Translating Shades of Grey: How can we accelerate value in health care?Published 22 days ago
By Margot Walthall, MHA, and Darshak Sanghavi, MD, OptumLabs
Change is hard, particularly as our country focuses more on value — not volume — in health care.
Getting paid based on number of tests or procedures has led to over-treating patients, exposing them to unnecessary health complications and higher costs. By focusing instead on the most effective treatments based on research, we can deliver the highest quality care, while saving money for the health system — and for patients.
We know some medical tests and procedures are more harmful than helpful for certain patients. They may create additional health risks, cost a lot, have unpleasant side effects and, in some instances, lead to a cascade of unnecessary or unsafe follow-on services.
These “low-value” medical services are more common than you might realize. With the input of more than 70 medical specialty societies, the Choosing Wisely campaign has identified hundreds of low-value services (LVS) to help improve care decisions between providers and patients.
For instance, we think of cancer screenings as generally useful preventive measures (the sooner you catch something, the better) that saves many lives. However, these experts found that colonoscopies for patients older than 75 can be more harmful than helpful. In these patients, colonoscopies can put them at risk for intestinal tears, dehydration and fainting. Combine this fact with additional out-of-pocket costs, time and stress associated with the screening and ask: Are these tests worth the costs if the patient does not have specific risk factors for colon cancer?
It can be challenging to reconcile population-based recommendations when treating an individual person. Moreover, our research with AARP reveals that reducing the use of some of these LVS is easier said than done. This suggests that while our health care system has made some progress in transitioning to high value tests and procedures, we have a long way to go.
Some low value services are easier to reduce than others
The appropriate use of health care services is an important issue for AARP, OptumLabs’ founding consumer advocate partner, and its nearly 38 million members. We wanted to see whether efforts to reduce low value services were having an impact on the health system.
Using de-identified OptumLabs claims data, researchers from AARP and OptumLabs analyzed trends in use of 16 low-value services from 2009, before the creation of Choosing Wisely, to 2014 among adults ages 50-plus with commercial insurance and adults ages 65-plus with Medicare Advantage.
Overall, we found that since the Choosing Wisely campaign and other efforts to increase value in health care began, there continues to be a lot of variation — clear successes and additional opportunities — in the use of specific LVS.
Here are some examples.
Declines in chest x-rays before surgery
For decades, it’s been common for doctors to routinely give patients chest x-rays before surgery as a safety measure. Because these x-rays expose patients to radiation, can cause many false alarms and cost extra money, Choosing Wisely recommends that only patients with relevant risks should get them, such as those who:
- Have signs or symptoms of a heart or lung condition
- Have heart or lung disease
- Are over 70 years old and haven’t had a chest X-ray within the last six months
- Are having surgery on the heart, lungs or any other part of the chest.
Based on our research, it appears many doctors have gotten the message and researchers found chest x-rays before surgery have fallen considerably across all populations over the six years.
Figure 1. Pre-operative chest x-ray, 2009-2014
Declines in cervical cancer screening for women over 65
According to American Cancer Society Guidelines, women over 65 who have had regular pap smears over the past 10 years should not be screened for cervical cancer unless they have a serious cervical pre-cancer. This recommendation is because the risk of cancer is low at those ages and the testing can lead to unnecessary treatments.
Reassuringly, researchers found a substantial decline in cervical cancer screening for women over 65 who had commercial insurance. Initially, the rates of over-testing were markedly higher in the Medicare Advantage subgroup compared to commercial enrollees, but over time, that gap has dramatically improved.
Figure 2. Cervical cancer screening, 2009-2014
Little reduction in MRIs for low back pain
Use of MRI to help diagnose low back pain, one of the most common conditions among adults, had a much more subtle decline. A recent Health Affairs study showed similar findings. Why is this type of imaging more difficult to reduce, despite evidence that many patients with uncomplicated low back pain do not get better faster with imaging?
Figure 3. MRI for low back pain, 2009-2014
To determine if an MRI for low back pain is warranted — and it is in certain situations — doctors need to have careful conversations with their patients about all of their symptoms and health history instead of just ordering the imaging “to be safe.” MRIs are expensive and may be associated with higher rates of unnecessary surgery. But, doctors may not have enough time or may feel pressure from their patient to do the imaging. Therefore, reducing MRI scans in this setting appears to be more challenging.
How can we accelerate change?
The results of our work with AARP show that curtailing some low value services is more difficult than others. Awareness alone is not always enough for change. But there are some additional tactics that can help push change. These include reducing barriers to patient-provider conversations, rewarding a de-implementation culture of LVS and encouraging bundles of related — and potentially unnecessary — services for common patient situations.
Reduce conversation barriers
Thoughtful provider-patient conversations about which services are recommended, and when, are core to ensuring value in health care. However, a recent survey of providers suggests it has become more difficult to have these discussions with patients. Reasons cited include limited time during office visits, a lack of data to make confident choices, patient insistence and a desire to keep patients happy, as well as malpractice concerns and wanting to do certain things “just to be safe.”
To solve this, we need to start having broader conversations with patients about what treatments are and are not appropriate so that both doctors and patients feel confident about the choices they make together.
Create a culture for change
According to the same provider survey, a cultural change within an organization is required to prioritize the de-implementation of wasteful services as much as the implementation of new treatments. Experts highlight four key actions for organizations to reduce LVS:
- Ensure leaders prioritize the change.
- Create a culture of trust, innovation and improvement.
- Establish a shared purpose and language.
- Commit resources to the measurement of value.
Take it to the bundle
Financial incentives have been blamed for a lot of overuse. Under fee-for-service models, the more doctors treat, the more they are paid. Some bundled payment models that focus on a collection of services rather than individual services have been shown to motivate higher quality health care at lower cost. For example, a possible “Low back pain service bundle” could offer the opportunity to address the overuse of imaging, opioids, surgeries and more. This could lead to fewer MRI scans that result in unnecessary surgery, yielding lower costs and better care.
As a society, we’ve accepted the fact that many patients receive low value medical services, which continues to take its toll on the health care in America. To move forward in studying and translating what works for de-implementation, we have to focus on better methods that measure clinically meaningful outcomes and unintended consequences for patients.
ABOUT THE AUTHORS
*Margot Walthall, MHA, is vice president of integrated programs and translation at OptumLabs
*Darshak Sanghavi, MD, is chief medical officer and senior vice president of translation at OptumLabs
Using new data to personalize stroke preventionPublished 45 days ago
By Peter Noseworthy, MD, with Xiaoxi Yao, PhD, Mayo Clinic
The irregular heartbeat known as atrial fibrillation (AF) is a common and serious heart disease impacting about 3 million Americans today. Patients with AF experience palpitations, shortness of breath and dizziness, which can make simple activities such as playing with grandchildren or walking upstairs challenging. They also face a five-fold higher than average risk of stroke, a complication many fear more than death. Fortunately a class of drugs called anticoagulants, or ‘blood thinners,’ can help prevent stroke.
For 50 years, the only blood thinning drug option had been warfarin, which can be a cumbersome treatment because it interacts with certain foods and requires frequent blood testing to measure its effect on blood clotting. But over the past decade, new non-vitamin K antagonist oral anticoagulants (NOACs) have emerged. Based on the results of highly controlled clinical trials, NOACs are at least as effective as warfarin in stopping strokes. Importantly for patients, these drugs are more convenient to take. Blood testing is not required and there may be fewer problems with ingesting various food and drugs.
Figure 1. Timeline of oral anticoagulant medication options
Does that mean that we are free to prescribe these new treatments to all our patients with AF? Not so fast. While NOACs may provide patients with a better quality of life, important questions remain on getting the right treatment to the right patient. That’s where Mayo Clinic’s work with OptumLabs comes into the picture.
As a cardiologist, I try to help my patients control their symptoms and avoid stroke so that they can enjoy long and fulfilling lives. Now, with at least five different anticoagulant drugs options, as well as several devices and procedures available, choosing the right treatment for an individual patient can be difficult. Increasing this difficulty is that none of the major clinical trials compared each new NOAC drug option to one another, and many of my patients are different than those who participated in the clinical trials.
Fortunately, the data collected by OptumLabs from routine clinical encounters have helped us answer many of these questions that can help millions of people with AF.
Comparing new drugs in the OptumLabs Data Warehouse
Over the past five years, my colleague Dr. Xiaoxi Yao and I have used the OptumLabs Data Warehouse, a curated data set that includes de-identified claims data from more than 125 million commercial and Medicare Advantage beneficiaries of diverse ages and races across the U.S., to publish nearly 30 papers on the topic. We’ve studied three critical anticoagulation areas: effectiveness and safety, appropriate dosing and kidney damage over time.
Effectiveness and safety
Past clinical trials showed that NOACs generally prevent stroke as effectively as warfarin, but they do not compare each type of NOAC drug to one another in terms of stroke prevention and other possible side effects. In our study published in CHEST, we looked at medical claims data from October 2010 to February 2015 and compared stroke and bleeding episodes among three groups of patients with AF based on the NOAC that they took.
It turned out the three drugs are pretty similar for preventing stroke, but have different safety profiles when it comes to bleeding risk. Apixaban users had the least major bleeding, for example, while rivaroxaban users had the most major bleeding.
Figure 2. NOAC head-to-head comparison for major bleeding risk
NOACs have to be dosed very carefully. If the dose is too high, then patients could have unexpected bleeding, and if the dose if too low, they may develop blood clots and be at risk for a stroke. A key to giving a patient the right dose is first to know how well their kidneys work. This is important since some patients with kidney damage, which can be common in people with AF, may need much lower doses than patients without kidney damage because they have an increased risk for bleeding. To see if doctors were properly giving a lower dose to people with kidney problems, we looked at 14,865 patients with AF from October 2010 to September 2015.
In our report published in the Journal of the American College of Cardiology (JACC), we found almost 1 in 5 patients overall weren’t taking the right dose. This means many patients received a dose that was potentially too high, putting them at risk of bleeding, or potentially too low, increasing the risk of a stroke.
Figure 3. Prevalence of inappropriate NOAC dosing
Kidney damage over time
Even when dosed properly, researchers have noticed that warfarin and NOACs may gradually harm kidneys over time. In addition to stroke and bleeding risk, kidney damage itself is an important outcome to understand in patients taking anticoagulants. Published in JACC, another one of our studies looked at this in 9,769 patients with AF between October 2010 and April 2016.
We found that within two years of being on an oral anticoagulant, kidney damage was fairly common. NOACs as a group, however, were associated with less kidney damage than warfarin.
Figure 4. Kidney safety and outcomes associated with various oral anticoagulants
Putting the story together for patients
Keeping all of this in mind when prescribing anticoagulants for patients can be hard. So our team is working on a decision tool that considers the data from our research, as well as the research of others, to help doctors choose the safest, and most effective treatment plan for our patients.
Here’s an example of how our learnings can support appropriate anticoagulation choice for two very different hypothetical patients.
By combining large “real world” data sets with the information we’ve gained from clinical trials, I believe we are several steps closer to being able to precisely identify the right treatment for each individual patient.
Advancing the use of real-world data to help speed treatments to patientsPublished Feb 12 2018, 12:08 PM
By William Crown, PhD, OptumLabs
For more than 20 years, I used medical claims data to compare the effects of medications on patient outcomes and cost. In the back of my mind (and sometimes the front) I was always worried about what I might be missing since claims data contained limited clinical information. For example, I knew that it would be helpful to know the body mass index, cholesterol levels and blood pressure of patients when studying heart failure drugs. And, when studying breast cancer treatment, it would be critical to control for cancer stage. But none of this information was available in claims data alone.
This changed in 2009 with the HiTECH Act, which mandated meaningful use of electronic medical record (EMR) data for health care providers. The HiTECH Act transformed the data landscape almost overnight by making it possible to broaden our view into patient care and outcomes by linking patient-level EMR and claims data.
My previous concerns about possibly missing something important turned to curiosity: Could using real-world data such as linked patient claims and EMR records simulate a randomized controlled trial (RCT)? Could important evidence be produced in months, rather than years, to dramatically cut development costs and accelerate patient access to effective therapies?
OptumLabs is working to answer these questions.On the path to drug development, clinical trials typically take 6-7 years. Source: Pharmaceutical Manufacturers Association of America
What’s so great about randomized controlled trials?
Randomized controlled trials (RCTs) are used to obtain FDA approval to manufacture and sell a new drug. By randomly assigning patients to treatment and comparator groups, RCTs minimize bias from missing control variables. Randomization also reduces bias intentionally or unintentionally introduced by the investigator or sponsor in the study.
While these trials use strict research protocols to help ensure the collection of high quality data, their idealized environment often doesn’t reflect reality. For example, people do not always take their medication as prescribed for a variety of reasons. Data shows that people with chronic conditions like diabetes, hyperlipidemia, or hypertension don't take their medication 25-50 percent of the time.1 From the perspective of medication adherence alone, the very features of the research protocol that help to strengthen the internal validity of RCT results also may limit their generalizability to the real world.
The times, they are changing
Recent FDA regulatory mandates acknowledge this limitation of RCTs and a growing interest in using real-world evidence (RWE) — or, the output of research using real-world data — for approving new uses of an already approved drug and monitoring data to protect patient safety. Reauthorization of the Prescription Drug User Fee Act (PDUFA VI2) and the 21st Century Cures Act3 now require that the FDA
- Conduct a public workshop on regulatory uses of RWE by the end of FY 2018
- Fund a pilot and set methodology for RWE by the end of FY 2019
- Publish draft guidance by end of FY 2021
Researchers have already been generating RWE via claims analyses to gather safety data on approved products (e.g., FDA's Sentinel Initiative).4 This has helped establish research designs and methodologies for balancing comparator treatments across large numbers of observed attributes in the data. As a result, well-designed studies utilizing RWE are beginning to look more and more like trusted RCTs.
Our ability today to link multiple sources of real-world data can help strengthen RWE further. For example, at OptumLabs, we’re combining claims and clinical data. The EMR will tell us when the physician writes a prescription for a medication. But how do we know if the prescription was actually filled? That information is in claims data, not the clinical data. Details about how the patient responded to that medication, however, may only be found in clinical notes. It takes both pieces to see the whole picture, and linking these two sources helps remove biases that would arise from using either type of data alone.
Healthcare databases frequently used for real world evidence generation. Source: Franklin J, Schneeweiss S. When and How Can Real World Data Analyses Substitute for Randomized Controlled Trials? Clinical Pharmacology and Therapeutics 2017.
It’s an observational study — what could possibly go wrong?
There are contradictory findings when it comes to the reliability of RWE. A recent Cochrane Review concluded that observational studies generally have similar findings as RCTs in the same disease states.5 But substantial literature also shows wide variation in results from database studies within the same therapeutic areas.6
There are cases when an observational study and RCT may have very different initial results, but reach similar conclusions once the observational study design is adjusted to match that RCT. For example, the observational Nurses' Health Study showed that hormone replacement therapy (HRT) reduced the risk of heart disease among post-menopausal women.7 A decade later, results from a large RCT, the Women's Health Initiative, found the exact opposite.8
These seemingly different conclusions created huge anxiety among post-menopausal women being treated with HRT, as well as their physicians. However, it turns out that the difference in conclusions was not due to randomization at all. Rather, the risk of heart disease was highest in a two-year window after starting HRT and then declined over time. When the observational Nurses Health data was re-analyzed to control for this, the results were the same as the Women’s Health Initiative RCT.
The bottom line is that sometimes observational studies have been able to replicate the results of RCTs and sometimes not. To increase our confidence in the findings from observational studies, we need to understand why this is the case.
How can we raise confidence in appropriate use of observational studies?
OptumLabs is partnering with the Multiregional Clinical Trials Center at Brigham and Women’s hospital to explore this question. Observational Patient Evidence for Regulatory Science And uNderstanding Disease (OPERAND) is a collaboration of stakeholders from industry, academia and regulation to consider the principles behind, the methodology to draw upon, and the appropriate use of observational data in regulatory review and approval. We can then use these design elements to examine the circumstances under which real-world data — specifically, linked claims and EMR data — can be used to confirm previously published RCTs.
While individual RCTs have been replicated using observational database analyses before, we plan to replicate a large number of trials across several different therapeutic areas simultaneously (a novel feat). Our findings will complement the efforts of groups like Duke Margolis Center working on the FDA regulatory policy guidelines for RWE. Moreover, the ability to trust policy evaluations of benefit design, care organizational models, disease management programs or non-drug medical interventions all rest on the same principles being explored in OPERAND.
There will always be a place for RCTs in areas such as initial drug approvals. But with the vast amounts of high-quality data available to us today, we have a unique opportunity to explore how we can use this data to generate reliable RWE more quickly and at less cost — ultimately benefiting patients by accelerating access to effective treatments.
About the authors:
*William Crown, PhD, is chief scientific officer at OptumLabs
- Rowan C., Flory J., Gerhard T., et al. Agreement and Validity of Electronic Health Record Prescribing Data Relative to Pharmacy Claims Data: A Validation Study From a US Electronic Health Record Database. Pharmacoepidemiol Drug Saf. 2017. https://doi.org/10.1002/pds.4234.
- Anglemyer A, Horvath HT, Bero L Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials (Review). The Cochrane Collaborative: John Wiley & Sons, Ltd., 2014.
- Hernandez-Diaz S., Varas-Lorenzo C., Garcia Rodriquez LA. 2006. Non-steroidal anti-inflammatory drugs and the risk of acute myocardial infarction. Basic Clin Pharmacol Toxicol. 98(3):266-274. Kwok CS, Loke YK. 2010. Meta-analysis: the effects of proton pump inhibitors on cardiovascular events and mortality in patients receiving clopidogrel. Aliment Pharmacol. Ther. 31(8):810-823.
- Stampfer MJ et al. Postmenopausal Estrogen Therapy and Cardiovascular Disease: Ten-Year Follow-Up from the Nurses’ Health Study. N. Engl. J. Med. 325, 756-762 (1991).
- Rossouw JE et al. Risks and Benefits of Estrogen Plus Progestin in Healthy Postmenopausal Women: Principal Results from the Women’s Health Initiative Randomized Controlled Trial. JAMA 288, 321-333 (2002).
To address the opioid crisis, build a comprehensive national frameworkPublished Jan 25 2018, 6:51 PM
By Darshak Sanghavi, MD; Aylin Altan, PhD; Christopher Hane, PhD; Paul Bleicher, MD, PhD, OptumLabs
Among the myriad of challenges around the U.S. opioid epidemic is the lack of consistent, data-driven ways for the health system to measure and respond to it. That’s why OptumLabs collaborated with a panel of national clinical and public health experts to develop a comprehensive framework of 29 claims-based measures that are organized by four opioid-related domains: prevention, pain management, opioid use disorder treatment, and maternal and child health. These metrics have been shared with diverse stakeholders in various forms, and have garnered significant interest for their potential to guide a more holistic approach to evaluating and improving efforts to tackle this public health crisis. We are excited to share them with you! – Editor
This following piece first appeared in the Health Affairs Blog on December 18, 2017.
The annual rate of opioid-related deaths in the United States will surpass the historic peak annual death rates from motor vehicle accidents, HIV infections, and firearms to become the leading cause of death for people less than 50 years of age. The National Vital Statistics System reported roughly 64,000 deaths from drug overdoses in 2016 and year-to-year relative rate increases of more than 20 percent. Responding to this epidemic with such dramatic increases, the Department of Health and Human Services declared the national opioid crisis a public health emergency on October 26, in conjunction with President Donald Trump’s pronouncement on the same day.
The current opioid crisis came after substantial increases in per-capita frequency and dosages of prescription opioids beginning in the late-1990s. Researchers hypothesize the increases were driven in part by efforts to recognize pain as the fifth vital sign as codified by the Joint Commission in 2001, pharmaceutical marketing practices, and lack of recognition of the risks of dependence and addiction with prolonged use. According to the Centers for Disease Control and Prevention (CDC), when averaged nationally, roughly one prescription for opioids is written for every person in the United States annually.
Current gaps and opportunities in quality metrics
The epidemic nature of the opioid crisis and its immense complexity has exposed important gaps in the ability of the health care system to respond in a data-driven manner. Typically, federally-endorsed performance measures are developed to track and address key health care processes or outcomes targeted for improvement using blunt payment incentives, which understandably require a high degree of evidence and testing prior to widespread deployment. The development process is not typically geared to rapid-cycle quality improvement programs, which can be deployed via education, quality improvement, comparison reporting, and value-based payment incentives as has been demonstrated in the private sector. As a result, formal federal measure development has not kept pace with the rapid cadence of the opioid epidemic.
For example, the Pharmacy Quality Alliance developed three opioid misuse measures in 2015 (targeted towards identifying high frequency/dosage prescribers or towards “doctor-shopping” patients) and these measures did not complete the endorsement process of the National Quality Forum (NQF) until 2017. Broad adoption by federal payment programs may take several more years, during which time contributing prescribing trends could continue. Over a year ago, the CDC released guidelines on the appropriate dosing of opioids for pain, and the former U.S. Surgeon General mailed a letter to 2.3 million clinicians on the same topic, but no endorsed quality measure to assist with this recommendation has been made widely available.
The need for a comprehensive quality measurement framework examining the impact of the epidemic from many vantage points has particular timeliness, as the White House opioid commission recently recommended that the nation "invest only in those programs that achieve quantifiable goals and metrics."OptumLabs, a collaborative research and innovation center, embarked on a five-month program to develop a comprehensive framework of 29 claims-based measures for the opioid crisis. The measure set for this program benefitted from the input of a panel of national clinical and public health experts, including representatives from the CDC and the Substance Abuse and Mental Health Services Administration. Four key domains for the metrics were identified: prevention, appropriate acute and chronic pain treatment, Opioid Use Disorder (OUD) treatment, and maternal/child health. Where possible, measures were abstracted from publically available specifications which were refined as needed by expert coding teams. The measures were calculated using the OptumLabs Data Warehouse, which includes de-identified integrated pharmacy, medical claims, and enrollment data from a geographically diverse population of approximately 150 million United States residents currently or previously enrolled in commercial and Medicare Advantage programs. The measures are described in Exhibit 1 and technical specifications are described in a supplemental index.
Exhibit 1: Comprehensive opioid use quality measure framework and annual trends, 2016
# Measure 2016 Enrollees meeting inclusion/exclusion criteriaa 5,568,625 Prevention Primary outcome measures 1. New opioid fillers per 1000 enrollees 122 2. Initial opioid prescription compliant with CDC recommendations (composite)b 55.4% 3. New opioid fillers who avoid chronic use 97.9% 4. Prevalence of opioid overdose (OD) per 100,000 person-years 35.9 Secondary outcome measures 5. Initial opioid prescription is prescribed while patient is not exposed to benzodiazepines (component of primary measure #2) 91.1% 6. Initial prescription is not for methadone (component of primary measure #2) 100% 7. Initial opioid prescription is for short acting formulation (component of primary measure #2) 99.6% 8. Initial prescription is for <50MME/day (component of primary measure #2) 77.2% 9. Initial opioid prescription is for <=7 days supply (component of primary measure #2) 79.7% 10. No use of opioids for new low back pain patients 87.1% 11. No concurrent opioid and benzodiazepine use 78.0% 12. Appropriate contact with provider before second opioid prescription 54.0% Pain management Primary outcome measures 13. Chronic pain treatment with opioids is optimally managed (composite)c 9.4% 14. Avoidance of breakthrough post-surgical pain leading to ED visit and new opioid presription 95.3% Secondary outcome measures 15. Appropriate contact with provider among chronic opioid users (component of primary measure #13) 95.1% 16. No ED visit for breakthrough pain among chronic opioid users (component of primary measure #13) 85.3% 17. Evidence of non-opioid pharmacological treatment for pain among chronic opioid users (component of primary measure #13) 45.9% 18. Evidence of non-pharmacological therapy for pain among chronic opioid users (component of primary measure #13) 23.8% Opioid use disorder (OUD) treatment Primary outcome measures 19. Evidence of medication-assisted treatment (MAT) among patients with opioid use disorder (OUD) or OD 27.8% 20. Prevalence of OUD per 1000 person-years 8.0 Secondary outcome measures 21. Evidence of MAT following OD 10.8% 22. Evidence of naloxone fill among patients with OUD or OD 0.7% 23. No opioid prescription following any OUD or OD diagnosis 41.0% Maternal, infant & child health Primary outcome measures 24. Percentage of infants with NAS born to mothers on MAT 20.6% 25. Initial opioid prescription compliant with CDC recommendations for patients under 18y age (composite) 68.6% 26. Prevalence of OD per 100,000 person-years under 18y age 7.2 Secondary outcome measures 27. Cases per 1,000 live births of infants born with neonatal abstinence syndrome (NAS) 1.2 28. New opioid filler per 1,000 enrollees under 18y age 36 29. Prevalence of OUD per 1,000 person-years under 18y age 0.21
Source: OptumLabs Data Warehouse. Note: aIncludes commercial and Medicare Advantage health plan enrollees with two years of continuous enrollment in medical and pharmacy coverage, no evidence of active cancer treatments, and not in long-term or palliative care. Denominators vary by measure and are noted for composite measures. bNumber of new opioid users in 2016: 750,594. cNumber of chronic opioid users in 2016: 311,870.
Results of comprehensive framework development
Related to the prevention rubric of Exhibit 1, data from 2016 shows that the rate of compliance with CDC-recommended prescribing for a first opioid fill was approximately 55% and the rates of new opioid fills was 122 per 1,000 enrollees. To better explore the distribution, the measures were computed at the county level and found to be nearly normally distributed (See Exhibit 2), with substantial and meaningful variation, suggesting that local factors may strongly impact prescribing volume and guideline-adherence. (Similar variation was seen among several measures; data not shown.)
Exhibit 2: Distribution of initial opioid prescriptions in compliance with CDC recommendations and rates of new prescriptions at the county level, 2016
Source: OptumLabs Data Warehouse.
In 2016, the year the CDC issued guidance for prescribing, there was striking geographic variation (See Exhibit 3) in appropriate prescribing with further variation in the specific component of guideline non-adherence. In some areas physicians tended to prescribe high doses for new prescriptions, while in other areas, they prescribed for longer durations suggesting the need for tailored interventions based on location. For example, in Massachusetts, there was overall average performance in the composite measure of CDC-compliant prescribing when compared with national benchmarks, and the opportunity for improvement appears to relate to excessive opioid dosage, rather than prescription duration (See Exhibit 4).
Exhibit 3: County-based performance variation in selected opioid use quality measures, 2016
Source: OptumLabs Data Warehouse. Note: Blue indicates better performance. Grey areas indicate counties with insufficient data to calculate measures. Histograms depict performance on x-axis, and number of counties on y-axis.
Exhibit 4: County-based performance variation in selected opioid use quality measures, Massachusetts, 2016
Source: OptumLabs Data Warehouse.
Encouragingly, in the pain rubric of Exhibit 1, post-operative pain appeared to be well-managed with 95 percent avoiding emergency department visits resulting in receipt of new opioids. Our claims based measures related to patients on chronic opioids showed moderate utilization of non-opioid drug treatment by these patients with about 45 percent receiving this treatment, and low use of non-drug treatments (roughly one in four patients). It should be noted that patients may be accessing some of these therapies outside the benefits of their current health insurance plans.
In the treatment rubric of Exhibit 1, rates of medication-assisted treatment (MAT) for OUD were approximately 28%, with a particular opportunity in those following overdose where rates of MAT were 11%. This is consistent with reported national trends. Additional data from our analysis suggests the relative prevalence of OUD increased 50 percent over the 2014-2016 time period, potentially due to both higher incidence and improved coding with ICD-10 adoption.
Moving forward with comprehensive framework
While traditionally developed performance metrics are essential, their value in managing acute crises is limited by an extended ratification process, a focus on one measure at a time, and delayed provider feedback. By offering a comprehensive diagnostic snapshot with benchmarked data on populations that can be attributed based on patients’ geography, payer, or provider, “just in time” comprehensive quality frameworks can enable public health agencies, integrated health care providers, and/or health plans to more rapidly benchmark their status and deploy and evaluate the impact of interventions.
Within our larger organization, for example, initiatives for safer prescribing via pharmacy benefit management, access to MAT via expanded provider networks, clinical and consumer education programs, and dozens of other programs are being pursued in various populations and geographic regions, and such a framework could help assess outcomes over time. To take one example, first-fill drug utilization rules were placed in effect in July 2017 by several hundred clients served by our organization, and within two months of implementation, initial review found relative reductions of 82 percent in excessive dosage and 65 percent reduction in excessive duration of new opioid prescriptions relative to CDC opioid best practice prescribing guidelines among this cohort. In this manner, “just in time” frameworks may permit a precision-medicine approach to the opioid epidemic based on a common set of data-driven metrics.
About the authors:
*Darshak Sanghavi, MD, is chief medical officer at OptumLabs
*Aylin Altan, PhD, is senior vice president of research at OptumLabs
*Christopher Hane, PhD, is vice president of data science at OptumLabs
*Paul Bleicher, MD, PhD, is chief executive officer at OptumLabs
Risk in perspective: A rare heart surgery infection explained with dataPublished Jan 08 2018, 11:59 AM
Darshak Sanghavi, MD; Samantha Noderer, MA
Rondi remembers the day she got the letter in the mail from her hospital in central Massachusetts. It was addressed to her, but was about her teenaged son, Cole, who was born with a heart defect and underwent cardiac surgery a few months earlier. Like Rondi, thousands of other patients and families across the country were opening similar letters from their doctors in the fall of 2015.
We are notifying patients who have had open-heart surgery, about a potential infection risk related to this surgery. We are contacting you today, as you or a member of your family have been identified in clinical records as a patient who might be affected...
Rondi had many questions.
“It was definitely nerve-racking,” said Rondi. “I was glad to know about the issue but my biggest concern was: How bad is it? Is this letter telling me everything?”
Rondi was just one of thousands of people who received this letter because of a spike in reported infections connected to a device used in heart surgeries. But should Rondi have been concerned? Was there a real imminent threat to her son’s health?
In this blog post, we will look at how data can provide important context on health risks, assisting us in determining when and how to communicate to patients.
A rare bacterial infection sourced from a commonly used device
The risk of infection was linked to bacteria that contaminated heater-cooler devices used during open chest surgeries. According to tests, contamination may have occurred during manufacturing of the equipment. More than 250,000 heart bypass procedures are performed each year in the U.S. with the help of these heater-cooler devices that regulate body temperatures. This could have been a major public health crisis.
The suspect was a type of bacteria known as nontuberculous mycobacterium (NTM). While most people exposed to these bacteria never get an infection, a spike in reports of infections in patients linked to contaminated heater-cooler devices concerned public health officials at the Centers for Disease Control and Prevention (CDC). They asked providers to inform patients of the infection risk, which resulted in the letter Rondi received.
“The letter explained that the signs of an infection could take several months or years to show. And the list of potential symptoms was very broad, such as night sweats, muscle aches, weight loss, fatigue and unexplained fever,” said Rondi. “I was most nervous to tell Cole about the letter without more information. It wasn’t until we spoke with our cardiologist and he clearly explained the small risk and how it relates to my son specifically, that we felt more at ease.”
Based on a few published studies, the CDC estimated that the risk of a patient getting an infection was between about 1 in 100 and 1 in 1,000. This is a large range, which can be stressful for patients when the risks are not put into proper perspective.
We asked ourselves whether it was possible to find a more precise estimate of risk. Knowing the precise risk could better inform public health communications and keep families like Rondi’s at ease in the event of future outbreaks.
To answer this question, OptumLabs queried our data set of commercial and Medicare Advantage claims for more than 127 million people over 20 years.
Demonstrating real-world risks with real-world data
We explored the risk of mycobacterial infection among a group of patients who had claims for open heart bypass surgery between July 1, 2007 and June 30, 2015 and compared it to the risk of infection among patients who had claims for angioplasty — a non-open heart cardiac procedure that does not involve a heater-cooler device — during the same time period. Both groups of patients had very similar health conditions. Because the only major difference between them was the type of surgery they had, we were able to isolate the impact of the heater-cooler device used in open heart surgery. Infection was defined by looking at ICD-9 diagnosis codes for treatment with rifabutin, a common antibiotic used to fight the infection.
Looking at patients enrolled in the health plan for four years in a row, the small rate of infection among patients who had bypass surgery with a heater-cooler device was not statistically higher than the rate of infection among patients who had angioplasty without a heater-cooler device. In short, it appears that the actual risks to patients were quite low.
This initial analysis isn't definitive by any means, but shows how health care data can help point us in the right direction to guide patients, and support doctors when communicating the risks of one treatment or procedure over another to patients.
Looking at these results, should a letter have been sent to Rondi? We would argue that the letter should have been written with more precision, informed by data, and ready to answer patients’ second and third questions. Ideally, patient communications should provide relevant information that can help them put complicated risks into perspective. It can reduce confusion and prevent unnecessary worry.
When it comes to our health or the health of a loved one, it’s often the questions left unanswered that can cause more distress than even the worst news. With the help of data, we can work to get the right answers to the right people to guide important real world decisions and positive outcomes.
About the authors:
*Darshak Sanghavi, MD, is chief medical officer at OptumLabs
*Samantha Noderer, MA, is communications & translation manager at OptumLabs
Making diabetes care personal with the right dataPublished Dec 08 2017, 4:20 PM
By Darshak Sanghavi, MD, and Samantha Noderer, MA, OptumLabs
It isn’t easy being the pancreas.
Nobody understands this better than somebody with diabetes — a chronic disease impacting more than 30 million people in the United States, according to the Centers for Disease Control and Prevention. A healthy pancreas constantly checks the body’s blood glucose (sugar) levels and adjusts them from rising too high by releasing tiny bursts of insulin. In people with type 2 diabetes, the body resists that insulin and in time the pancreas cannot keep up with the demand. When glucose in the blood becomes dangerously high, it can cause damage to vital organs like the kidneys, heart, eyes, and brain over many years. That’s a lot of pressure!
When the pancreas struggles, doctors can step in to monitor a patient’s average glucose levels through a blood test called hemoglobin A1C (HbA1c). For most people with controlled type 2 diabetes, it’s recommended they’re tested 1–2 times per year.
But it turns out there’s a lot of variation when it comes to how often patients are getting their A1C tested. Some are tested more frequently than necessary. Others are not tested enough. OptumLabs® and our partners are leveraging data to help us to gain insights on how this may impact a patient’s health, and how to better manage their diabetes.
Your zip code could impact how often you’re tested for diabetes
This map from Dartmouth researchers shows the striking variations in the proportion of Medicare beneficiaries getting the recommended yearly A1C testing.
There are large areas of the country where roughly 1 in 4 patients don’t get their A1C monitored once a year. If left untreated, poor blood glucose control can cause serious complications down the line, like kidney failure or blindness.
Understanding the variation in appropriate blood glucose level testing has led government health care programs and others to emphasize proper monitoring and comprehensive diabetes care through special measures. Today, insurance companies are graded — and rewarded — by Medicare on their performance with several diabetes measures. One such measure is keeping the A1C level for patients with type 2 diabetes under 8 percent. Low A1C levels mean the average blood sugar isn't too high — which is a good thing to a certain extent.
Guidelines may improve care, but sometimes there are unintended consequences
Higher quality care is everybody’s goal and driving accountability for better quality makes sense to most people. But what happens when providers focus on following these guidelines to test at least once a year and keep A1C levels low? Is it possible that a well-intentioned guideline could have some unanticipated side effects? Endocrinologist Dr. Rozalina McCoy, MD, and her research team at Mayo Clinic (OptumLabs’ co-founding partner) recently investigated this question and found some surprising trends.
Using OptumLabs data, the Mayo team found1 that patients with controlled type 2 diabetes were being tested much more frequently than 1–2 times per year. More than half of patients were getting their A1C checked 3–4 times per year, and 6 percent of patients were getting 5 or more tests per year.
When doctors test more often, they have more information about a patient’s glucose levels. This information could lead to better care, but there’s also potential this may do more harm than good.
In a follow-up study, the Mayo team discovered2 that more than 1 in 5 patients with controlled type 2 diabetes were likely being over treated with glucose-lowering drugs, almost doubling their risk of dangerously low blood sugar episodes (“hypoglycemia”). These are severe episodes that can land patients in an emergency room or hospital.
Making medical care better is a learning process. When a problem comes to light, like inconsistent A1C testing, the system responds to correct the problem by creating measures and incentives. However, unexpected side effects can emerge like over-testing that leads to over-treatment. What should we do to fix this unintended consequence?
Learning from research to improve quality measures
Upon reflection, the root of this issue is that the diabetes care measure only rewards lowering A1C levels. In collaboration with our founding consumer advocate partner AARP, OptumLabs established a new research project with Mayo Clinic to create a more nuanced performance measure that takes a “Goldilocks approach” to diabetes management.
Rather than just shooting for a low A1C, a new measure will seek to reward being “in the sweet spot” of “just right” care, so the A1C should be neither too high nor too low.
Patient’s A1C level
Glucose-lowering drug treatment
(red is unsafe, green is safe)
Too low Any medication No medication Safe range Target regimens depend on clinical complexity, number and type of glucose-lowering medications prescribed, and other patient risk factors. Too high ≤ 1 medication High-intensity medication treatment
The actual measure has multiple subcategories to address the nuanced approach required to tailor treatment for patients in different Hba1C ranges with unique risk profiles.
Mayo Clinic partners will prototype the measure using millions of de-identified patient records available from OptumLabs, and if successful, work to share this knowledge more widely with nationally recognized health organizations such as the National Quality Forum, which OptumLabs has collaborated with3 in the past.
This is the blessing and the curse in the age of big data: Doctors can get a lot of helpful information to guide treatment, but the data flow can also be overwhelming. A key challenge for researchers, like those at OptumLabs, is shaping how to use the right signals to improve and not complicate treatment.
It certainly isn't easy being the pancreas, which continuously evaluates and modifies its testing and output to manage someone's blood sugar. No one can reproduce its working perfectly. But the overall example it sets — always observing, testing, responding, correcting and having a big impact on the overall system — is one that OptumLabs and our partners strive to emulate.About the Authors
*Darshak Sanghavi, MD, is chief medical officer at OptumLabs
*Samantha Noderer, MA, is communications & translation manager at OptumLabs
- BMJ. HbA1c overtesting and overtreatment among US adults with controlled type 2 diabetes, 2001-13: observational population based study. Published Dec. 8, 2015. Accessed Dec. 5, 2017.
- JAMA. Intensive Treatment and Severe Hypoglycemia Among Adults With Type 2 Diabetes. Published July 2016. Accessed Dec. 5, 2017.
- National Quality Forum. NQF Launches Measure Incubator. Accessed Dec. 5, 2017.
Creating impact: Partnering to answer big questions with health care dataPublished Nov 03 2017, 11:01 AM
By Paul Bleicher, MD, PhD; William Crown, PhD; and Darshak Sanghavi, MD
Welcome to the OptumLabs® blog. Here, we will take a regular look at some of the most complicated issues in health care, and explain how we’re addressing them through collaborative research and innovation.
We'll make it accessible. We'll make it interesting. The goal is to bring you into the world of health care data with engaging but scientifically rigorous stories that make an impact on the health care system and on the health of people.
But first, what does it take to create “impact” in health care using health care data? Impact is one of the key goals of OptumLabs. We think a lot about how to create impact and how to measure it.
Sometimes, when you have one of the largest databases in health care, it is tempting to study a problem or do an analysis because the results will be “interesting.” But answering “interesting” questions alone doesn’t create a positive impact on people’s lives.
Creating impact by solving some of health care’s biggest problems with complex health data depends on a number of factors. First, you need the data and the sound science to use it. Next, you need the right expertise in health care to conceive and implement solutions once you generate important findings. And, often it takes working together with world-class organizations that have the resources and skills to implement these solutions.
With access to health care’s richest data set of more than 200 million de-identified lives, data and analysis — with a focus on health system improvement — resides at the center of how we solve the biggest problems in health care.
OptumLabs staff and partners represent the world’s experts in health care. With our diverse group of partners from Mayo Clinic, AARP, AMGA, University of California Health to many others in the private and public sectors, we work collaboratively with the help of the OptumLabs Data Warehouse — our industry-leading data asset containing de-identified, linked administrative claims, medical records, and patient self-reported health information.
Applying some of the newest artificial intelligence techniques, we look at long-term trends to better understand the health experience of individuals and its impact on the industry by pairing claims data with unique details on patient and health plan costs, demographics, health behaviors and more.
In this blog, we'll show how that happens. Every couple of weeks, OptumLabs experts and partners will touch on some of the biggest health care problems today; like how to track and understand the many dimensions of the opioid crisis, whether Alzheimer's disease and dementias can be detected earlier, how to guard against harming patients with diabetes while trying to control their blood sugars, and much more.
Join us on our journey to improve health and health care. Follow us on Twitter (@OptumLabs) for new post alerts.
About the authors:
*Paul Bleicher, MD, PhD, is the chief executive officer at OptumLabs
*William Crown, PhD, is the chief scientific officer at OptumLabs
*Darshak Sanghavi, MD, is the chief medical officer at OptumLabs
Uncovering hidden patterns in dementia that might save livesPublished Nov 03 2017, 11:01 AM
By Darshak Sanghavi, MD and Samantha Noderer, MA
Decades ago, doctors had to rely on personal experience and luck to make discoveries.
For example, around World War II, a Dutch pediatrician noticed that some of his patients, who were malnourished despite being well-fed, suddenly improved when a grain shortage hit their community.1 The doctor unknowingly stumbled on celiac disease. Once he realized that gluten in wheat had caused the illnesses, he was able to effectively treat his patients.
Today, instead of relying on serendipity to understand mysterious conditions, we have the potential to uncover hidden trends in the massive amounts of data created by electronic medical notes and records.
One way OptumLabs® is trying to do this is through a collaborative program with the Global CEO Initiative on Alzheimer’s Disease.2 OptumLabs and several research and expert partners are exploring the roots of Alzheimer’s disease and other dementias.
But instead of relying on chance, OptumLabs and our partners are using advanced data science techniques to organize vast information for research and visualize new, meaningful patterns in dementia.
Paper trails to computer processing
Over the past two decades, electronic health records (EHRs) have transformed health care by recording information digitally. Gone are the often illegible handwritten notes that made it impossible to measure or monitor trends across hundreds or thousands of patients. Digital records now make better presentation and interpretation of large amounts of data possible.
While we’re now able to understand what the notes say, we’re still challenged in seeing trends across large populations. That’s why we need a way to take these unstructured, narrative texts in electronic notes, and create an easier-to-analyze spreadsheet. That's where “natural language processing” comes in.
Natural language processing (NLP) uses a combination of linguistic, statistical and exploratory methods to analyze text via computer programs and organize it for research.3 Here’s an example of how thousands of charts turn into spreadsheets via NLP.
In the OptumLabs clinical data, NLP-derived phrases are separated into tables based on their content. The Signs, Diseases and Symptoms (SDS) table, for example, filters to a medical concept that relates to the patient and provides details such as the location on the patient’s body, features such as severity, whether it’s confirmed or denied, and other notes.
Here we can see the NLP table filtered to fall risks. Location does not apply here.
SDS_TERM SDS_LOCATION SDS_ATTRIBUTE SDS_SENTIMENT NOTE_SECTION fall risk (null) Altered (null) psychosocial fall risk (null) (null) negative psychosocial fall risk (null) (null) negative (null) fall risk (null) Moderate (null) (null) fall risk (null) (null) negative social history
In short, NLP allows researchers to go from illegible handwritten notes to a structured spreadsheet of consistent data that we can review in a systematic way.
It’s still difficult to make discoveries across a massive table with thousands or millions of rows of data. However, there are helpful techniques that build upon NLP, allowing us to visualize big data and help find patterns.
Mapping patient journeys through clinical note visualizations
The visual communication of data is an invaluable learning tool. It can illustrate facts and relationships in context, revealing a higher level of understanding than text alone. OptumLabs is incorporating this approach in a variety of projects underway.
We are using a combination of NLP and data visualization to find hidden clues in medical notes that people were developing signs of dementia before they were diagnosed. This is important because there may be measures that doctors can take to prevent or delay this progressive disease if they had warning.
To start, we used NLP to “read” clinical notes in our de-identified EHR database from more than 40 U.S. provider practice groups or independent delivery networks that serve more than 50 million people.
We looked back at the medical history of patients with dementia and the related terms that showed up one to four years earlier in their medical records.
To visualize the changes over time as patients move closer to their date of diagnosis, we organized the data in an “alluvial flow” diagram. This diagram style gets its name from nature’s alluvial fans that form as sediments carried from a point of flowing water build up over time.4
Figure. Alluvial flow diagram of dementia-related signs and symptoms mentioned in clinical notes three years prior to an Alzheimer’s disease or dementia diagnosis. Source: OptumLabs EHR Clinical Notes Data via NLP.
Arranged from largest to smallest, the size of the black bars at each time stamp represents the number of patients with each of these issues mentioned in their chart. The colored flows represent how many patients transition from one state to the next.
This visualization helps us understand the early signals that would be useful in future projects focused on prediction, prevention, and treatment of Alzheimer’s disease and related dementias.
We can use this same technique to explore patient journeys in other disease areas and use many types of data such as administrative claims data, longitudinal survey data, or disease registry information.
As we move from big data to bigger data in health care, OptumLabs continues to explore data visualization opportunities that uncover important patterns that would otherwise go unnoticed. In turn, these patterns just might lead to discoveries that save lives.About the authors:
*Darshak Sanghavi, MD, is chief medical officer at OptumLabs
*Samantha Noderer, MA, is communications & translation manager at OptumLabs
- Slate. Why Do So Many People Think They Need Gluten-Free Foods? Published Feb. 26, 2013. Accessed Oct. 16, 2017.
- Global CEO Initiative on Alzheimer’s Disease
- Nature Reviews Genetics. Mining electronic health records: towards better research applications and clinical care. Published May 2, 2012. Accessed Oct. 16, 2017.
- National Geographic Society. Alluvial fans. Accessed Oct. 16, 2017.