, pathologists, and others provide healthcare in the modern world. When it is properly collected and analyzed, data holds the key to precision medicine’s personalized and targeted patient care.

“Big data is changing science. There are applications everywhere, from astronomy to genomics to oceanography,” said Alexander S. Szalay, PhD, Bloomberg Distinguished Professor and Professor in the Department of Computer Science at Johns Hopkins University, and Director of the Institute for Data Intensive Engineering and Science (IDIES), in the news release.

“Data-intensive scientific discovery is a new paradigm. The technical challenge we face is how to get consistent, reproducible results when you collect data at scale. AstroPath is a step towards establishing a universal standard,” he added.

Should AstroPath prove to be a clinically safe and accurate method for developing precision medicine cancer therapies, anatomic pathologists can look forward to exciting new ways to diagnose cancer and determine the best courses of treatment based on each patient’s unique medical needs.

—Dava Stewart

Related Information

Astronomy Meets Pathology to Identify Predictive Biomarkers for Cancer Immunotherapy

Analysis of Multispectral Imaging with the AstroPath Platform Informs Efficacy of PD-1 Blockade

Astronomy Meets Pathology: An Interdisciplinary Effort to Discover Predictive Biomarker Signatures for Immuno-Oncology

From Stars to Cells: Johns Hopkins Researchers Discover Predictive Spatial Phenotypic Signatures with AstroPath

Astronomy and Pathology Join Forces to Predict Immunotherapy Response: Q/A with Spatial Biology Experts

Damo Consulting Survey Predicts Future Health Network Spending Will Primarily be on Improving EHRs; Could be Positive Development for Medical Laboratories

Survey shows healthcare providers plan to wait for AI and digital health technologies to mature before making major investments in them

Clinical laboratories must develop strategies for connecting to their client doctors’ electronic health record (EHR) systems. Thus, a new survey that predicts most healthcare networks will continue to focus health information technology (HIT) spending on improving their EHRs—rather than investing in artificial intelligence (AI) and digital healthcare—provides valuable insights for medical laboratory managers and stakeholders tasked with implementing and maintaining interfaces to these systems.

According to Damo Consulting’s 2019 Healthcare IT Demand Survey, when it comes to spending money on information technology (IT), healthcare executives believe AI and digital healthcare technologies—though promising—need more development.

Damo’s report notes that 71% of healthcare providers surveyed expect their IT budgets to grow by 20% in 2019. However, much of that growth will be allocated to improving EHR functionality, Healthcare Purchasing News reported in its analysis of Damo survey data.

As healthcare executives plan upgrades to their EHRs, hospital-based medical laboratories will need to take steps to ensure interoperability, while avoiding disruption to lab workflow during transition.

The survey also noted that some providers that are considering investing in AI and digital health technology are struggling to understand the market, the news release states.

“Digital and AI are emerging as critical areas for technology spend among healthcare enterprises in 2019. However, healthcare executives are realistic about their technology needs versus their need to improve care delivery. They find the currently available digital health solutions in the market are not very mature,” explained Paddy Padmanabhan (above), Chief Executive Officer of Damo Consulting, in a news release. (Photo copyright: The Authors Guild.)

Providers More Positive Than Vendors on IT Spend

Damo Consulting is a Chicago-area based healthcare and digital advisory firm. In November 2018, Damo surveyed 64 healthcare executives (40 technology and service leaders, and 24 healthcare enterprise executives).  Interestingly, healthcare providers were more positive than the technology developers on IT spending plans, reported HITInfrastructure.com, which detailed the following survey findings:

  • 79% of healthcare executives anticipate high growth in IT spending in 2019, but only 60% of tech company representatives believe that is so.
  • 75% of healthcare executives and 80% of vendor representatives say change in healthcare IT makes buying decisions harder.
  • 71% of healthcare executives and 55% of vendors say federal government policies help IT spending.
  • 50% of healthcare executives associate immaturity with digital solution offerings.
  • 42% of healthcare providers say they lack resources to launch digital.  

“While information technology vendors are aggressively marketing ‘digital’ and ‘AI,’ healthcare executives note that the currently available solutions in these areas are not very mature. These executives are confused by the buzz around ‘AI’ and ‘digital,’ the changing landscape of who is playing what role, and the blurred lines of capabilities and competition,” noted Padmanabhan in the survey report.

The survey also notes that “Health systems are firmly committed to their EHR vendors. Despite the many shortcomings, EHR systems appear to be the primary choice for digital initiatives among health systems at this stage.”

Some Healthcare Providers Starting to Use AI

Even as EHRs receive the lion’s share of healthcare IT spends, some providers are devoting significant resources to AI-related projects and processes.

For example, clinical pathologists may be intrigued by work being conducted at Cleveland Clinic’s Center for Clinical Artificial Intelligence (CCAI), launched in March. The CCAI is using AI and machine learning in pathology, genetics, and cancer research, with the ultimate goal of improving patient outcomes, reported Becker’s Hospital Review.

“We’re not in it because AI is cool, but because we believe it can advance medical research and collaboration between medicine and industry—with a focus on the patient,” Aziz Nazha, MD, Clinical Hematology and Oncology Specialist and Director of the CCAI, stated in an article posted by the American Medical Association (AMA).

AI Predictions Lower Readmissions and Improve Outcomes

Cleveland Clinic’s CCAI reportedly has gathered data from 1.6 million patients, which it uses to predict length-of-stays and reduce inappropriate readmissions. “But a prediction itself is insufficient,” Nazha told the AMA. “If we can intervene, we can change the prognosis and make things better.”

The CCAI’s ultimate goal is to use predictive models to “develop a new generation of physician-data scientists and medical researchers.” Toward that end, Nazha notes how his team used AI to develop genomic biomarkers that identify whether a certain chemotherapy drug—azacitidine (aka, azacytidine and marketed as Vidaza)—will work for specific patients. This is a key goal of precision medicine

CCAI also created an AI prediction model that outperforms existing prognosis scoring systems for patients with Myelodysplastic syndromes (MDS), a form of cancer in bone marrow.

Partners HealthCare (founded by Brigham and Women’s Hospital and Massachusetts General Hospital) recently announced formation of the Center for Clinical Data Science to make AI and machine learning a standard tool for researchers and clinicians, according to a news release.

Meanwhile, at Johns Hopkins Hospital, AI applications track availability of beds and more. The Judy Reitz Capacity Command Center, built in collaboration with GE Healthcare Partners, is a 5,200 square feet center outfitted with AI apps and staff to transfer patients and help smooth coordination of services, according to a news release.

Forbes described the Reitz command center as a “cognitive hospital” and reports that it has essentially enabled Johns Hopkins to expand its capacity by 16 beds without undergoing bricks-and-mortar-style construction.

In short, medical laboratory leaders may want to interact with IT colleagues to ensure uninterrupted workflows as EHR functionality evolves. Furthermore, AI developments suggest opportunities for clinical laboratories to leverage patient data and assist in improving the diagnostic accuracy of providers in ways that improve patient care.

—Donna Marie Pocius

Related Information:

2019 Healthcare IT Demand Survey

Digital and AI are Top Priorities in 2019 as EHR Investments Continue to Dominate

Healthcare IT Spending Priorities Include Big Data Analytics, AI

Healthcare IT Demand Survey: Digital and AI are Top Priorities in 2019 as EHR Systems Continue to Dominate IT Spend

Cleveland Clinic Launches Clinical AI Center: 4 Things to Know

Cleveland Clinic Ready to Push AI Concepts to Clinical Practice

Cleveland Clinic Creating Center for AI in Healthcare

Partners HealthCare Embraces Democratization of AI to Accelerate Innovation in Medicine

Johns Hopkins Hospital Launches Capacity Command Center to Enhance Hospital Operations

The Hospital Will See You Now

Could Clinical Laboratories and Pathologists Have a New Use for DNA as a Data Storage Technology?

Researchers in Boston are working to develop DNA as a low-cost, effective way to store data; could lead to new molecular technology industries outside of healthcare

Even as new insights about the role of DNA in various human diseases and health conditions continue to tumble out of research labs, a potential new use for DNA is emerging. A research team in Boston is exploring how to use DNA as a low-cost, reliable way to store and retrieve data.

This has implications for the nation’s clinical laboratories and anatomic pathology groups, because they are gaining experience in sequencing DNA, then storing that data for analysis and use in clinical care settings. If a way to use DNA as a data storage methodology was to become reality, it can be expected that medical laboratories will have the skillsets, experience, and information technology infrastructure already in place to offer a DNA-based data storage service. This would be particularly true for patient data and healthcare data.

Finding a way to reduce the cost of data storage is a primary reason why scientists are looking at ways that DNA could be used as a data storage technology. These scientists and technology developers seek ways to alleviate the world’s over-crowded hard drives, cloud servers, and databases. They hope this can be done by developing technologies that store digital information in artificially-made versions of DNA molecules.

The research so far suggests DNA data storage could be used to store data more effectively than existing data storage solutions. If this proves true, DNA-based data storage technologies could play a key role in industries outside of healthcare.

If so, practical knowledge of DNA handling and storage would be critical to these companies’ success. In turn, this could present unique opportunities for medical laboratory professionals.

DNA Data Storage: Durable but Costly

Besides enormous capacity, DNA-based data storage technology offers durability and long shelf life in a compact footprint, compared to other data storage mediums.

“DNA has an information-storage density several orders of magnitude higher than any other known storage technology,” Victor Zhirnov, PhD, Chief Scientist and Director, Semiconductor Research Corporation, told Wired.

However, projected costs are quite high, due to the cost of writing the information into the DNA. However, Catalog Technologies Inc. of Boston thinks it has a solution.

Rather than producing billions of unique bits of DNA, as Microsoft did while developing its own DNA data storage solution, Catalog’s approach is to “cheaply generate large quantities of just a few different DNA molecules, none longer than 30 base pairs. Then [use] billions of enzymatic reactions to encode information into the recombination patterns of those prefab bits of DNA. Instead of mapping one bit to one base pair, bits are arranged in multidimensional matrices, and sets of molecules represent their locations in each matrix.”

The Boston-based company plans to launch an industrial-scale DNA data storage service using a machine that can daily write a terabyte of data by leveraging 500-trillion DNA molecules, according to Wired. Potential customers include the entertainment industry, federal government, and information technology developers.

Catalog is supported by $9 million from investors. However, it is not the only company working on this. Microsoft and other companies are reportedly working on DNA storage projects as well.

“It’s a new generation of information storage technology that’s got a million times the information density, compared to flash storage. You can shrink down entire data centers into shoeboxes of DNA,” Catalog’s CEO, Hyunjun Park, PhD (above center, between Chief Science Officer Devin Leake on left and Milena Lazova, scientist, on right), told the Boston Globe. (Photo copyright: Catalog.)

Microsoft, University of Washington’s Synthetic DNA Data Storage

Microsoft and researchers at the University of Washington (UW) made progress on their development of a DNA-based storage system for digital data, according to a news release. What makes their work unique, they say, is the large-scale storage of synthetic DNA (200 megabytes) along with the ability to the retrieve data as needed.

“Synthetic DNA is durable and can encode digital data with high density, making it an attractive medium for data storage. However, recovering stored data on a large-scale currently requires all the DNA in a pool to be sequenced, even if only a subset of the information needs to be extracted,” the researchers wrote in their paper published in Nature Biotechnology.

“Here, we encode and store 35 distinct files (over 200 megabytes of data ) in more than 13-million DNA oligonucleotides and show that we can recover each file individually and with no errors, using a random access approach,” the researchers explained.

“Our work reduces the effort, both in sequencing capacity and in processing, to completely recover information stored in DNA,” Sergey Yekhanin, PhD, Microsoft Senior Researcher, told Digital Trends.

Successful research by Catalog, Microsoft, and others may soon lead to the launch of marketable DNA data storage services. And medical laboratory professionals who already know the code—the life code that is—will likely find themselves more marketable as well!

—Donna Marie Pocius

Related Information:

The Rise of DNA Data Storage

The Next Big Thing in Data Storage is Actually Microscopic

Catalog Hauls in $9 Million to Make DNA-Based Data Storage Commercially Viable

UW and Microsoft Researchers Achieve Random Access in Large-Scale DNA Data Storage

Random Access in Large-Scale DNA Data Storage

Microsoft and University of Washington Show DNA Can Store Data in Practical Way

PwC Predicts Forces Shaping Healthcare in 2018; Some Could Impact Clinical Laboratories and Anatomic Pathology Groups

PwC’s list of 12 factors that will shape the healthcare landscape in 2018 calls attention to many new innovations Dark Daily has reported on that will impact how medical laboratories perform their tests

PwC’s Health Research Institute (HRI) issued its annual report, detailing the 12 factors expected to impact the healthcare industry the most in 2018. Dark Daily culled items from the list that will most likely impact clinical laboratories and anatomic pathology groups. They include:

How clinical laboratory leaders respond to these items could, in part, be determined by new technologies.

AI Is Everywhere, Including in the Medical Laboratory

Artificial intelligence is becoming highly popular in the healthcare industry. According to an article in Healthcare IT News, business executives who were polled want to “automate tasks such as routine paperwork (82%), scheduling (79%), timesheet entry (78%), and accounting (69%) with AI tools.” However, only about 20% of the executives surveyed have the technology in place to use AI effectively. The majority—about 75%—plan to invest in AI over the next three years—whether they are ready or not.

One such example of how AI could impact clinical laboratories was demonstrated by a recent advancement in microscope imaging. Researchers at the University of Waterloo (UW) developed a new spectral light fusion microscope that captures images in full color and is far less expensive than microscopes currently on the market.

“In medicine, we know that pathology is the gold standard in helping to analyze and diagnose patients, but that standard is difficult to come by in areas that can’t afford it,” Alexander Wong, PhD, one of the UW researchers, told CLP.

“The newly developed microscope has no lens and uses artificial intelligence and mathematical models of light to develop 3D images at a large scale. To get the same effect using current technologies—using a machine that costs several hundred thousand dollars—a technician is required to ‘stitch together’ multiple images from traditional microscopes,” CLP noted.

Healthcare Intermediaries Could Become Involved with Clinical Laboratory Data

Pricing is one of the biggest concerns for patients and government entities. This is a particular concern for the pharmaceutical sector. PwC’s report notes that “stock values for five of the largest intermediaries in the pharmacy supply chain have slumped in the last two years as demands for lower costs and better outcomes have intensified.”

Thus, according to PwC, pressure may come to bear on intermediaries such as Pharmacy Benefit Managers (PBMs) and wholesalers, to “prove value and success in creating efficiencies or risk losing their place in the supply chain.”

Similar pressures to lower costs and improve efficiency are at work in the clinical laboratory industry as well. Dark Daily reported on one such cost-cutting measure that involves shifting healthcare payments toward digital assets using blockchains. The technology digitally links trusted payers and providers with patient data, including medical laboratory test results. (See, “Blockchain Technology Could Impact How Clinical Laboratories and Pathology Groups Exchange Lab Test Data,” September 29, 2017.)

PwC 2018 Annual Report

PwC’s latest report predicts 12 forces that will continue to impact healthcare, including clinical laboratories and anatomic pathology groups, in 2018. Click on the image of the cover above to access an online version of the report. (Photo copyright: PwC/Issuu.)

The Opioid Crisis Remains at the Forefront

Healthcare will continue to feel the impact of the opioid crisis, according to the PwC report. Medical laboratories will continue to be involved in the diagnosis and treatment of opioid addition, which has garnered the full attention of the federal government and has become a multi-million-dollar industry.

Security Remains a Concern

Cybersecurity will continue to impact every facet of healthcare in 2018. Healthcare IT News reported, “While 95% of provider executives believe their organization is protected against cybersecurity attacks, only 36% have access management policies and just 34% have a cybersecurity audit process.”

Patients are aware of the risks and are often skeptical of health information technology (HIT), Dark Daily reported in June of last year. Clinical laboratories must work together with providers and healthcare organizations to audit their security measures. Recognizing the importance of the topic, the National Independent Laboratory Association (NILA) has named cybersecurity for laboratory information systems (LIS) a focus area.

Patient Experience a Priority

Although there have been significant improvements in the area of administrative tasks, there is still an enormous demand for a better patient experience, including in clinical laboratories. Healthcare providers want patients to make changes for the better that ultimately improve outcomes and the patient experience is one path toward that goal.

“Provider reimbursements will be based in part on patient engagement efforts such as promoting self-management and coaching patients between visits,” PwC noted in its report, a fact that Dark Daily has continually reported on for years. (See, “Pathologists and Clinical Lab Executives Take Note: Medicare Has New Goals and Deadlines for Transitioning from Fee-For-Service Healthcare Models to Value-Based Reimbursement,” April 1, 2015.)

Demands for Price Transparency Increase

As they follow healthcare reform guidelines to increase quality while lowering costs, state governments will continue to ramp up pressure on healthcare providers and third parties in the area of pricing. Rather than simply requiring organizations to report on pricing, states are moving towards legislating price controls, as Dark Daily reported in February.

Social Factors Affect Healthcare Access

The transition to value-based care makes the fact that patients’ socioeconomic statuses matter when it comes to their health. “The most important part of getting good results is not the knowledge of the doctors, not the treatment, not the drug. It’s the logistics, the social support, the ability to arrange babysitting,” David Berg, MD, co-founder of Redirect Health told PwC.

One such transition that is helping patients gain access to healthcare involves microhospitals and their adoption of telemedicine technologies, which Dark Daily reported on in March.

“Right now, they seem to be popping up in large urban and suburban metro areas,” Priya Bathija, Vice President, Value Initiative American Hospital Association, told NPR. “We really think they have the potential to help in vulnerable communities that have a lack of access.”

Data Collection Challenges Pharma

The 21st Century Cures Act, along with the potential exploitation of Big Data, will make it possible for organizations to gain faster, less expensive approvals from the US Food and Drug Administration (FDA). As Dark Daily noted in April, the FDA “released guidelines on how the agency intends to regulate—or not regulate—digital health, clinical-decision-support (CDS), and patient-decision-support (PDS) software applications.

“Physician decision-support software utilizes medical laboratory test data as a significant part of a full dataset used to guide caregivers,” Dark Daily noted. “Thus, if the FDA makes it easier for developers to get regulatory clearance for these types of products, that could positively impact medical labs’ ability to service their client physicians.”

Healthcare Delivery During and Following Natural Disasters

PwC predicts the long-term physical results, financial limitations, and supply chain disruptions following natural disasters will continue to affect healthcare in 2018. The devastation can prevent many people from receiving adequate, timely healthcare.

However, new laboratory-on-a-chip (LOC) and other “lab-on-a-…” testing technologies, coupled with medical drone deliver services, can bring much need healthcare to remote, unreachable areas that lack electricity and other services. (See Dark Daily, “Lab-on-a-Fiber Technology Continues to Highlight Nano-Scale Clinical Laboratory Diagnostic Testing in Point-of-Care Environments,” April 2, 2018, and, “Johns Hopkins’ Test Drone Travels 161 Miles to Set Record for Delivery Distance of Clinical Laboratory Specimens,” November 15, 2017.)

PwC’s report is an important reminder of from where the clinical laboratory/anatomic pathology industry has come, and to where it is headed. Sharp industry leaders will pay attention to the predictions contained therein.

—Dava Stewart

Related Information:

Top Health Industry Issue of 2018

PwC Health Research Institute Top Health Industry Issues of 2018 Report: Issuu Slide Presentation

12 Defining Healthcare Issues of 2018

Is Laboratory Medicine Ready for Artificial Intelligence?

Artificial Intelligence Imaging Research Facilitates Disease Diagnosis

Blockchain Technology Could Impact How Clinical Laboratories and Pathology Groups Exchange Lab Test Data

Skepticism, Distrust of HIT by Healthcare Consumers Undermines Physician Adoption of Medical Reporting Technologies, But Is Opportunity for Pathology Groups, Clinical Laboratories

Pathologists and Clinical Lab Executives Take Note: Medicare Has New Goals and Deadlines for Transitioning from Fee-For-Service Healthcare Models to Value-Based Reimbursement

Researchers Point to Cost of Services, including Medical Laboratories, for Healthcare Spending Gap Between the US and Other Developed Countries

Telemedicine and Microhospitals Could Make Up for Reducing Numbers of Primary Care Physicians in US Urban and Metro Suburban Areas

New FDA Regulations of Clinical Decision-Support/Digital Health Applications and Medical Software Has Consequences for Medical Laboratories

Lab-on-a-Fiber Technology Continues to Highlight Nano-Scale Clinical Laboratory Diagnostic Testing in Point-of-Care Environments

Johns Hopkins’ Test Drone Travels 161 Miles to Set Record for Delivery Distance of Clinical Laboratory Specimens

;