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Patient-Reported Outcomes Improve Quality, Equity of Care

Patient-Reported Outcomes Improve Quality, Equity of Care

For several years, patient-reported outcomes (PROs) have been a topic of interest, in part due to increased utilization of electronic data and the integration of delivery systems. PROs are defined by the Food and Drug Administration (FDA) and National Quality Forum as "any report of the status of a patient's health condition that comes directly from the patient, without interpretation of the patient's response by a clinician or anyone else." In short, PRO tools ask patients questions to measure how they feel and what they are experiencing. With patient-reported outcome measures (PROMs), patients provide information about their health, quality of life, and functional status, either in absolute terms (e.g., pain severity rating) or in response to treatment changes (e.g., new nausea onset). The goal of gathering this information from the patient’s perspective without any interpretation from a healthcare provider is to improve both the quality of care being delivered and health outcomes.

The use of PROs has a variety of potential benefits. They can elicit enhanced patient engagement, be used to clarify the patient’s priorities and thus improve shared decision-making between patients and providers, and can bring to light any benefits or harms of interventions. The potential impact of PROs, therefore, is substantial because involving patients in their healthcare is linked to a myriad of positive patient outcomes. For example, based on a review of studies investigating patient participation, some of the benefits to patients include:

  • increased satisfaction and trust,
  • empowerment,
  • greater self-efficacy to manage health,
  • higher quality of life,
  • better understanding of condition and personal requirements,
  • improved adherence to medical treatment plans,
  • improved communication about symptoms with positive and lasting effects on health.

Ever increasing in its availability, the use of PROs is included in clinical investigations, healthcare practice, healthcare management, and various regulatory or reimbursement areas. As the patient continues to become more central to healthcare, they are in the best position to determine if their healthcare objectives have been achieved. PROMs are not the same as measures reported by patients on their experience of the healthcare system, such as being treated with dignity or waiting too long; however, patient-reported outcome-based performance measures (PRO-PMs) are beginning to find their way into healthcare and may integrate such measures. To help understand the relationship between PROs, PROMs, and PRO-PMs, see Figure 1, which was designed by the Centers for Medicare and Medicaid Services (CMS) in their supplemental guide on PROMs.

Figure 1.

To gather PROs, the tools and instruments known as PROMs must measure criteria that are identifiable, valid, and reliable. Most often these are general or disease-specific self-completed questionnaires, scales, or single-item measures that provide a score for any of the following:

  • functional status,
  • health related quality of life,
  • symptom and/or symptom burden,
  • personal experience of care,
  • health-related behaviors.

Generic PROMs often delve into areas covered by a variety of different conditions, allowing for comparisons across multiple medical conditions. These PROMs help with evaluation and implementation of care provision methodology and equality of service delivery. Some may even provide a cost-effectiveness component. Disease-specific PROMs identify the impact of definitive symptoms on the condition. PROMs can be used as either the primary or secondary outcome measure of a study or trial, and most studies use a combination of disease-specific and generic PROMs.

Measurement tools integrate other existing data (biological, genetic, clinical, and physical) to assess how a patient is functioning regarding their overall health, quality of life, mental well-being, or satisfaction with a healthcare process. Using all these data sources provides a more complete picture of the patient’s health journey and allows for patients and their providers to share decision-making and define individualized care. They also provide a unique opportunity to identify inequalities in healthcare access and treatment.

When utilizing PROMs, practitioners must plan for how the information will be collected and utilized. PROMs can be collected in a variety of ways, including face-to-face interviews, online or paper questionnaires, telephone interviews, or diaries. When deciding which PROMs to utilize, it is important to consider the preferences of patients, providers, and any other involved decision-makers. It is also essential to consider the cognitive, physical, demographic, and socioeconomic barriers that may exist for the patient to ensure they have adequate accommodations to participate. The length, schedule, and timeframe of assessments should also be appropriately assessed, along with any permissions needed to use the information. Lastly, the PROMs should be easy to score and interpret, actionable, and able to facilitate clinical decisions.

The use of PROs is here to stay. The hope is that improvements in interoperability, data governance, security, privacy, and ethics will allow greater integration of PROs. In turn, PROs will allow patient preferences, needs, and health outcomes to further drive value-based healthcare.

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Predictive Analytics Assist with Chronic Disease Prevention

Predictive Analytics Assist with Chronic Disease Prevention

The healthcare system has an immense wealth of information at its digital fingertips. Big data is constantly expanding from sources such as digitized patient records, patient wearables, medical apps, genome datasets, monitoring devices, and more. A critical challenge facing hospitals and health systems today is in effectively identifying strategies and personnel to utilize big data in a way that influences clinical care. Those that succeed in this task will find themselves in a much better position to advance care and improve patient outcomes.

One developing strategy to convert big data sets into improved patient outcomes is the use of predictive analytics, an approach that differs from what many hospital quality improvement departments are currently utilizing. For example, the Michigan Value Collaborative (MVC) Coordinating Center has been helping hospitals identify opportunities for quality improvement since 2013 by aggregating and analyzing payor claims data and presenting the results on the registry and in analytics reports. The goal of these efforts is to help hospitals compare utilization against peers and draw important insights across a range of medical and surgical procedures. This retrospective approach helps MVC members to learn from their past performance in order to pursue meaningful, observable improvements within their buildings. It is one piece of the big data puzzle. Predictive analytics, on the other hand, allows clinicians to utilize big data before their patient experiences significant healthcare services or treatments. As its name denotes, this approach identifies prevention opportunities before the incidence of disease by predicting a patient’s risk. This is especially important for diseases that require early detection for optimal treatment and survival.

Unlike Robotic Process Automation (RPA), which is also on the rise in health systems across the country, predictive analytics is performed by Artificial Intelligence (AI). This means that computer systems will perform tasks typically requiring human intelligence, including analyses and decision-making. In some ways, this strategy mimics what physicians have long been doing at a patient’s bedside: collecting a patient’s medical history and risk factors in order to tailor their treatment and advice. This process is essential in evaluating a patient’s risk of developing chronic diseases, which often run in their family or are more likely due to socioeconomic factors. An article from the University of Illinois Chicago posits that predictive analytics represent a significant potential for cost savings if they help clinicians and their patients prevent the onset of chronic diseases, one of healthcare’s costliest areas.

“On a population-wide level, predictive analytics can help greatly cut costs by predicting which patients are at higher risk for disease and arrange early intervention, before problems develop,” the article stated. “This involves aggregating data that are related to a variety of factors. These include medical history, demographic or socioeconomic profile, and comorbidities.”

The Centers for Disease Control and Prevention (CDC) states that, “90% of the nation’s $3.8 trillion in annual health care expenditures are for people with chronic and mental health conditions.” So the potential cost savings from reducing chronic disease treatment are significant.

Using predictive analytics in a clinical setting can leverage both patient records and socioeconomic factors. Medical records will often include family history of chronic diseases such as cancer, diabetes, and heart disease, which would make a patient more likely to develop the condition themselves. In addition to family history, a patient’s socioeconomic factors (e.g., education, employment, and environment) and lifestyle choices are significant predictors of chronic disease. A study in the American Journal of Preventive Medicine outlines how researchers used predictive analytics to screen for cardiovascular disease risk from social determinants of health, and ultimately guide clinician treatment options. The researchers also suggest that large databases about social determinants of health variables, especially environmental ones, are not as readily available as they should be, and are an important area of opportunity for future data collection efforts.

A similar application of this technology was used in a study published by Cancer Immunology Research to predict lung cancer immunotherapy success. In the study, researchers used an AI algorithm to identify changes in patterns from CT scans that were previously not detected by clinicians, which ultimately predicted how well a patient would respond to immunotherapy. This suggests that predictive analytics can help improve the accuracy of diagnoses and treatment.

Of course, the applications for predictive analytics extend beyond chronic disease prevention and treatment. In the past year, researchers have also used predictive analytics to forecast outcomes for patients positive for COVID-19. In The American Journal of Emergency Department Medicine, a published study validated a tool that helps physicians predict adverse events among patients presenting with suspected COVID-19. The study suggests that the algorithm and scores can help physicians decide when to hospitalize or discharge patients during the pandemic. Therefore, predictive analytics appear to also provide insights that enhance treatment.

Many additional articles (such as one article from Health IT Analytics) and published studies recommend predictive analytics for its potential benefits. As with any technology, however, it is not without its risks. The use of AI brings about concerns for privacy, especially since hospitals must properly steward patient data and comply with HIPAA regulations. But there are several other considerations identified in a recent Deloitte analysis (see Figure 1), not the least of which is ensuring the algorithm doesn’t introduce bias that disproportionately harms minorities and communities of color. Predictive analytics may also present evaluation challenges. Once algorithms are validated, their widespread use in clinical settings should be confirmed for their efficacy, which requires measuring the absence of disease.

Figure 1.

The potential benefits of predictive analytics are variable and significant; however, as healthcare learns to integrate AI technologies, it will be important to keep its risks in mind and address them accordingly. The MVC Coordinating Center endeavors to assist its members through their data analytics journey by providing insights into specific data sets. When pursuing additional technologies or analytic tools, the Coordinating Center encourages members to volunteer as a sounding board and resource for other members. If your hospital or physician organization is currently utilizing AI or considering it with your patient data, we encourage you to reach out so MVC can share your experience with others. You can reach the MVC Coordinating Center at michiganvaluecollaborative@gmail.com.

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Condition Selection Process Announced for MVC Component of BCBSM P4P Program

Condition Selection Process Announced for MVC Component of BCBSM P4P Program

This week the Michigan Value Collaborative (MVC) Coordinating Center announced the condition selection process for program year (PY) 2022 and PY 2023 of the MVC Component of the Blue Cross Blue Shield of Michigan (BCBSM) Pay-for-Performance (P4P) program. The timeline for each program year’s stages are detailed in Figure 1.

Figure 1.

In the announcement, hospitals were tasked with selecting two conditions for which they will be evaluated and returning their condition selection form to the Coordinating Center by Friday, August 13, 2021. The announcement also outlined changes to the scoring methodology, cohort assignments, and bonus points available.

The Coordinating Center’s recent announcement included condition selection reports with targets for each condition option that may help inform hospitals’ selection decisions. Each participating hospital will choose two of the seven available conditions for PY22 and PY23: spine surgery, joint replacement, chronic obstructive pulmonary disease (COPD), coronary artery bypass grafting (CABG), congestive heart failure (CHF), colectomy (non-cancer), and pneumonia. When selecting conditions, the Coordinating Center recommends reviewing your data in the registry and considering several factors for each condition, including case counts and identifiable areas with the greatest cost opportunities. The Coordinating Center also recommends considering where resources are currently being directed in your facility and potentially aligning with those efforts.

One notable change from prior program years is the methodology by which hospitals earn achievement and improvement points. Hospital scores will continue to be based on a hospital’s risk-adjusted, price-standardized total episode payments for two selected conditions, and they can still earn a maximum score of 10 points. However, the improvement and achievement scores will become more similar in order to be placed on the same scale. As such, the achievement equation will change from being based on rank within MVC cohort at performance year to being based on distance from MVC cohort mean at baseline year. Similarly, the improvement equation will utilize the distance from the hospital’s mean at baseline. These new equations (see Figure 2) as well as complete descriptions of the updated methodologies are reviewed at length with examples in the technical document.

Figure 2.

P4P cohorts have also been reassigned for PY22 and PY23. These changes are also detailed in the technical document, and the new cohort assignments can be found on the MVC website. The cohorts are not intended to group hospitals that are exactly alike; rather, they create a reasonably-comparable grouping from which MVC can complete statistical analysis.

The final change is to the awarding of bonus points. In place of the previous 5% cohort reduction bonus, participants can instead earn bonus points by completing two questionnaires (one per selected condition) and submitting these to the Coordinating Center by November 1st of each program year. The purpose of this is to gather examples of quality improvement initiatives in operation at MVC member hospitals to share with the Collaborative. Moving forward, this will help support members in reducing costs through collaboration.

Each of the changes mentioned above are designed to deliver a more transparent, intuitive, flexible, and fairer P4P program. The Coordinating Center will offer an explainer webinar to answer questions and walk through the details of these changes in more detail. The webinar will be offered on two dates: the first is scheduled for Thursday, July 29 from 11:00-12:00 pm, and the second is on Tuesday, August 3 from 1:00-2:00 pm. Both webinars can be accessed using the following Zoom link: https://umich.zoom.us/j/95502303999. Participants can also call +1 301 715 8592 (meeting ID #955 0230 3999). For those interested in the explainer webinar who are unavailable on both dates, a recording of the first webinar will be available. If you are interested in receiving a link to this recording, please email the MVC team at michiganvaluecollaborative@gmail.com.

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Introducing MVC’s Newest Analyst, Kristen Palframan, MPH

Introducing MVC’s Newest Analyst, Kristen Palframan, MPH

I am excited to have joined the Michigan Value Collaborative (MVC) this month as a data analyst. I’m really looking forward to working with the MVC team and using my experience in data management and analysis to support the goal of improving the quality and value of healthcare in Michigan.

My background is primarily in research and data analysis. I have a Bachelor of Science degree in Animal Behavior from Bucknell University. After conducting behavioral research and wildlife disease fieldwork with animals throughout and following college, I developed an interest in disease prevention and came to Michigan to pursue a Master of Public Health (MPH) degree from the University of Michigan School of Public Health. During my MPH program I took a variety of epidemiology and statistics courses, and I particularly enjoyed those that involved programming in SAS and SQL. After graduating from the University of Michigan with an MPH degree in Epidemiology in 2018, I worked for three years as an epidemiologist for the U.S. Department of Veterans Affairs (VA) in the Office of Mental Health and Suicide Prevention. At the VA, I worked on analyses, reports, dashboards, and manuscripts focused on supporting suicide prevention among U.S. Veterans. My work for the VA primarily used electronic medical record data from the Veterans Health Administration as well as mortality data from the Centers for Disease Control and Prevention’s National Death Index.

Now I am thrilled to use my experience in healthcare data analysis to support MVC’s mission and I’m looking forward to growing as an analyst and gaining experience working with claims data. If you have any questions or would like to contact me, please feel free to email me at kpalf@med.umich.edu.

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Custom Hospital Analytics Result in Case Study for Collaborative

Custom Hospital Analytics Result in Case Study for Collaborative

The Michigan Value Collaborative (MVC) Coordinating Center encourages its members to seek out custom analytics to inform and support ongoing quality improvement activities. These requests can help hospitals and physician organizations dig deeper into specific aspects of their administrative claims data and, as a result, better understand areas for improvement.

As custom analytics have been prepared and shared with respective members, the Coordinating Center has endeavored to learn the extent to which these analytics have been utilized. The resulting feedback has enriched MVC’s understanding of its members’ quality initiatives, and presents a great opportunity for MVC to educate its members about the successes and lessons learned of their peers.

In that spirit, the Coordinating Center has sought the permission of various hospitals to generate case studies based on this collaborative work. One such case study featuring McLaren Port Huron Hospital was created this past year and shared with the entire Collaborative via the MVC Newsletter (Figure 1). It features a custom analytics request about the rates and adherence of follow-up visits in their congestive heart failure (CHF) population as well as readmission rates for chronic obstructive pulmonary disease (COPD). The resulting custom analytics reports prepared by the Coordinating Center were also accompanied by best practice sharing sourced from other Collaborative members.

Figure 1.

The Coordinating Center plans to continue to generate shareable case studies about similar requests if those facilities have provided their permission. Similarly, MVC will continue to identify such opportunities for information sharing and networking across facilities in order to support its members.

If any members of the Collaborative are interested in pursuing custom analytics in the future or have ideas to share across hospitals, please contact the Coordinating Center at michiganvaluecollaborative@gmail.com.