Samara Rosenfeld

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New data from the Kaiser Family Foundation and Epic Health Research Network show while there was a rise in the number of COVID-19 cases in fall 2020, there was a decline in non-COVID-19 hospital admissions during that time period.

“These new data provide additional information to help assess the economic impact of the COVID-19 pandemic on hospitals and insurers and also adds to our understanding of the extend to which people are continuing to delay or forgo care nearly one year into the pandemic,” Tyler Heist and colleagues wrote.

The investigators analyzed trends in total hospital admissions and then separately analyzed non-COVID-19 admissions overall and by patient region, age, and sex. They calculated actual admissions as a share of total predicted admissions in 2020 based on trends from past years.

The analysis was based on electronic health record data from Epic Health Research Network and included all inpatient hospital admission volume from Dec. 31, 2017 to Dec. 5, 2020 and involved patients who were discharge or died as of Jan. 13, 2021. The data were aggregated and pooled weekly from 34 healthcare organizations across the U.S, of which, the states represented 73% of COVID-19 cases as of Jan. 21, 2021 and 76.7% of the U.S. population.

Heist and the team found total hospital admissions dropped to 69.2% of predicted admissions during the lowest point in the year (week ending April 4) before rising and staying at or above 90% since June 2020. Since the week ending Dec. 5, 2020, total admissions were at 94.2% of what was predicted. There was a decrease in hospital admissions from March 8 to Dec. 5, 2020, which represented 8.5% of the total expected admissions for all of 2020.

As COVID-19 cases rose in November 2020, non-related hospitalizations began declining and were about 80% of predicted hospitalizations by the end of the month, suggesting people may be delaying or completely skipping care due to the pandemic. The more recent decline in non-COVID-19-related admissions has been steepest in the Midwest and West since December. Admissions in those regions were at roughly 76% of predicted levels at the end of November when cases of COVID-19 were surging in those regions.

The drop in admissions was unanticipated by health systems and could have impacted each one financially in different ways. Recent study findings demonstrated the median hospital had enough cash on hand to pay its operating expenses for 53 days in 2018, but the 25th percentile hospital only had enough cash on hand for eight days. Smaller hospitals, public hospitals, and rural hospitals are considered the most likely to face financial challenges due to revenue loss from COVID-19. Some hospitals may be at risk of closing or merging if they cannot make up for the declines in revenue caused by admission decline.

Articles

New data provide insight into the economic impact of the COVID-19 pandemic on hospitals and insurers.

The Decline in Non-COVID-19 Hospitalizations Is Financially Affecting Hospitals

The Office for Civil Rights (OCR) yesterday announced the resolution of three disability discrimination complaints aimed at MedStar Health hospitals and care locations.

The resolution will ensure patients with disabilities are allowed access to support persons at MedStar Health during COVID-19. Support persons are defined as a family member, personal care assistant, similar disability service provider, or someone knowledgeable about the management or care of the patient who is authorized to assist the patient in decision making.

Patients who complained were impacted by MedStar Health’s temporary visitor restrictions adopted during COVID-19. This denied them the support they needed for their disabilities to ensure equal access to healthcare.

The patients who submitted complaints all had various support needs: a patient with neuro-cognitive deficits from a stroke, partial blindness, and cancer was denied a support person while seeking treatment for seizure symptoms in the emergency department at MedStar St. Mary’s Hospital in Maryland; a patient with hearing loss and brain dysfunction who was suffering disorientation and memory was denied a support person while recovering from heart surgery at the MedStar Washington Hospital Center in Washington, D.C.; and a person with advanced neuro-muscular autoimmune disease who and memory loss and physical impairments was denied a support person when admitted to the MedStar Georgetown University Hospital in Washington, D.C. for a pre-scheduled surgery.

Those who submitted complaints stated they were denied effective communication with their treatment teams, denied the ability to make informed decisions and provide consent, and were subjected unnecessarily to physical and pharmacological restraints.

Because of the allegations, OCR enforced Section 504 of the Rehabilitation Act and Section 1557 of the Affordable Care Act, both of which prohibit discrimination on the basis of disability in programs receiving Federal financial assistance.

Through working with OCR, MedStar Health revised its policy to distinguish between visitors and support persons — those who perform specific disability-related functions for patients with disabilities when necessary to have an equal chance to get and benefit from healthcare services. The updated policy applies to all its healthcare locations, which encompass 10 hospitals, 14 urgent care centers, seven cancer treatment centers, and more than 100 locations providing services for physical therapy, orthopedics, heart, and vascular care.

“While recognizing the safety considerations during the COVID-19 pandemic, civil rights protections remain in full force and effect and MedStar Health’s modifications in its practices ensure that patients with disabilities get the support to which they are entitled in a safe and effective way,” Robinsue Frohboese, acting director of OCR, 

MedStar Health denied patients with disabilities access to a support person.

OCR Resolves Discrimination Allegations Against MedStar Health

The use of remote and virtual care (RVC) models has increased since the COVID-19 pandemic struck. But how has using such technology impacted small healthcare businesses?

Findings of a recent study suggest small physician practices cannot harness RVC developments and consider it incompatible with business survival during and after COVID-19.

“If small healthcare firms cannot compete with RVC (or synergistically integrate RVC platforms into their current business practices) and eventually become nonoperational, the resulting damage to traditional healthcare services may be severe, particularly for critical care delivery and other important services that RVC cannot effectively replace,” Madhavan Parthasarathy, M.D., Ph.D., and a team of investigators wrote.

Parthasarathy and the team aimed to assess the perceptions of small healthcare businesses regarding the impact of RVC on their sustainability during COVID-19, as well as gauge their perceptions of their current levels of adoption of and satisfaction with RVC models and analyze how well that aligned with their perceptions of the current business scenario. Further they wanted to determine whether such perceptions influenced the view of their midterm sustainability.

The team conducted a focus group study with eight healthcare experts through Zoom — four entrepreneurs familiar with the healthcare environment in Colorado, two physicians, and two executive health faculty members. The Zoom call centered around the impact of RVC on the sustainability of small physician offices, clinics, and pharmacies during COVID-19. While those on the supported the notion that RVC adoption was posing a challenge to small healthcare businesses, they also believed clinics would likely not only benefit from but also be able to rapidly integrate RVC into their business practices more than physicians and pharmacies. The experts felt small local pharmacies would be negatively impacted during the pandemic because consumers can visit online pharmacies and avoid waiting at a physical location.

Following the focus group, a consulting firm sampled 445 small healthcare businesses in Colorado — 135 clinics, 141 physician offices, and 169 pharmacies — to validate the expert presumptions. Only 282 businesses responded to the survey and 270 were included in the final analysis.

The survey consisted of 11 questions about the current sustainability of the business in the subsequent one to three years. Respondents also answered whether they were satisfied with the current state of the business and current remote care practices.

Satisfaction with RVC and the current business scenario had direct, significant, and positive effects on respondents’ view of their midterm sustainability. The interaction term of satisfaction with RVC and current business scenario was significant and positive for clinics (

=.02), significant and negative for physician offices (

=.05), and not significant for pharmacies (

=.76). Such variations indicated that although clinics positively perceived use of RVC with their current business scenario, the opposite was true for small physician offices.

“While it is clear that the future of healthcare depends on these transformative models, the complexity lies in managing these models with appropriate support, training, reimbursement, and incentive models for small businesses, while meeting the expectations and demands of patient-centric healthcare delivery,” Parthasarathy and the team concluded.

Impact of Remote and Virtual Care Models on the Sustainability of Small Health Care Businesses: Perceptual Analysis of Small Clinics, Physician Offices, and Pharmacies in Colorado

Small healthcare businesses must manage remote and virtual care models with appropriate support, training, reimbursement, and incentive models.

Small Healthcare Businesses Struggling to Leverage Remote and Virtual Care During COVID-19

Findings of a new study suggest the use of machine learning assistance to prioritize the evaluation of patients with undiagnosed diseases is feasible and may increase the number of applications processed during a given time.

Isaac Kohane, M.D., Ph.D., and a team of investigators hope the findings create discussion around the current practices of automating referral decisions in the broader context of healthcare, which could save time and money for hospitals and health systems.

Kohane and his colleague, Hadi Amiri, Ph.D., developed computational models to effectively predict admission outcomes for applicants seeking Undiagnosed Diseases Network (UDN) evaluation. Further, they ranked the applications based on the likelihood of patient admission to the UDN.

Inclusion criteria were that the applicant should have a condition that was undiagnosed despite evaluation by a healthcare professional and had at least one objective finding pertaining the phenotype for which the application was submitted. Applications in the data set included an application form containing demographic characteristic information; an official referral letter signed by a provider summarizing the applicant’s medical problems, previous diagnoses, treatments, and medications; application submission date; application review date; and the outcome of the application.

The investigators split the data set into training (80%), validation (10%), and test (10%) categories for evaluation. They then developed a logistic regression classifier and used a grid search to optimize its hyperparameters using validation data. The classifier was trained using features obtained or extracted from application materials, including “baseline” features from patient demographic information (normalized age at time of the application, age at disease onset, disease duration, and number of prior UDN visits).

Kohane and Amiri used the confidence score of the logistic classifier to rank applications based on their likelihood of acceptance. To get the confidence score, the team conducted 

-fold cross-validation experiments with the UDN data set and stored confidence scores on test instances for final ranking. They used UDN’s review session frequency and number of applications that could be reviewed in each session to measure the mean application process time from the ranked list of applications. The team considered four approaches to generate ranked lists, including the current processing order at the UDN, first-in-first-out queue, classifier ranking, and accepted-first ranking.

Overall, the accepted cohort was significantly younger (mean age at application, 19.7 years old vs 36.5 years old) and had earlier onset of disease (mean age at symptom onset, 10.8 years old vs 28.4 years old) than those who were not accepted. The duration of the disease and number of prior UDN site visits in both groups were similar. Application processing time was significantly longer for applications that were not accepted (3.29 vs 4.73 months). A large number of applications in both groups were for neurologic (47.6% and 34.1%) and musculoskeletal symptoms (12.5% and 9.6%), while few applications were for gynecologic (.2% and .2%) or toxicologic (0% and .4%) symptoms.

The investigators found the best classifier had a sensitivity of .843, a specificity of .738, and an area under the receiver operating characteristic curve of .844 for predicting admission outcomes among 1,212 accepted and 1,210 not accepted applications. The classifier was able to decrease the mean UDN processing time for accepted applications from 3.29 months to 1.05 months — a 68% improvement — by ordering applications based on their likelihood of acceptance.

The system may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients who have undiagnosed diseases. Accelerating the process could improve the diagnostic journeys for such patients and be a model for automation of triaging or referral for other resource-constrained applications. Further, shorter turnaround time could reduce the overall cost of diagnosis because the longer it takes to accept patients, the more diagnostic routes are likely to be sought.

Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network

The machine-learning model could save time and healthcare costs. 

Samara Rosenfeld

Articles

The Decline in Non-COVID-19 Hospitalizations Is Financially Affecting Hospitals

OCR Resolves Discrimination Allegations Against MedStar Health

Small Healthcare Businesses Struggling to Leverage Remote and Virtual Care During COVID-19

Machine-Learning Model Helps Prioritize Evaluation of Patients With Undiagnosed Diseases