Pathologists and clinical laboratory scientists may find one hospital’s use of a machine-learning platform to help improve utilization of lab tests both an opportunity and a threat
Variation in how individual physicians order, interpret, and act upon clinical laboratory test results is regularly shown by studies in peer-reviewed medical journals to be one reason why some patients get great outcomes and other patients get less-than-desirable outcomes. That is why many healthcare providers are initiating efforts to improve how physicians utilize clinical laboratory tests and other diagnostic procedures.
This effort came about after clinical and administrative leadership at Flagler Hospital realized that only about one-third of its physicians regularly followed certain medical decision-making guidelines or clinical order sets. Armed with these insights, staff members decided to find a solution that reduced or removed variability from their healthcare delivery.
Reducing Variability Improves Care, Lowers Cost
Variability in physician care has been linked to increased healthcare costs and lower quality outcomes, as studies published in JAMA and JAMA Internal Medicine indicate. Such results do not bode well for healthcare providers in today’s value-based reimbursement system, which rewards increased performance and lowered costs.
Clinical order sets are designed to be used as part of clinical decision support systems (CDSS) installed by hospitals for physicians to standardize care and support sound clinical decision making and patient safety.
However, when doctors don’t adhere to those pre-defined standards, the results can be disadvantageous, ranging from unnecessary services and tests being performed to preventable complications for patients, which may increase treatment costs.
“Over the past few decades we’ve come to realize clinical variation plays an important part in the overuse of medical care and the waste that occurs in healthcare, making it more expensive than it should be,” Michael Sanders, MD (above) Flagler’s Chief Medical Information Officer, told Modern Healthcare. “Every time we’re adding something that adds cost, we have to make sure that we’re adding value.” (Photo copyright: Modern Healthcare.)
Flagler’s AI project involved uploading clinical,
demographic, billing, and surgical information to the AyasdiAI platform, which then
employed machine learning to analyze the data and identify trends. Flagler’s
physicians are now provided with a fuller picture of their patients’ conditions,
which helps identify patients at highest risk, ensuring timely interventions that
produce positive outcomes and lower costs.
How Symphony AyasdiAI Works
The AyasdiAI application utilizes a category of mathematics called topological data analysis (TDA) to cluster similar patients together and locate parallels between those groups. “We then have the AI tools generate a carepath from this group, showing all events which should occur in the emergency department, at admission, and throughout the hospital stay,” Sanders told Healthcare IT News. “These events include all medications, diagnostic tests, vital signs, IVs, procedures and meals, and the ideal timing for the occurrence of each so as to replicate the results of this group.”
Caregivers then examine the data to determine the optimal
plan of care for each patient. Cost savings are figured into the overall
equation when choosing a treatment plan.
Flagler first used the AI program to examine trends among their pneumonia patients. They determined that nebulizer treatments should be started as soon as possible with pneumonia patients who also have chronic obstructive pulmonary disease (COPD).
“Once we have the data loaded, we use [an] unsupervised
learning AI algorithm to generate treatment groups,” Sanders told Healthcare
IT News. “In the case of our pneumonia patient data, Ayasdi produced nine
treatments groups. Each group was treated similarly, and statistics were given
to us to understand that group and how it differed from the other groups.”
Armed with this information, the hospital achieved an 80% greater physician adherence to order sets for pneumonia patients. This resulted in a savings of $1,350 per patient and reduced the readmission rates for pneumonia patients from 2.9% to 0.4%, reported Modern Healthcare.
The development of a machine-learning platform designed to
reduce variation in care (by helping physicians become more consistent at
following accepted clinical care guidelines) can be considered a warning shot
across the bow of the pathology profession.
This is a system that has the potential to become interposed
between the pathologist in the medical laboratory and the physicians who refer
specimens to the lab. Were that to happen, the deep experience and knowledge
that have long made pathologists the “doctor’s doctor” will be bypassed.
Physicians will stop making that first call to their pathologists, clinical
chemists, and laboratory scientists to discuss a patient’s condition and
consult on which test to order, how to interpret the results, and get guidance
on selecting therapies and monitoring the patient’s progress.
Instead, a “smart software solution” will be inserted into
the clinical workflow of physicians. This solution will automatically guide the
physician to follow the established care protocol. In turn, this will give the
medical laboratory the simple role of accepting a lab test order, performing
the analysis, and reporting the results.
If this were true, then it could be argued that a laboratory
test is a commodity and hospitals, physicians, and payers would argue that they
should buy these commodity lab tests at the cheapest price.
Centers for Medicare and Medicaid Innovation is considering adding clinical laboratory services to bundled payments in its proposed Oncology Care First model
CMMI, an organization within the Centers for Medicare and Medicaid Services (CMS), is charged with developing and testing new healthcare delivery and payment models as alternatives to the traditional fee-for-service (FFS) model. On November 1, 2019, CMMI released an informal Request for Information (RFI) seeking comments for the proposed Oncology Care First (OCF) model, which would be the successor to the Oncology Care Model (OCM) launched in 2016.
“The inefficiency and variation in oncology care in the
United States is well documented, with avoidable hospitalizations and emergency
department visits occurring frequently, high service utilization at the end of
life, and use of high-cost drugs and biologicals when lower-cost, clinically
equivalent options exist,” the CMMI RFI states.
With the proposed new model, “the Innovation Center aims to build on the lessons learned to date in OCM and incorporate feedback from stakeholders,” the RFI notes.
How the Oncology Care First Model Works
The OCF program, which is voluntary, will be open to
physician groups and hospital outpatient departments. CMMI anticipates that
testing of the model will run from January 2021 through December 2025.
It will offer two payment mechanisms for providers that
choose to participate:
A Monthly Population Payment (MPP) would apply
to a provider’s Medicare beneficiaries with “cancer or a cancer-related
diagnosis,” the RFI states. It would cover Evaluation and Management (EM)
services as well as drug administration services and a set of “Enhanced
Services,” including 24/7 access to medical records.
Of particular interest to medical laboratories, the RFI also
states that “we are considering the inclusion of additional services in the monthly
population payment, such as imaging or medical laboratory services, and seek
feedback on adding these or other services.”
In addition, providers could receive a
Performance-Based Payment (PBP) if they reduce expenditures for patients
receiving chemotherapy below a “target amount” determined by past Medicare
payments. If providers don’t meet the threshold, they could be required to
repay CMS.
Practices that wish to participate in the OCF model must go through an application process. It is also open to participation by private payers. CMS reports that 175 practices and 10 payers are currently participating in the 2016 Oncology Care Model (OCM).
“We want better quality care for patients,” explained Lara Strawbridge, MPH (above), Director of the CMMI Division of Ambulatory Payment Models, during a US Department of Health and Human Services public listening session on Nov. 8. “We hope that at the same time, costs are maintained or reduced.” The new OCF payment model will feature a Monthly Population Payment mechanism that could include reimbursements for medical laboratory services, which has some medical laboratory organizations concerned. (Photo copyright: Center for Medicare and Medicaid Innovation.)
Medical Lab Leaders Concerned about the CMMI OCF Model
One group raising concerns about the inclusion of medical laboratory service reimbursements in the Monthly Population Payment scheme is the Personalized Medicine Coalition. “Laboratory services are crucial to the diagnosis and management of many cancers and are an essential component of personalized medicine,” wrote Cynthia A. Bens, the organization’s senior VP for public policy, in an open letter to CMMI Acting Director Amy Bassano. “We are concerned that adding laboratory service fees to the MPP may cause providers to view them as expenses that are part of the total cost of delivering care, rather than an integral part of the solution to attain high-value care,” Bens wrote.
She advised CMMI to “seek further input from the laboratory
and provider communities on how best to contain costs within the OCF model,
while ensuring the proper deployment of diagnostics and other laboratory
services.”
Members of the coalition include biopharma companies, diagnostic companies, patient advocacy groups, and clinical laboratory testing services. Lab testing heavyweights Quest Diagnostics (NYSE:DGX) and Laboratory Corporation of America (NYSE:LH) are both members.
CMS ‘Doubles Down’ on OCM
The proposal received criticism from other quarters as well. “While private- and public-sector payers would be well served to adopt and support a VBP [value-based payment] program for cancer care, we need to better understand some of the shortcomings of the original OCM design and adopt lessons learned from other successful VBP models to ensure uptake by providers and ultimately better oncology care for patients,” wrote members of the Oncology Care Model Work Group in a Health Affairs blog post. They added that with the new model, “CMS seems to double down on the same design as the OCM.”
Separately, CMMI has proposed a controversial Radiation
Oncology (RO) alternative payment model (APM) that would be mandatory for
practices in randomly-selected metro areas. The agency estimates that it would
apply to approximately 40% of the radiotherapy practices in the US.
These recent actions should serve to remind pathologists and
clinical laboratories that CMS continues to move away from fee-for-service and
toward value-based care payment models, and that it is critical to plan for
changing reimbursement strategies.
Researchers find a savings of more than one million dollars and prevention of hundreds, if not thousands, of adverse drug events could have been had with machine learning system
Support for artificial intelligence (AI) and machine learning (ML) in healthcare has been mixed among anatomic pathologists and clinical laboratory leaders. Nevertheless, there’s increasing evidence that diagnostic systems based on AI and ML can be as accurate or more accurate at detecting disease than systems without them.
Dark Daily has covered the development of artificial intelligence and machine learning systems and their ability to accurately detect disease in many e-briefings over the years. Now, a recent study conducted at Brigham and Women’s Hospital (BWH) and Massachusetts General Hospital (MGH) suggests machine learning can be more accurate than existing clinical decision support (CDS) systems at detecting prescription medication errors as well.
The study was partially retrospective in that the
researchers compiled past alerts generated by the CDS systems at BWH and MGH
between 2009-2011 and added them to alerts generated during the active part of
the study, which took place from January 1, 2012 to December 31, 2013, for a
total of five years’ worth of CDS alerts.
They then sent the same patient-encounter data that generated those CDS alerts to a machine learning platform called MedAware, an AI-enabled software system developed in Ra’anana, Israel.
MedAware was created for the “identification and prevention
of prescription errors and adverse drug effects,” notes the study, which goes
on to state, “This system identifies medication issues based on machine
learning using a set of algorithms with different complexity levels, ranging
from statistical analysis to deep learning with neural networks. Different
algorithms are used for different types of medication errors. The data elements
used by the algorithms include demographics, encounters, lab test results,
vital signs, medications, diagnosis, and procedures.”
The researchers then compared the alerts produced by
MedAware to the existing CDS alerts from that 5-year period. The results were
astonishing.
According to the study:
“68.2% of the alerts generated were unique to
the MedAware system and not generated by the institutions’ CDS alerting system.
“Clinical outlier alerts were the type least
likely to be generated by the institutions’ CDS—99.2% of these alerts were
unique to the MedAware system.
“The largest overlap was with dosage alerts,
with only 10.6% unique to the MedAware system.
“68% of the time-dependent alerts were unique to
the MedAware system.”
Perhaps even more important was the results of the cost
analysis, which found:
“The average cost of an adverse event
potentially prevented by an alert was $60.67 (range: $5.95–$115.40).
“The average adverse event cost per type of
alert varied from $14.58 (range: $2.99–$26.18) for dosage outliers to $19.14
(range: $1.86–$36.41) for clinical outliers and $66.47 (range: $6.47–$126.47)
for time-dependent alerts.”
The researchers concluded that, “Potential savings of $60.67 per alert was mainly derived from the prevention of ADEs [adverse drug events]. The prevention of ADEs could result in savings of $60.63 per alert, representing 99.93% of the total potential savings. Potential savings related to averted calls between pharmacists and clinicians could save an average of $0.047 per alert, representing 0.08% of the total potential savings.
“Extrapolating the results of the analysis to the 747,985
BWH and MGH patients who had at least one outpatient encounter during the
two-year study period from 2012 to 2013, the alerts that would have been fired
over five years of their clinical care by the machine learning medication
errors identification system could have resulted in potential savings of
$1,294,457.”
Savings of more than one million dollars plus the prevention
of potential patient harm or deaths caused by thousands of adverse drug events
is a strong argument for machine learning platforms in diagnostics and
prescription drug monitoring.
Researchers Say Current Clinical Decision Support Systems
are Limited
Machine learning is not the same as artificial intelligence. ML is a “discipline of AI” which aims for “enhancing accuracy,” while AI’s objective is “increasing probability of success,” explained Tech Differences.
Healthcare needs the help. Prescription medication errors cause patient harm or deaths that cost more than $20 billion annually, states a Joint Commission news release.
CDS alerting systems are widely used to improve patient
safety and quality of care. However, the BWH-MGH researchers say the current
CDS systems “have a variety of limitations.” According to the study:
“One limitation is that current CDS systems are rule-based and can thus identify only the medication errors that have been previously identified and programmed into their alerting logic.
“Further, most have high alerting rates with many false positives, resulting in alert fatigue.”
Commenting on the value of adding machine learning
medication alerts software to existing CDS hospital systems, the BWH-MGH
researchers wrote, “This kind of approach can complement traditional rule-based
decision support, because it is likely to find additional errors that would not
be identified by usual rule-based approaches.”
However, they concluded, “The true value of such alerts is
highly contingent on whether and how clinicians respond to such alerts and
their potential to prevent actual patient harm.”
Future research based on real-time data is needed before machine
learning systems will be ready for use in clinical settings, HealthITAnalytics
noted.
However, medical laboratory leaders and pathologists will
want to keep an eye on developments in machine learning and artificial
intelligence that help physicians reduce medication errors and adverse drug
events. Implementation of AI-ML systems in healthcare will certainly affect
clinical laboratory workflows.
By negotiating directly with healthcare providers, employers cut health insurers out of the loop, at least for certain specified healthcare conditions and surgeries
It’s a new trend in how employers provide healthcare benefits for their employees. In order to save money, a growing number of employers are going to low-cost hospitals, physicians, and other providers to contract directly for their services. This may be the opening that allows some clinical laboratories to approach larger employers in their region and negotiate pricing and contract terms without the need to involve a health insurer.
What’s motivating more employers to reach out and contract directly with low-cost healthcare providers is the realization that their health insurance plan typically pays much more than Medicare to hospitals, physicians, clinical laboratories, and other ancillary providers. This fact is supported by a study conducted by the Rand Corporation that found “large employers generally lack useful information about the prices they are paying for healthcare services,” and that of the 1,600 hospitals in 25 states that Rand surveyed, “employer-sponsored health plans paid hospitals an average of 241% of what Medicare would have paid for the same inpatient and outpatient services in 2017,” which is up from 236% of Medicare in 2015, Modern Healthcare reported.
Thus, to better control the skyrocketing cost of healthcare,
and the health benefits plan they offer their employees, employers are
increasingly turning to self-coverage and implementing company benefits plans
that reward employees for price shopping and for selecting the lowest costs
healthcare services.
This trend is another reason why clinical laboratory leaders should be tracking changes in federal price transparency requirements, along with the increased consumer interest in accessing healthcare prices in advance of service.
Employers Negotiate Directly for Healthcare Services
Innovative employer plans to decrease healthcare costs
include:
Contracting directly with medical providers,
Opening primary care clinics within their
corporate facilities,
Referring employees to contracted providers for certain
procedures, and
Creating bundled-payment deals with select
providers.
Modern Healthcare reports that both public and
private employers in five states (Colorado, Connecticut, Michigan, Montana,
Texas, and Wisconsin) are “considering or launching group purchasing
initiatives with narrow- or tiered-network plans, onsite primary-care clinics,
and contracts with advanced primary-care providers,” as well as “direct-contracting
with providers, such as referring employees to designated centers of excellence
for some procedures and conditions under bundled-payment deals with warrantied
results.”
Cheryl DeMars, CEO of The Alliance, a Wisconsin healthcare purchasing cooperative, says there is a movement afoot. “I’m seeing a level of boldness on the part of our members that I haven’t seen before in my 27 years here,” she told Modern Healthcare.
“Almost 100 million employees covered through self-insured plans not only represents a staggeringly large market for healthcare cost containment, it is an extraordinary opportunity for America to meaningfully reduce our national healthcare bill,” Kirk Fallbacher (above), President and CEO of Advanced Medical Pricing Solutions (AMPS) told Healthcare Finance News. (Photo copyright: NextGenRBP.com)
Self-insured Employers can Reduce the Nation’s Healthcare
Bill, says KFF
A 2018 US Census Bureau report states that more than 181 million people in the US were enrolled in employer-sponsored health plans in 2017, and that the estimated average premium for employer-sponsored family coverage increased at an annual rate of 4.5% from 2008 to 2019.
That increase was approximately twice the rate of overall
inflation and growth in average hourly earnings during the same time period, according
to the report, which also states that the surge in premiums was driven by price
increases for medical services and that use of most healthcare services among
employees has actually been declining.
For US employers, “the steep increase in their healthcare
cost crowds out financial resources that could be used for employee wage
increases, capital investments, and other spending priorities, such as
retirement plans,” the report notes.
However, an estimated 94 million of the 156 million workers in the US—approximately 61%—are currently covered under a self-insured medical plan through their employer, the KFF Employer Health Benefits 2019 Annual Survey states.
Healthcare.gov defines the self-insured health insurance plan as a “type of plan usually present in larger companies where the employer itself collects premiums from enrollees and takes on the responsibility of paying employees’ and dependents’ medical claims. These employers can contract for insurance services such as enrollment, claims processing, and provider networks with a third-party administrator, or they can be self-administered.”
“It doesn’t signal the end of the insurance industry,” he
said. “On the cost side of the equation, the PPO approach is beginning to come
to an end. The costs are outstripping inflation and wages.”
Moving to self-insurance is another part of the current trend for price transparency in the healthcare industry and may offer opportunities for clinical laboratories to increase profits. Clinical laboratories and anatomic pathology groups might want to contact the Human Resources Departments of local major employers to educate them on the costs and quality value of their labs. Such a proactive and innovative move could encourage employers to include those labs in the provider networks of their self-insured health benefit plans.
Anatomic pathology laboratories are struggling to remain profitable as they grapple with increasing workloads amid sweeping reimbursement cuts. The traditional pathology business model hangs in the balance, with some labs operating at such a thin margin that it may only take one severe adverse event to put them out of business.
Digital pathology is one wide-ranging solution that’s tackling these systemic challenges. Driven by recent technological and regulatory approvals, leading academic and commercial laboratories are increasingly going digital to overcome significant gaps in efficiency and diagnostic accuracy. These same labs are implementing computational applications that leverage artificial intelligence (AI) to expand on the productivity, quality, and confidence gains they’ve already realized.
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Anatomic pathology laboratories are struggling to remain profitable as they grapple with increasing workloads amid sweeping reimbursement cuts. The traditional pathology business model hangs in the balance, with some labs operating at such a thin margin that it may only take one severe adverse event to put them out of business.
