Healthcare industry watchdog Group Leapfrog says that if CMS suppresses the data “all of us will be in the dark on which hospitals put us most at risk”
For some time, hospitals and clinical laboratories have struggled with transparency regulation when it comes to patient outcomes, test prices, and costs. So, it is perplexing that while that Centers for Medicare and Medicaid Services (CMS) pushes for more transparency in the cost of hospital care and quality, the federal agency also sought to limit public knowledge of 10 types of medical and surgical harm that occurred in hospitals during the COVID-19 pandemic.
And even though the CMS announced in its August 1 final rule (CMS-1771-F) that it was “pausing” its plans to suppress data relating to 10 measures that make up the Patient Safety and Adverse Events Composite (PSI 90), a part of the Hospital-Acquired Condition (HAC) Reduction Program, it is valuable for hospital and medical laboratory leaders to understand what the federal agency was seeking to accomplish.
According to USA Today, medical complications at hospitals such as pressure ulcers and falls leading to fractures would be suppressed in reports starting next year. Additionally, CMS “also would halt a program to dock the pay of the worst performers on a list of safety measures, pausing a years-long effort that links hospitals’ skill in preventing such complications to reimbursement,” Kaiser Health News reported.
The proposed rule’s executive summary reads in part, “Due to the impact of the COVID-19 PHE on measure data used in our value-based purchasing (VBP) programs, we are proposing to suppress several measures in the Hospital VBP Program and HAC Reduction Program … If finalized as proposed, for the FY 2023 program year, hospitals participating in the HAC Reduction Program will not be given a measure score, a Total HAC score, nor will hospitals receive a payment penalty.”
In a fact sheet, CMS noted that its intent in proposing the rule was neither to reward nor penalize providers at a time when they were dealing with the SARS-CoV-2 outbreak, new safety protocols for staff and patients, and an unprecedented rise in inpatient cases.
“We want the public to have complete trust in the data and will only be providing data we have determined has a high confidence of credibility and accuracy,” said CMS Chief Medical Officer Lee Fleisher, MD (above), Director of the CMS Center for Clinical Standards and Quality in a statement, Axios reported. Clinical laboratory leaders would find it more difficult to compare the performance of their hospitals against peer hospitals, should this proposed rule take effect as written. (Photo copyright: Lee Fleisher.)
Groups Opposed to the CMS Proposal
Like healthcare costs, quality data need to be accessible to the public, according to a health insurance industry representative. “Cost data, in the absence of quality data, are at best meaningless, and at worst, harmful. We see this limitation on collection and publication of data about these very serious safety issues as a step backward,” Robert Andrews, JD, CEO, Health Transformation Alliance, told Fortune.
The Leapfrog Group, a Washington, DC-based non-profit watchdog organization focused on healthcare quality and safety, urged CMS to reverse the proposal. The organization said on its website that it had collected 270 signatures on letters to CMS.
“Dangerous complications, such as sepsis, kidney harm, deep bedsores, and lung collapse, are largely preventable yet kill 25,000 people a year and harm 94,000,” wrote the Leapfrog Group in a statement. “Data on these complications is not available to the public from any other source. If CMS suppresses this data, all of us will be in the dark on which hospitals put us most at risk.”
Leah Binder, Leapfrog President/CEO, told MedPage Today she is concerned the suppression of public reporting of safety data may continue “indefinitely” because CMS does not want “to make hospitals unhappy with them.”
AHA Voices Support
Meanwhile, the American Hospital Association noted that the CMS “has made this proposal to forgo calculating certain hospital bonuses and penalties due to the impact of the pandemic,” Healthcare Dive reported.
“We agree with CMS that it would be unfair to base hospital incentives and penalties on data that have been skewed by the unprecedented impacts of the pandemic,” said Akin Demehin, AHA Senior Director, Quality and Safety Policy, in a statement to Healthcare Dive.
Though CMS’ plans to limit public knowledge of medical and surgical complications have been put on hold, medical laboratory leaders will want to stay abreast of CMS’ next steps with this final rule. Suppression of hospital harm during a period of increased demand for hospital transparency could trigger a backlash with healthcare consumers.
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.
Cerner and Epic are the industry’s revenue leaders, though smaller vendors remain popular with physician groups
Sales of electronic health record (EHR) systems and related hardware and services reached $31.5 billion in 2018. And those sales will increase, according to a 2019 market analysis from Kalorama Information. This is important information for clinical laboratories and anatomic pathology groups that must interface with the EHRs of their physician clients to enable electronic transmission of lab orders and test results between doctor and lab.
Kalorama’s ranking includes familiar big EHR manufacturer names—Cerner (NASDAQ:CERN) and Epic—and includes a new name, Change Healthcare, which was born out of Change Healthcare Holding’s merger with McKesson. However, smaller EHR vendors remain popular with many independent physicians.
“We estimate that 40% of the market is not in the top 15 [in total revenue rankings],” said Bruce Carlson, Kalorama’s publisher, in an exclusive interview with Dark Daily. “There’s a lot of room. There are small vendors out there—Amazing Charts, e-MDs, Greenway, NextGen, Athena Health—that show up on a lot of physician surveys.”
“The EHR is really important,” noted Bruce Carlson (above), Publisher at Kalorama. “Since there are a variety of systems—sometimes different from the LIS [laboratory information management system]—you want to make sure you know the vendors and the space.” Carlson says opportunities remain for new entrants in the 700-plus competitor space, which is expected to see continued mergers and acquisitions that will affect clinical laboratories and their client physicians. (Photo copyright: Twitter.)
Interoperability a Key Challenge, as Most Medical
Laboratories Know
Interoperability—or the lack thereof—remains one of the
industry’s biggest challenges. For pathologists, that means seamless electronic
communication between medical laboratories and provider hospitals can be
elusive and can create a backlash against EHR vendors.
Kalorama notes a joint investigation by Fortune and Kaiser Health News (KHN), titled, “Death by a Thousand Clicks: Where Electronic Health Records Went Wrong.” The report details the growing number of medical errors tied to EHRs. One instance involved a California lawyer with herpes encephalitis who allegedly suffered irreversible brain damage due to a treatment delay caused by the failure of a critical lab test order to reach the hospital laboratory. The order was typed into the EHR, but the hospital’s software did not fully interface with the clinical laboratory’s software, so the lab did not receive the order.
“Many software vendors and LIS systems were in use prior to
the real launching of EHRs—the [federal government] stimulus programs,” Carlson
told Dark Daily. “There are a lot of legacy systems that aren’t
compatible and don’t feed right into the EHR. It’s a work in progress.”
Though true interoperability isn’t on the immediate horizon, Carlson expects its arrival within the next five years as the U.S. Department of Health and Human Services ramps up pressure on vendors.
“I think it is going to be a simple matter eventually,” he
said. “There’s going to be much more pressure from the federal government on
this. They want patients to have access to their medical records. They want one
record. That’s not going to happen without interoperability.”
Other common criticisms of EHRs include:
Wasted provider time: a recent study published in JAMA Internal Medicine notes providers now spend more time in indirect patient care than interacting with patients.
Physician burnout: EHRs have been shown to increase physician stress and burnout.
Not worth the trouble: The debate continues over whether EHRs are improving the quality of care.
Negative patient outcomes: Fortune’s investigation outlines patient safety risks tied to software glitches, user errors, or other flaws.
There’s No Going Back
Regardless of the challenges—and potential dangers—it appears EHRs are here to stay. “Any vendor resistance of a spirited nature is gone. Everyone is part of the CommonWell Health Alliance now,” noted Carlson.
Clinical laboratories and pathology groups should expect
hospitals and health networks to continue moving forward with expansion of
their EHRs and LIS integrations.
“Despite the intensity of attacks on EHRs, very few health systems are going back to paper,” Carlson said in a news release. “Hospital EHR systems are largely in place, and upgrades, consulting, and vendor switches will fuel the market.”
Thus, it behooves clinical laboratory managers and
stakeholders to anticipate increased demand for interfaces to hospital-based
healthcare providers, and even off-site medical settings, such as urgent care
centers and retail health clinics.
OIG suggests better use of analytics by CMS could prevent gaming of the system by providers; clinical laboratories can help through test utilization management technology
In 2015, CMS implemented the Hospital-Acquired Condition Reduction Program (HACRP) as part of the Patient Protection and Affordable Care Act (ACA). The HACRP program incentivizes hospitals to lower their HAI rates by adjusting reimbursements according to the inpatient quality reporting (hospital IQR) data provided by the healthcare providers. Hospital IQR data is the basis on which CMS validates a hospital’s HAI rate (among other things CMS is tracking) to determine the hospital’s reimbursement rate for that year.
CMS, in 2016, met its regulatory requirement to validate inpatient quality reporting data;
It reviewed data of 400 randomly selected hospitals as well as 49 hospitals targeted for failing to report half their HAIs, or for low scores in the prior year’s validation process;
However, OIG also reported that CMS did not include hospitals that displayed abnormal data patterns in its targeted sample. Targeting those hospitals, according to the OIG, could identify inaccurate reporting.
CMS staff had identified 96 hospitals with aberrant data patterns, but did not target them for validation—even though the agency can select up to 200 targeted hospitals for review, Becker’s Hospital Review pointed out.
Dollars More Important than Deaths
According to the OIG report, Medicare excluded in its investigation dozens of hospitals with suspected HAI reporting. This is odd since the CMS and the Centers for Disease Control (CDC) apparently are aware that some healthcare providers have manipulated data to improve their quality measure scores and thus increase their reimbursement rates.
“Collecting and analyzing quality data is increasingly central to Medicare programs that link payments to quality and value. Therefore, it is important for CMS to ensure that hospitals are not gaming [manipulating data to improve scores] their reporting of quality data,” the OIG report noted.
“There are greater requirements for what a company says about a washing machine’s performance than there is for a hospital on quality of care. And this needs to change,” stated Peter Pronovost, MD, PhD, in the Kaiser Health News article. “We require auditing of financial data, but we don’t require auditing of healthcare quality data, and that implies that dollars are more important than deaths.” Pronovost is Senior Vice President for Patient Safety and Quality at Johns Hopkins University School of Medicine.
Peter Pronovost, MD, PhD (above) testifying on preventable deaths before the Senate Subcommittee on Primary Health and Aging in 2014. He is Senior Vice President for Patient Safety and Quality at Johns Hopkins University School of Medicine in Baltimore. Pronovost told Kaiser Health News that there are no uniform standards for reviewing data that hospitals report to Medicare. (Photo copyright: US Senate Committee on Health, Education, Labor and Pensions.)
Medicare Missed Hospitals with Suspected HAI Data
CMS should have done an in-depth review of many hospitals that submitted “aberrant data patterns” in 2013 and 2014, the OIG stated in its report. According to a Kaiser HealthNews article, such patterns could include:
A rapid change in results;
Improbably low infection rates; and
Assertions that infections nearly always struck before patients arrived at the hospital.
“There’s a certain amount of blind faith that hospitals are going to tell the truth. It’s a bit much to expect that if they had a bad record they are going to fess up to it,” noted Lisa McGiffert, Director of the Safe Patient Project at Consumers Union, in the Kaiser Health News article.
CMS Needs Better Data Analytics
So, what does the OIG advise CMS to do? The agency called for “better use of analytics to ensure the integrity of hospital-reported quality data.” Specifically, OIG suggested CMS:
Identify hospitals with abnormal percentages of patients who had infections on admission;
Apply risk scores to identify hospitals with high propensity to manipulate reporting;
Use experiences to create and improve models that identify hospitals most likely to game their reporting.
CMS’ Administrator Seema Verma reportedly responded, “We will continue to evaluate the use of better analytics as feasible, based on Medicare’s operational capabilities.”
Medical Laboratory Diagnostic Testing Part of Gaming the System
A 2015 CMS/CDC joint statement noted “three ways that hospitals may be deviating from CDC’s definitions for reportable HAIs,” and two involve diagnostic test ordering. According to the OIG report, they include:
Overculturing: Diagnostic tests may be overutilized by providers in absence of clinical symptoms. Hospitals may use positive results to game their data by claiming infections that appeared days later were present on admission and thus not reportable.
Underculturing: Hospitals underculture when they do not order diagnostic tests in the presence of clinical symptoms. By not ordering the test, the hospital does not learn whether the patient truly has an infection and, therefore, the hospital does not have to report it.
Adjudication: Hospital administrative staff may inappropriately overrule those who report infections. HAIs are, therefore, not shared.
Clinical Laboratories Can Help
One in 25 people each day receives an HAI, CDC estimates. The OIG findings should be a reminder to medical laboratories and pathology groups that quality measures and patient outcomes are often transparent to media, patients, and the public.
One way medical laboratories in hospitals and health systems can help is by investing in utilization management technology and protocols that ensure appropriate lab test utilization. Informing doctors on the availability of appropriate diagnostic tests based on patients’ existing conditions, unique physiologies, or medical histories, could help prevent hospitals from inadvertently or deliberately game the system.
Clearly, transparency in healthcare is increasing. That means there will be more news stories revealing federal agencies’ failures to respond to healthcare data in ways that could have protected patients and the public. Clinical laboratories don’t want to be included in negative reporting.
An earlier Johns Hopkins study looked at diagnostic errors and determined that such errors were the leading cause of malpractice payouts. Can clinical laboratories help?
This finding has many implications for pathologists and clinical laboratory managers. Often, medical errors are associated with the failure of physicians to order correct medical laboratory tests at critical junctures. Alternatively, a medical error can result if the physician fails to take appropriate action after getting an accurate lab test result. Thus, any effort within the health system to reduce medical errors will probably bring pathologists and medical laboratory scientists into closer consultation with clinicians.
What the researchers at Johns Hopkins also learned during their study is that medical error is not reported as a cause of death on death certificates. Further, the Centers for Disease Control and Prevention (CDC) has no “medical error” category in its annual report on deaths and mortality, The New York Times (NYT) reported. (more…)
