Center for Evidence-based Practice | University of Pennsylvania Health System

Major Gaps in Hepatitis C Care Identified As New Drugs and Screening Efforts Emerge, Penn Study Finds.
Less than 10 percent of People Infected with Hepatitis C are Cured

July 2, 2014 — A new meta-analysis published online in PLOS ONE by infectious disease and epidemiology specialists from the Perelman School of Medicine at the University of Pennsylvania highlights significant gaps in hepatitis C care that will prove useful as the U.S. health care system continues to see an influx of patients with the disease because of improved screening efforts and new, promising drugs.

In the largest study of its kind, the team examined data culled from 10 studies between 2003 and 2013 and found that less than 10 percent of people infected with hepatitis C in the United States — 330,000 of nearly 3.5 million people — were cured (achieved viral suppression) with antiviral hepatitis C treatment. The researchers also found that only 50 percent of people were diagnosed and aware of their infection; 43 percent of those with the disease had access to outpatient care; and only 16 percent were prescribed treatment.

“This study puts forth a good baseline of hepatitis C care in the United States over the last 10 years—which will be useful in monitoring the success and impact of new screening efforts and advances in antiviral therapy,” said the study’s first author,Baligh Yehia, MD, MPP, MSHP, an assistant professor of Medicine in Penn’s division of Infectious Diseases. “There are many people who don’t know that they have the infection, don’t have access to hepatitis C care and medications, and who haven’t been treated. With this data, we can see these gaps more clearly. This information will be useful for ensuring better access to hepatitis c care and treatment in the coming years.”
In June, the Centers for Medicare and Medicaid Services began reimbursing for hepatitis C virus screenings for two target populations, including baby boomers (those born between 1945 through 1965) and those at high risk for the infection. Six months prior, the U.S. Food & Drug Administration (FDA) approved sofosbuvir, an oral medication shown to cure most cases of hepatitis C infection, with fewer side effects than the current treatment options. Other drugs — which have shown success in clinical trials, some conducted at Penn Medicine—are expected to gain FDA approval within the year.

“The new regimens will be game changers in the treatment of chronic hepatitis C,” said senior author Vincent Lo Re III, MD, MSCE, assistant professor of Medicine and Epidemiology in the division of Infectious Diseases and department of Biostatistics and Epidemiology at Penn. “Given the high prevalence of this infection, particularly in baby boomers who didn’t know they were infected, having new, highly-effective treatment options to eradicate the virus will be a tremendous benefit to patients that will ultimately help us to reduce liver-related complications and re-infection rates.”

Such advances are expected to increase the number of patients treated for the disease. In the 1990s, HIV treatment turned a monumental corner with the advent of antiretroviral therapy. “It’s a very similar situation that we can learn from,” said Yehia. “With those advances, came challenges with access to and engagement in care. As hepatitis C therapy continues to advance, a focus on improving diagnosis, linkage to care, and insurance coverage will be more critical.”

The team screened close to 10,000 articles before identifying 10 studies that address one or more steps in the cascade of care, ranging from diagnosis to viral suppression. Some of the data came from the National Health and Nutrition Examination Survey and the Chronic Hepatitis B and C Cohort study. The researchers addressed seven key steps along this cascade and estimated the following based off the data analyzed:

“The advent of new antiviral agents for hepatitis C will shorten treatment duration, likely increasing the number of people offered treatment, and improving cure rates, which are the final two steps of the hepatitis C treatment cascade,” said Yehia. “However, educating providers and the general public about prevention, care, and treatment, ensuring access to providers skilled in the treatment of hepatitis C, and addressing the high cost of these agents will be critical to maximizing the benefits of these new therapies.”
Co-authors of the study, which was funded by the National Institutes of Health, include Craig A. Umscheid, MD, MSCE, and Asher J. Schranz, MD.

Penn Experts Urge Focus on Reducing Preventable Hospital Readmissions, Estimated to Constitute Just 25 Percent of All Readmissions.
Current efforts may hinder quality improvement and unfairly penalize hospitals

June 25, 2014 — Experts from the Perelman School of Medicine at the University of Pennsylvania and Vanderbilt University suggest that the reporting of hospital readmission rates should be based exclusively on preventable or potentially preventable readmissions, in a review published online in the Journal of Hospital Medicine. Currently the Centers for Medicare and Medicaid Services (CMS) does not take into account whether a readmission is preventable when assessing hospitals’ performance on this quality metric.

Under the Affordable Care Act, hospitals must report readmission rates for heart attack, heart failure, and pneumonia to CMS. CMS then imposes financial penalties on institutions having an excessive number of readmissions that take place within 30 days following patient discharge. Additional medical conditions will be added in 2015. Many patients with these conditions suffer from additional illnesses that are complex and come with many co-morbid conditions.
“Reducing hospital readmissions is clearly important on many levels,” says lead author Julia G. Lavenberg, PhD, RN, a research analyst at Penn’s Center for Evidence-based Practice. “Patients prefer to remain at home, payers save money, and hospitals avoid financial penalties for having high readmission rates. But while current policy assumes that a significant proportion of readmissions are preventable, research tells us that this is simply not so. Moreover, there is no consensus in the medical and policy communities on how to define preventable readmissions, which is essential for taking action to reduce them.”

Other payers, such as private insurers, are likely to follow the federal lead and withhold funding for high readmission rates. As a result, hospitals and health systems nationwide are devoting significant time, effort, and money to reducing readmissions. Steps include increasing patient education before discharge, introducing or expanding home health visits, and working more closely with nursing homes and rehabilitation centers.

“Current research tells us that only about 25 percent of hospital readmissions are preventable,” says senior author Sunil Kripalani, MD, MSc, chief of the Section of Hospital Medicine at Vanderbilt University. “We urge a focus on preventing these readmissions, so that hospitals can enhance efforts in areas where it will have the greatest effect as well as ensure fair and equitable reporting of hospital performance.”

Until a validated measure of preventability is developed, the authors recommend several steps. First, the readmission time horizon should be reduced from the current 30 days to seven or 15 days, as research suggests that early readmissions – those within seven to 15 days of discharge -- are more likely preventable than those occurring later. Second, policymakers should take the socioeconomic status of patients into account by only comparing hospitals serving similar patient communities when determining penalties for excess readmission rates. Finally, adjusting for other community factors such as practice patterns and access to care is necessary to more accurately reflect factors under a hospital’s control.

“We’re encouraging a major shift in perspective,” adds co-author Craig A Umscheid, MD, MSCE, a hospitalist and Director of the Center for Evidence-based Practice at Penn. “We want policymakers to acknowledge that hospitals should not be penalized for factors beyond their control which may play an important role in readmissions, such as inadequate community health resources or severity of the patient’s illness.”

Such a shift would require agreement among healthcare researchers and policymakers on how to identify and measure preventable or potentially preventable readmissions. While there are existing methods for doing so, there is no consensus on which is best. Furthermore, some of these methods are proprietary and thus unavailable for evaluation by others. These methods use such techniques as identifying readmission for conditions closely related to the original diagnosis or complications arising from the original admission.

To rectify this gap, the authors urge healthcare researchers and policymakers to come to agreement on a transparent, universal method for defining preventable or potentially preventable readmissions. This could include algorithms -- based on insurance claims data -- that recognize patterned relationships between original and readmission diagnoses for a variety of medical conditions. Then, clinicians at individual hospitals could use these standards to evaluate whether individual readmissions were potentially preventable or not. In time, researchers could codify these individual decisions into best-practice standards to serve as guides for subsequent assessments.
In addition to Lavenberg and Umscheid, Penn co-authors include Brian Leas; Kendal Williams, MD; and David R. Goldmann, MD.

Dr. Umscheid was supported in part by the National Center for Research Resources and the National Center for Advancing Translational Sciences (UL1TR000003). Dr. Kripalani receives funding from the National Heart, Lung and Blood Institute (R01HL109388), and from CMS (1C1CMS331006-01 and 1C1CMS330979-01).

Penn Study Shows Automated Prediction Alert Helps Identify Patients at Risk for 30-Day Readmission

11/27/2013 An automated prediction tool which identifies newly admitted patients who are at risk for readmission within 30 days of discharge has been successfully incorporated into the electronic health record of the University of Pennsylvania Health System. The tool, developed by researchers at the Perelman School of Medicine, is the subject of a study published in the December issue of the Journal of Hospital Medicine.

The all-Penn team found that having been admitted to the hospital two or more times in the 12 months prior to admission is the best way to predict which patients are at risk for being readmitted in the 30 days after discharge. As a result of this finding, the automated tool is now able to identify patients as being “high risk” for readmission and creates a “flag” in their electronic health record. Upon admission of a high-risk patient, the flag appears next to the patient’s name in a column titled “readmission risk.” The flag can be double-clicked to display detailed information relevant to discharge planning including inpatient and emergency department visits over the previous 12 months, as well as information about the care teams, lengths of stay, and problem(s) associated with those prior admissions.

“The results we’ve seen with this tool show that we can predict, with a good deal of accuracy, patients who are at risk of being readmitted within 30 days of discharge,” said lead author Charles A. Baillie, MD, an internal medicine specialist and fellow in the Center for Clinical Epidemiology and Biostatistics at Penn Medicine. “With this knowledge, care teams have the ability to target these patients, making sure they receive the most intensive interventions necessary to prevent their readmission.”

Interventions proven to help reduce 30-day readmissions include enhanced patient education and medication reconciliation on the day of discharge, increased home services to provide a safe landing, follow up appointments soon after discharge, and follow-up phone calls to ensure an extra level of protection. In the process of medication reconciliation, pharmacists compare a patient's current hospital medication orders to all of the medications that the patient was taking at home prior to their hospital admission. This is done to avoid medication errors such as omissions, duplications, dosing errors, or drug interactions.

In support of the study, the Penn Medicine Center for Evidence-based Practice identified in the published literature a number of variables associated with readmission to the hospital, including: prior admissions, visits to the emergency department, previous 30-day readmissions, and the presence of multiple medical disorders.

Using two years of retrospective data, the team examined these variables using their own local data and found that a single variable – prior admission to the hospital two or more times within a span of 12 months – was the best predictor of being readmitted in the future. This marker was integrated into the electronic health record and was studied prospectively for the next year. During that time, patients who triggered the readmission alert were subsequently readmitted 31 percent of the time. When an alert was not triggered, patients were readmitted only 11 percent of the time.
“By automating the process of readmission risk prediction, we were able to provide risk assessment quickly and efficiently in real time, enabling all members of the inpatient team to carry out a coordinated approach to discharge planning, with special attention paid to those identified as being at the highest risk for readmission,” said Craig A Umscheid, MD, MSCE, assistant professor of Medicine and Epidemiology, director of the Penn Medicine Center for Evidence-based Practice, and senior author on the study.

The risk assessment tool is part of a series of steps taken by Penn Medicine to reduce readmissions.

“Readmission rates should improve over time as the risk flag is used more routinely and the interventions necessary to reduce readmission rates for those identified as high risk are implemented,” said Baillie.

In addition to Baillie and Umscheid, other Penn Medicine co-authors include Christine VanZandbergen, Gordon Tait, Asaf Hanish, Brian Leas, Benjamin French, C. William Hanson, and Maryam Behta.
http://www.uphs.upenn.edu/news/News_Releases/2013/11/baillie/