 Price markups are a major cause of healthcare inflation and financial harm to patients, especially those who are self-paying or covered by commercial insurance.

 Retrospective analysis of publicly-available information on Medicare physician payments, representing 100% of Part B services provided to fee-for-service beneficiaries during calendar year 2014.

 Outcomes were markup ratios for oncology services, defined as the ratio of submitted charges to the amount reimbursed by Medicare. For example, the overall cost-to-charge ratio for all Medicare-reimbursed services in 2013 was 3.4, or a 240% charge markup.

 Our analysis included oncology services provided by 3248 hospitals in all 50 states. There was significant variation in markup ratios by hospital across oncology specialty: radiology (median = 3.7; interquartile range [IQR], 3.1-4.5), hematology/oncology (median = 2.3; IQR, 1.8-2.9), medical oncology (median = 2.4; IQR, 1.8-3.0), pathology (median = 4.1; IQR 3.1-5.1), and radiation oncology (median = 3.6; IQR, 2.9-4.5). Higher markups were associated with for-profit status for medical oncology services (coefficient, 0.29; 95% CI, 0.12-0.45) and prestige status for radiology (0.53; 95% CI, 0.15-0.92) and pathology (0.65; 95% CI, 0.20-1.09) services.

 High markups exist for oncology services, and further legislation is needed to protect patients from highly variable pricing and to address disparities in access to high-quality cancer care.

Disparities in access to high-quality cancer care and rising treatment costs continue to worsen the problem of patient out-of-pocket expenses for cancer care.

There is significant variation in markup of services by hospital across oncology specialties (radiology, hematology/oncology, medical oncology, pathology, and radiation oncology).

Higher markups were associated with for-profit status for medical oncology services and prestige status for radiology and pathology services.

These findings support further efforts to protect uninsured and out-of-network patients from highly variable pricing.

Rising treatment costs, very high health insurance deductibles, and narrowing insurance networks have magnified the problem of patient out-of-pocket expenses for cancer care.

 As many as 8% of all patients will seek care from an out-of-network specialist and may be held responsible for chargemaster, or “sticker,” prices.

 This occurs when the patient does not realize they are seeing an out-of-network physician (“surprise medical bill”) or when patients deliberately choose out-of-network facilities, which is common in oncology. Charges for the same service can vary widely across hospitals and create significant financial burdens on patients with cancer.

 To better understand the financial hardships that patients can face, we designed a study to characterize the variation in what hospitals charge for outpatient oncology services.

We assessed price markup variation by hospital and by oncology specialty using 2014 Medicare Part B physician reimbursement data from CMS.

 Claims were linked by National Physician Identifiers to the Physician Compare database to identify each physician’s primary hospital affiliation. We obtained hospital characteristics from the American Hospital Association database, including size, for-profit status, location, and academic status, and defined prestigious hospitals as those listed among the 2014 

s Hospital Honor Roll. The markup ratio was defined as the charge billed divided by the Medicare allowable amount, as in previous studies.

 Thus, a markup ratio of 3.5 means that for every $100 that Medicare pays, the hospital charged $350, or $250 in excess charges. We used multivariable linear regression to study hospital characteristics associated with higher markups. This study was exempted by the Johns Hopkins Institutional Review Board as not being human subjects research.

Of the 3248 hospitals from all 50 states identified in our analysis, 60% had fewer than 200 beds, 19% were for-profit, 37% were academic, and 2% had prestige status. We found significant variation in markup ratios by hospital across oncology specialty: radiology (median = 3.7; interquartile range [IQR], 3.1-4.5), hematology/oncology (median = 2.3; IQR, 1.8-2.9), medical oncology (median = 2.4; IQR, 1.8-3.0), pathology (median = 4.1; IQR, 3.1-5.1), and radiation oncology (median = 3.6; IQR, 2.9-4.5) (

). Higher markups were associated with for-profit status for medical oncology services (coefficient, 0.29; 95% CI, 0.12-0.45) and prestige status for radiology (0.53; 95% CI, 0.15-0.92) and pathology (0.65; 95% CI, 0.20-1.09) services.

Our findings contribute to emerging evidence that prestigious hospitals or large hospital chains use higher chargemaster pricing to “anchor” negotiations and gain higher reimbursement from insurers.

 In this way, price markups contribute to the inflation of medical costs in the healthcare system and can affect patients’ treatment decisions as their out-of-pocket costs increase.

 Patients who go out-of-network can be burdened by onerous collection processes that demand payment for more than what the hospital would receive from an in-network patient.

 This difference can be dramatic, representing a critical health disparity in cancer care. Moreover, patients are rarely able to select their radiologist or pathologist, which contributes to the phenomenon of surprise medical bills. On a moral level, we believe that it is unethical for a nonprofit medical center to put a patient with cancer into household bankruptcy because they cannot pay a bill inflated above what Medicare would pay for the identical service.

The recent trend toward narrower insurance networks increases the chance that patients will face high out-of-pocket costs from seeing an out-of-network specialist. One analysis of new federal Marketplace plans under the Affordable Care Act showed that 15% lacked in-network physicians altogether in at least 1 specialty.

 New patient protections are now emerging in several states to address this problem, with New York and other states passing legislation requiring that hospitals negotiate directly with insurers rather than bill patients chargemaster prices.

 Further legislation like that in New York may protect patients from this highly variable pricing and address disparities in access to high-quality cancer care.

Johns Hopkins Department of Surgery (AP, JT, MAM), Baltimore, MD; Johns Hopkins School of Medicine (TX, MP), Baltimore, MD.

 This study was supported by the Alpha Omega Alpha Carolyn L. Kuckein Student Research Fellowship. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

 An oral abstract has been presented at Medical Quality 2016 (March 30-April 2, 2016), hosted by the American College of Medical Quality.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (TX, MP, JT, MAM); acquisition of data (AP, TX); analysis and interpretation of data (AP, TX, MP, JT); drafting of the manuscript (AP, TX); critical revision of the manuscript for important intellectual content (AP, TX, MP, JT, MAM); statistical analysis (AP, TX); obtaining funding (TX); and supervision (MAM).

 Martin A. Makary, MD, MPH, Johns Hopkins Department of Surgery, Halsted 610, 600 N Wolfe St, Baltimore, MD 21287. Email: mmakary1@jhmi.edu.

1. The costs of cancer: addressing patient costs. American Cancer Society Cancer Action Network website. acscan.org/sites/default/files/Costs%20of%20Cancer%20-%20Final%20Web.pdf. Published April 2017. Accessed May 22, 2017.

2. Xu T, Park A, Bai G, et al. Variation in emergency department vs internal medicine excess charges in the United States. 

 2017;177(8):1139-1145. doi: 10.1001/jamainternmed.2017.1598.

3. White C, Reschovsky JD, Bond AM. Understanding differences between high- and low-price hospitals: implications for efforts to rein in costs. 

. 2014;33(2):324-331. doi: 10.1377/hlthaff.2013.0747.

4. Batty M, Ippolito B. Mystery of the chargemaster: examining the role of hospital list prices in what patients actually pay. 

. 2017;36(4):689-696. doi: 10.1377/hlthaff.2016.0986.

5. Szabo L. As drug costs soar, people delay or skip cancer treatments. 

. March 15, 2017. npr.org/sections/health-shots/2017/03/15/520110742/as-drug-costs-soar-people-delay-or-skip-cancer-treatments. Accessed September 25, 2017.

6. Dorner SC, Jacobs DB, Sommers BD. Adequacy of outpatient specialty care access in marketplace plans under the Affordable Care Act. 

. 2015;314(16):1749-1750. doi: 10.1001/jama.2015.9375.&ensp;

Articles

WEB EXCLUSIVE

Charges for oncology services vary widely across hospitals and impose financial burdens. Further legislation is needed to address disparities in access to high-quality cancer care.

Variation in Markups on Outpatient Oncology Services in the United States

 The majority of studies on medication therapy management (MTM) have focused on the conduct of in-person comprehensive medication reviews (CMRs). Although face-to-face MTM services may be the most well-known mode of delivery, there are other ways CMRs can be implemented as part of an MTM service. Specifically, telephonic CMR is an alternative method of offering MTM services that many patients may welcome. The purpose of this commentary was to highlight current findings regarding the impact of telephonic CMRs as a component of MTM services on health outcomes.

 Review of available literature analyzing the impact of telephonic CMRs on health outcomes.

 PubMed was used to identify studies published between January 1980 and December 2016 using the search terms 

. This search was limited to publications in English. Case reports were excluded.

 CMRs conducted telephonically as part of an MTM program may potentially reduce healthcare expenditures, hospitalizations, and mortality.

 Although study results have shown telephonic CMRs to be an effective way to enhance patient care and reduce healthcare expenditures, the components of telephonic MTM programs that make them most effective and the population on which they have the greatest impact are unknown. Moving forward, further evaluation of these programs is necessary to determine which specific factors, such as access to patient electronic health records, patient follow-up frequency, and the use of collaborative practice agreements, are most vital to improving patient outcomes.

Medication therapy management (MTM) services have been implemented with the goal of enhancing patient outcomes through optimization of drug therapy and empowerment of patients to improve their health. Comprehensive medication reviews (CMRs), the principal component of MTM services, can be delivered face-to-face or telephonically.

Telephonic delivery of CMRs expands the reach of MTM services.

Study results have shown telephonic CMRs to be an effective way to enhance patient care and reduce healthcare expenditures.

Life expectancy has continued to rise over the past decades. It is estimated that the population of individuals in the United States older than 65 years will reach 83.7 million by 2050, twice this population in 2012.

 Polypharmacy and chronic health conditions increase with age, prompting greater emphasis on safe and effective use of medications in the older adult population. This is one reason that CMS requires Part D sponsors to provide medication therapy management (MTM) programs to their beneficiaries.

 The goal of MTM programs is to improve patient outcomes through drug therapy optimization, a reduction in adverse drug events, and patient empowerment.

Most MTM services are delivered either face-to-face or telephonically. The telephonic mode of delivery offers distinct advantages. First, a telephonic MTM session is easily accessible to patients. This allows patients in rural areas, those who are homebound, and those without transportation to receive the benefits of MTM services. Furthermore, telephonic MTM programs have the capacity to employ a team of pharmacists who can accommodate the needs of patients who speak English as a second language and are unable to find an MTM provider who speaks their language in a local setting. Another unique asset of telephonic MTM delivery is that patients can reach into their medicine cabinet and describe their prescriptions,  OTC products, and supplements. During face-to-face sessions, pharmacists depend on patients bringing in their medications or recalling them from memory. Communication via telephone may also be more comfortable for certain patients, allowing them to broach personal questions or topics.

Telephonic delivery of MTM services, however, has certain challenges. The first barrier that must be overcome is establishing trust with patients. Some patients may be skeptical of the service or unwilling to discuss their medications over the phone. Another challenge is the reliance on verbal cues. Nonverbal cues can be useful to assess patient comprehension and well-being.

Telephonic MTM may not be ideal for every patient, but it is a welcome alternative for many. Although face-to-face MTM has been relatively well studied, its impact on patient outcomes is unclear.

 Limited data are available regarding the effect of telephonic delivery on patient outcomes. The purpose of this commentary is to highlight current findings regarding the impact of telephonic comprehensive medication reviews (CMRs) as a component of MTM services on health outcomes.

The literature was obtained through PubMed from January 1980 to December 2016, using the terms 

. Case reports were excluded. The search was limited to publications in English. A CMR was defined as a comprehensive review of all medications being taken by patients to assess their appropriateness and targeted medication review as an assessment of appropriateness of a specific medication (eg, statins in patients with diabetes). The included studies are summarized in the 

The impact of a telephonic MTM program on the health outcomes of home-based Medicare beneficiaries was explored in a prospective nonrandomized study.

 Patients who opted into the MTM program and received a telephonic CMR were compared with a control group of those who opted out of the program and did not receive a telephonic CMR. The primary purpose of this study was to determine the impact of a telephonic MTM program on 180-day mortality as well as on inpatient hospitalizations, emergency department (ED) visits, and the cost of Medicare Part D covered drugs. Beneficiaries receiving MTM services were 50% less likely to die in the 180 days after the intervention compared with controls. However, they also were more likely to experience increases in both inpatient hospitalization and medication costs. There was no difference in ED visits between the 2 groups.

It should be noted that this study was not adequately powered to show a difference in inpatient hospitalizations, costs, or ED outcomes. Also, the study design created a potential for participation bias. Those who opted to receive the MTM service may have held their health in higher regard than ones who declined. Further, the pharmacists conducting MTM services had access to an electronic health record (EHR), which may not be available in other programs. Nonetheless, the telephonic MTM service demonstrated a significant decline in mortality.

 determined the impact of a telephonic CMR on mortality over 1 year. This was assessed retrospectively in a large integrated health plan.

 Medicare beneficiaries who received a telephonic CMR during 2006 to 2010 were matched to a control group of Medicare beneficiaries who were not eligible for MTM services and did not receive a CMR. In addition to the primary outcome of 365-day all-cause mortality, secondary outcomes included percentages of hospitalizations and ED visits and drug costs per day for the 365 days following intervention. Compared with the control group, there was a 14% reduction in the risk of death, a 3% reduction in the risk of hospitalization, and no difference in the change in median daily drug costs for beneficiaries who received the MTM services.

It is important to note that MTM pharmacists in this study had access to EHRs and were working under collaborative practice agreements, which may have provided them the opportunity to have a larger impact on the patients than in other telephonic MTM programs. With that considered, this study demonstrated positive effects of a telephonic MTM program on important health outcomes.

A relevant question is: Which groups of patients may receive the most benefit from telephonic MTM services? Zillich et al

 evaluated the impact of telephonic MTM on hospitalization in Medicare patients newly admitted to home health care centers in a prospective randomized trial. Forty home health centers from across the United States were randomly selected. Eligible patients were block randomized to receive the MTM intervention or usual care. The patient population was composed of 64% admitted to home health care directly from an acute care hospital and 36% admitted from the community. Pharmacists completed a CMR and developed an action plan; a follow-up was done on day 7 and as needed for 30 days. The primary outcome of this study was 60-day all-cause hospitalization, and secondary outcomes included 30-day all-cause hospitalization and time to first hospitalization.

There was no significant difference between the 2 groups for any outcomes in the initial analysis. However, a planned post hoc analysis separated the participants into 4 groups based on baseline risk of hospitalization from the CMS risk score. An analysis of patients in the lowest-risk quartile found that those receiving a CMR were more than 3 times less likely to be hospitalized within 60 days, were more than 6 times less likely to be hospitalized within 30 days, and had a significantly longer time to first hospitalization compared with those not receiving a CMR.

Although the telephonic MTM intervention was not effective at reducing hospitalizations for the entire population, this study showed a marked reduction in the likelihood of hospitalization for patients in the lowest-risk quartile. This suggests that there may be a level of disease burden that renders the MTM intervention, by itself, insufficient. However, the intervention was effective for a population with a lower disease burden, which may have been more capable of managing their health with the assistance of telephonic MTM. This was supported in a follow-up study by Gernant et al

 that evaluated the effectiveness of a telephonic CMR as part of an MTM service on reducing 60-day all-cause ED utilization in Medicare home health patients. Overall, there was no statistical difference in ED utilization between the intervention group receiving the telephonic CMR and the control group. However, analysis of patients in the lowest-risk quartile showed that those receiving the telephonic CMR had significantly lower ED utilization. This may explain why there was no difference in 30-day hospital readmissions when comparing patients receiving a telephonic CMR with those who did not in the study conducted by Miller et al.

 This study did not have strict exclusion criteria and included patients with disease states that put them at high risk for readmission, such as cystic fibrosis.

 studied the impact of a telephonic MTM program on prescription, medical, and total healthcare expenditures among members of a Medicare Part D sponsor. A comparison of 9-month pre-intervention and 9-month postintervention all-cause total healthcare expenditures per participant demonstrated an association between provision of MTM services and healthcare cost savings. Participants receiving a telephonic CMR had an average savings in total all-cause healthcare expenditures of $3680 per member.

The control group, which did not participate in the telephonic MTM program, had an average increase of $393 per member. Interestingly, the difference in all-cause healthcare expenditures between the 2 groups was entirely due to the MTM program’s impact on medical spending. The MTM group experienced an average decrease of $3959 (30%), whereas the matched control group experienced an average increase of $130 (1%) in medical spending. Both groups experienced an increase of about 5% in drug spending. Drug costs may have increased due to greater adherence or the optimization of therapeutic regimens, requiring the addition of doses or medications to a patient’s therapy. The telephonic MTM program in this study significantly decreased healthcare expenditures compared with the control group.

 studied the impact of a telephonic MTM program on plan-paid healthcare costs by performing a retrospective cohort study of eligible members matched by propensity scores. The intervention consisted of a telephonic CMR conducted by a pharmacist. Each patient received at least 2 more telephonic consultations within 12 months. In addition to plan-paid healthcare costs, the utilization of medical services, overall days’ supply of targeted medications, and medication possession ratios (MPRs) were assessed. Plan-paid healthcare expenditures were significantly reduced among those receiving telephonic CMR compared with controls. The study group had a significant reduction in inpatient visits of 18.6% compared with an increase of 24.2% in the control group. However, there was no difference in ED visits between the 2 groups. The study group experienced an increase in average days of medication supply of 72.7 days compared with a decrease of 111 days for the control group, as well as a significant increase in MPRs for hypertension and dyslipidemia compared with the control group. However, the follow-up consultations provided in this study may not be feasible for all MTM programs, limiting the generalizability of the results. This study demonstrated a model for an effective telephonic MTM program that significantly reduced healthcare expenditures and inpatient visits while increasing patient adherence.

 retrospectively studied the impact of a telephonic MTM program on cost savings among Medicare Advantage Prescription Drug plan members. During 2006 and 2007, members of Michigan’s Health Alliance Plan who opted into the MTM program were compared with those who declined. Although both groups had a significant reduction in drug costs per member per month for the 2006 plan year, members receiving MTM services had a greater reduction in drug costs compared with those who declined: 17.2% versus 7%. A 6-question survey revealed that more than 95% of patients found the service helpful and 90% reported the telephonic communication as convenient.

 studied the impact of a telephonic MTM program on the resolution of medication- and health-related problems (MHRPs), medication adherence, and Medicare Part D drug costs. Medicare Part D beneficiaries voluntarily enrolling in the MTM program and receiving a telephonic MTM consultation were compared with Part D beneficiaries who were eligible to participate in the MTM program but did not enroll. The control group was matched to the study group based on their number of Part D drugs and of chronic diseases. The telephonic MTM consultation consisted of a CMR provided by a pharmacist. MHRPs were identified by the pharmacists. A 6-month follow up using patient EHR and prescription claims determined recommendations that had been accepted and the number of MHRPs that were still present. A prescription claims history was used to measure total Medicare Part D drug costs and MPR to assess medication adherence.

MHRPs were reduced by 48% among those receiving the MTM consultation, which was significantly greater than the 14% reduction in the control group. However, medication adherence, as measured by MPR, did not increase as a result of the MTM consultation. In addition, the consultation did not significantly reduce Part D drug costs compared with the control group. This study’s findings demonstrate that a telephonic MTM program can reduce MHRPs.

It should be noted that many contributing factors, including interventions performed by other providers, may affect the outcomes of MTM services.

Based on the review of published studies, telephonic MTM programs offering CMRs may significantly reduce mortality, hospitalizations, and healthcare expenditures and enhance patient satisfaction and quality of life through drug therapy optimization. However, the addition of medications when needed to achieve optimal therapy and/or increased adherence may negate the cost-saving opportunities in some patients. Moving forward, further evaluation of existing telephonic MTM programs is necessary to determine which components of the program make it most effective. Increasing access to patients’ EHRs, patient follow-up frequency, and the use of collaborative practice agreements may offer additional opportunities for improving patient outcomes.

 Medication Management Program, Institute of Therapeutic Innovations and Outcomes, Colleges of Pharmacy (EAD, AMC, MCN) and Medicine (MCN), The Ohio State University, Columbus, OH.

 Concept and design (EAD, AMC, MCN); acquisition of data (EAD); analysis and interpretation of data (EAD); drafting of the manuscript (EAD, AMC, MCN); and critical revision of the manuscript for important intellectual content (EAD, AMC, MCN).

 Milap C. Nahata, PharmD, MS, College of Pharmacy, Ohio State University, 500 W 12th Ave, Columbus, OH 43210. Email: nahata.1@osu.edu.

1. Ortman JM, Velkoff VA, Hogan H. An aging nation: the older population in the United States. United States Census Bureau website. census.gov/prod/2014pubs/p25-1140.pdf. Published May 2014. Accessed February 5, 2017.

2. CMS. Medication therapy management. CMS website. cms.gov/Medicare/Prescription-Drug-Coverage/PrescriptionDrugCovContra/MTM.html. Published 2016. Updated October 17, 2017. Accessed February 7, 2017.

3. Viswanathan M, Kahwati LC, Golin CE, et al. Medication therapy management interventions in outpatient settings. Comparative Effectiveness Review no. 138. Agency for Healthcare Research and Quality website. effectivehealthcare.ahrq.gov/sites/default/files/pdf/medication-therapy-management_research.pdf. Published November 2014. Accessed February 11, 2017.

4. Welch EK, Delate T, Chester EA, Stubbings T. Assessment of the impact of medication therapy management delivered to home-based Medicare beneficiaries. 

. 2009;43(4):603-610. doi: 10.1345/aph.1L524.

5. Hui RL, Yamada BD, Spence MM, Jeong EW, Chan J. Impact of a Medicare MTM program: evaluating clinical and economic outcomes. 

6. Zillich AJ, Snyder ME, Frail CK, et al. A randomized, controlled pragmatic trial of telephonic medication therapy management to reduce hospitalization in home health patients. 

. 2014;49(5):1537-1554. doi: 10.1111/1475-6773.12176.

7. Gernant SA, Snyder ME, Jaynes H, Sutherland JM, Zillich AJ. The effectiveness of pharmacist-provided telephonic medication therapy management on emergency department utilization in home health patients. 

. 2016;32(5):179-184. doi: 10.1177/8755122516660376.

8. Miller DE, Roane TE, McLin KD. Reduction of 30-day hospital readmissions after patient-centric telephonic medication therapy management services. 

. 2016;51(11):907-914. doi: 10.1310/hpj5111-907.

9. Ward MA, Xu Y. Pharmacist-provided telephonic medication therapy management in an MAPD plan. 

10. Moore JM, Shartle D, Faudskar L, Matlin OS, Brennan TA. Impact of a patient-centered pharmacy program and intervention in a high-risk group. 

. 2013;19(3):228-236. doi: 10.18553/jmcp.2013.19.3.228.

11. Pindolia VK, Stebelsky L, Romain TM, Luoma L, Nowak SN, Gillanders F. Mitigation of medication mishaps via medication therapy management. 

. 2009;43(4):611-616. doi: 10.1345/aph.1L591.

12. Moczygemba LR, Barner JC, Lawson KA, et al. Impact of telephone medication therapy management on medication and health-related problems, medication adherence, and Medicare Part D drug costs: a 6-month follow up. 

. 2011;9(5):328-338. doi: 10.1016/j.amjopharm.2011.08.001.

Discussion of current literature regarding the impact of telephonically delivered comprehensive medication reviews on patient outcomes, including mortality rates, hospital readmission rates, and healthcare expenditures.

Impact of Telephonic Comprehensive Medication Reviews on Patient Outcomes

 To develop and validate a predictive model for first severe chronic obstructive pulmonary disease (COPD) exacerbation using health insurance claims data and to validate the risk measure of controller medication to total COPD treatment (controller and rescue) ratio (CTR).

 A predictive model was developed and validated in 2 managed care databases: Truven Health MarketScan database and Reliant Medical Group database. This secondary analysis assessed risk factors, including CTR, during the baseline period (Year 1) to predict risk of severe exacerbation in the at-risk period (Year 2).

 Patients with COPD who were 40 years or older and who had at least 1 COPD medication dispensed during the year following COPD diagnosis were included. Subjects with severe exacerbations in the baseline year were excluded. Risk factors in the baseline period were included as potential predictors in multivariate analysis. Performance was evaluated using C-statistics.

223,824 patients. The greatest risk factors for first severe exacerbation were advanced age, chronic oxygen therapy usage, COPD diagnosis type, dispensing of 4 or more canisters of rescue medication, and having 2 or more moderate exacerbations. A CTR of 0.3 or greater was associated with a 14% lower risk of severe exacerbation. The model performed well with C-statistics, ranging from 0.711 to 0.714.

 This claims-based risk model can predict the likelihood of first severe COPD exacerbation. The CTR could also potentially be used to target populations at greatest risk for severe exacerbations. This could be relevant for providers and payers in approaches to prevent severe exacerbations and reduce costs.

To prevent worsening of chronic obstructive pulmonary disease (COPD) and its associated costs, identifying patients at high risk of a first severe exacerbation is critical.

A risk model for first severe COPD exacerbation was developed and validated using health insurance claims.

Total COPD treatment (controller and rescue) ratio (CTR) can predict first severe COPD exacerbation using health insurance claims.

COPD patients with a CTR of 0.3 or greater are at reduced risk of having a severe COPD exacerbation in the subsequent year.

Moderate exacerbation history, advanced age, use of rescue medication, chronic oxygen therapy, and type of COPD were also risk factors for first severe COPD exacerbation.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the United States, with more than 147,000 patients dying because of it in 2014.

 COPD poses a substantial economic burden on patients, healthcare systems, and society. In the United States, $32.1 billion in medical costs were associated with COPD in 2010, with costs projected to rise to $49.0 billion by 2020.

A major portion of COPD-related healthcare costs are attributable to exacerbations (sustained worsening of COPD from stable state, beyond normal day-to-day variations).

 Exacerbations are acute in onset and warrant additional treatment.

 Increased frequency of exacerbations leads to more hospital and emergency department (ED) visits.

 In 2009, approximately 740,000 hospitalizations were related to COPD.

 Hospitalization costs represent 85% of direct medical costs associated with COPD and are largely associated with exacerbations.

 Based on a Healthcare Cost and Utilization Project analysis, Perera et al showed that severe exacerbations, defined as hospitalizations for acute COPD exacerbations, were associated with increased aggregate costs, rising from $2.96 billion in 2006 to $3.47 billion in 2010.

 Furthermore, in a retrospective analysis of the Thompson Reuters MarketScan administrative database, Yu et al reported that COPD-related costs for patients with severe exacerbations were $7014 per 90 days compared with $658 per 90 days for patients with no exacerbations, with 83% of the costs associated with inpatient expenses.

 In another retrospective study, Abudagga et al reported that the costs associated with severe exacerbations were $18,120 and increased with each additional prior exacerbation.

Given the high costs associated with COPD exacerbations, it is important to identify patients at risk of exacerbation to target them for early treatment and improved prevention. Prior history of exacerbations is among the most important risk factors for the development of subsequent exacerbations.

 Previous clinical studies have identified additional risk factors for COPD exacerbations, with several groups developing exacerbation risk models.

 However, risk assessment models for COPD exacerbations utilizing readily accessible claims data that are available to health plans and healthcare groups, such as national quality-of-care organizations, are not available.

Identifying patients at risk for their first severe exacerbation is critical to target early and effective treatment, potentially slowing progression and associated cost increases. The present study was a secondary analysis from a wider study in which a medication ratio using pharmacy claims and a risk model were developed and validated. The primary results of this study have been published.

 In this secondary analysis, we aimed to construct and validate a predictive model of first severe COPD exacerbation event using health insurance claims, a source of data readily available to health plans and most quality-of-care organizations. This secondary analysis aimed to further validate the ability of the controller medication to total COPD treatment (controller and rescue) ratio (CTR) to predict first severe COPD exacerbation. The CTR is similar to the current Healthcare Effectiveness Data and Information Set (HEDIS) measure in asthma, the asthma medication ratio, and could have the potential to be used in a similar capacity.

 There were 2 objectives of the present analysis: 1) construction and validation of a risk model for first severe COPD exacerbation using patient demographics, comorbid conditions, and COPD treatment claims data as covariates; and 2) validation of the CTR as a measure of severe COPD exacerbation risk. The CTR is the ratio of total controller medications (inhaled corticosteroids [ICS], long&#8209;acting β

-adrenergic agonists [LABA], ICS + LABA, long&#8209;acting muscarinic antagonists, phosphodiesterase-4 inhibitors, and methylxanthines) to total controller plus rescue medications (short-acting β-agonists or short-acting muscarinic antagonists [SAMA]).

This retrospective study (GlaxoSmithKline study #HO-11-732) used the Truven MarketScan Commercial Claims and Encounter Database and Medicare Supplemental and Coordination of Benefits database (MarketScan database, Truven Health Analytics; Ann Arbor, Michigan) from 2006 to 2011 to construct and validate a risk model of prediction of first severe COPD exacerbation and to test the CTR measure. The Reliant Medical Group (Reliant database, Reliant Medical Group; Worcester, Massachusetts) was used for cross-validation.

The study start date (index date) was the second encounter with a COPD diagnosis (defined in study population section) within a year. Risk factors were assessed during the baseline period, which spanned 1 year, starting at the index date. This was followed by a 1&#8209;year at-risk period during which the occurrence of severe COPD exacerbations, the primary endpoint, was assessed (

Patients with COPD who were 40 years and older were included in the study. The index date of COPD status was defined as the second outpatient and/or ED encounter with a COPD diagnosis (

International Classification of Diseases, Ninth Revision, Clinical Modification

] codes 491.x [chronic bronchitis], 492.x [emphysema], or 496 [chronic airway obstruction]) within 1 year. Patients were also required to have at least 1 dispensing for an inhaled COPD medication or oral theophylline during the year following the index date and to have had continuous insurance eligibility (no more than 45 days without coverage) for at least 2 years following the index date.

Patients were excluded if they had a severe COPD exacerbation, defined as a COPD-related hospitalization during the baseline period, or at least 1 claim with an 

 diagnosis code for cancer (except cancer types that are typically considered to have little effect on lung function [eg, breast, prostate, or skin cancer except melanoma]) or other non-COPD lung disease during the baseline period.

The MarketScan database was used for model development and validation. This database includes health insurance claims data from 2006 to 2011, representing approximately 18 million covered individuals, and contains information on enrollment history and claims for medical (provider and institutional) and pharmacy services. The Reliant database was used for external validation. It includes longitudinal member-linked medical claims (physician and facility), pharmacy claims, enrollment records, laboratory results, and electronic health record information.

The primary endpoint of this study was the occurrence of a severe COPD exacerbation during the at-risk period (Year 2). A severe COPD exacerbation was defined as a hospitalization with either a primary diagnosis of COPD (excluding obstructive chronic bronchitis without exacerbation; 

code 491.20) or a secondary diagnosis for COPD with a primary diagnosis of respiratory failure (

 codes 518.81, 518.82, or 518.84). The choice of a 1-year window for the baseline and at-risk periods for the base case scenario made it possible to account for the seasonality of COPD exacerbations. Yet, given that risk factors were evaluated in the year prior to the at-risk period, they may not accurately reflect risk factors during the totality of the at-risk period. For example, the CTR may reflect more reliably the COPD medications patients received at the beginning of the at-risk period than those received toward the end of the at-risk period. As such, sensitivity analyses were conducted that limited the at-risk period to 90 days or 180 days following the baseline year (Year 1).

Candidate risk factors were assessed during the baseline period and used as predictors for severe exacerbation risk in the following year. Candidate risk factors were gender, age, region, insurance plan, H1N1 flu season (October 2009 to May 2010), pulmonologist visit, county characteristics (altitude; number of pulmonologists per 100,000 inhabitants; number of hospitals per 100,000 inhabitants; proportion of households below the low-income margin; median household income; proportion of patients without health insurance; proportions of high school dropouts and college graduates; urban/suburban/rural localization; and proportion of white, black, Hispanic, and Asian patients, derived from the Area Resource File

), type of COPD diagnosis, exacerbation history (moderate only [ie, defined as outpatient treated or ED visit for COPD with a dispensing for an oral corticosteroid within 7 days]), COPD medications (based on at least 1 dispensing), concomitant medications, procedures (flu and pneumococcal vaccines, use of chronic oxygen therapy, nebulizer, and spirometry; all based on Current Procedural Terminology and Healthcare Common Procedure Coding System codes), and comorbidities based on 

 Based on univariate associations among risk factors and the probability of an exacerbation in the at-risk period, risk factors were excluded if 

 tests for discrete variables and Wilcoxon tests for continuous variables. Risk factors present in less than 0.5% of the sample were excluded.

The choice of factors for the risk model was based on bootstrap resampling in combination with stepwise variable selection with logistic regression modeling, using a random sample of the MarketScan sample (development sample).

 A total of 1000 bootstrap samples were drawn; each was a random sampling with replacement from the original development sample, and each bootstrap replication was done using a different bootstrap sample. Within each bootstrap replication, all candidate risk factors were included, then stepwise elimination of risk factors was performed using the Akaike information criterion to identify predictors of exacerbations in the final model for the replication. Risk factors were chosen if they were selected in: 1) 100% of the 1000 bootstrap replications for the modeling of severe exacerbation or 2) at least 90% of the bootstrap replications for severe exacerbation and in 100% of the bootstrap replications for moderate or any type of exacerbations. CTR, which was defined as the number of canisters dispensed (converted to 30-day equivalent) for controller medication divided by the number of canisters dispensed for controller plus number of canisters of rescue medication (ie, SAMA, short&#8209;acting β

-adrenergic agonists [SABA], SAMA + SABA), was included in the final risk model. In order to assess the CTR as a predictive measure of first severe exacerbation, various ratio thresholds from at least 0.1 to at least 0.9 were tested. A cutoff of 0.3 was found to be the cutoff associated with the largest statistically significant risk reduction for severe exacerbations and has been shown to be the only cutoff associated with a statistically significantly reduced risk of any, moderate, and severe exacerbations in the previously published primary objectives of this study.

 Therefore, this ratio was used in the risk model of first severe COPD exacerbations.

The risk model was developed using the development sample, validated using the other random sample of the MarketScan data (validation sample), and cross-validated using the Reliant Medical Group data (cross-validation sample). Performance of the risk model was evaluated using C-statistics.

 Additional model analyses were performed on selected subpopulations, including those without a history of asthma and differing controller medication usage.

All statistical analyses were performed using SAS version 9.2 or newer (SAS Institute, Inc; Cary, North Carolina).

A total of 223,824 patients without history of severe exacerbations were included in the study, with 111,904 used in the development sample. Of the entire MarketScan sample of patients without history of severe exacerbations, approximately 80% experienced no exacerbations in the at-risk period (

). Patients who experienced exacerbations during the at-risk period tended to use more COPD medications and were more likely to use chronic oxygen therapy during the baseline period than patients who did not experience an exacerbation during the at-risk period (

Risk Factor Modeling for Severe Exacerbation

A total of 30 risk factors were included in the model; all were statistically significantly associated with severe exacerbations. The 5 risk factors with the largest impact on the probability of developing a severe exacerbation in the at-risk year were advanced age, use of chronic oxygen therapy, use of 4 or more canisters of rescue medication, having at least 2 prior moderate exacerbations, and type of COPD diagnoses received during the baseline period (

Patients with a CTR of 0.3 or greater had a lower risk of developing severe exacerbations (odds ratio [OR], 0.86; 95% CI, 0.77-0.96) (Table 2). In a subpopulation analysis, among patients with 4 or more fills for controller medications, a CTR of 0.3 or greater was associated with an even lower risk of severe exacerbation (OR, 0.76; 95% CI, 0.64-0.91) (

The model performed relatively well in predicting severe exacerbation risk, with C-statistics in the MarketScan development and validation and Reliant cross-validation samples of 0.713, 0.714, and 0.711, respectively (Table 2).

Most risk factors for severe exacerbation identified as most relevant using the 1-year at-risk period (eg, advanced age, use of chronic oxygen therapy, and having at least 2 prior moderate exacerbations in the baseline year) were also identified in sensitivity analyses using either a 90- or 180-day at-risk period (

]). Based on C-statistics, the performance of these models was similar to that of the model based on a 1&#8209;year at-risk period.

This report presents the development and validation of a risk model and the testing of the CTR for prediction of first severe COPD exacerbation using health insurance claims information. The main risk factors of first severe COPD exacerbation identified included advanced age, prior moderate exacerbation history, use of rescue medication, use of chronic oxygen therapy, and type of COPD diagnosis. A CTR of 0.3 or greater also was determined to predict first severe COPD exacerbation. The main advantage of the risk model and the CTR measure is that they do not rely on medical records, which are often unavailable to quality-of-care organizations and payers. Unlike previously reported measures, the CTR can be used to specifically assess the risk of first severe exacerbation and may help to identify which patients should be targeted for more intense management so that hospitalizations and costs could be reduced.

This model performed well in predicting the risk of first severe exacerbation at a population level using the MarketScan development and validation samples and the Reliant validation sample. Given the differences between the national MarketScan population and the regional Reliant database, the relatively consistent performance of the risk model in the Reliant population, with a C-statistic value of 0.711, indicates that the model and the CTR could be generalizable to heterogeneous populations.

Prior history of exacerbations is known to be among the most important risk factors for developing subsequent exacerbations.

 In an analysis of patients enrolled in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study, history of exacerbation was shown to be the best predictor of exacerbation, regardless of Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage.

 Previous incidence of exacerbations was also found to be an independent predictor of an exacerbation within 24 months (OR, 5.07; 95% CI, 2.55-10.07) in a study of 243 patients in the Netherlands.

 Our study confirmed that prior moderate exacerbations were a risk factor for a subsequent first severe exacerbation; however, a higher CTR value was associated with a reduced risk of first severe exacerbation regardless of exacerbation history. These results suggest that identifying patients at risk for their first severe exacerbation would be critical for targeting early and effective treatment, preventing worsening of the disease, and consequently reducing costs associated with COPD exacerbations.

There is a lack of published literature on risk factors for first severe COPD exacerbations. Müllerova et al evaluated the risk factors for a subcohort of patients with COPD from the ECLIPSE study who had experienced exacerbations requiring hospitalization for the first time during the 3-year study follow-up period.

 Significant risk factors identified were health status, COPD severity (based on forced expiratory volume in 1 second [FEV

]), increasing age, existence of emphysema or asthma, use of home oxygen, and elevated fibrinogen levels.

 Because data on COPD severity or fibrinogen levels were not available in the MarketScan data set, their relevance to exacerbation risk was not considered in this study.

Other studies have focused on the risk of severe exacerbation in general and did not model the risk of first severe COPD exacerbation.

 Garcia-Aymerich et al considered various clinical, medication compliance, lifestyle, and sociodemographic risk factors for COPD-associated hospitalizations.

 They found that previous COPD-associated hospitalizations, COPD severity (FEV

), under-prescription of long-term chronic oxygen therapy, and smoking were the most relevant risk factors for subsequent COPD-associated hospitalizations.

 In a follow-up study of patients from a clinical trial of tiotropium, Niewoehner et al identified increasing age, COPD severity, prior unscheduled ED visits or hospitalizations for COPD, cardiovascular comorbidities, and use of oral corticosteroids as significant risk factors for COPD-associated hospitalization.

Although previously published risk models of COPD exacerbations have focused on exacerbation frequency and COPD-associated hospitalizations, rather than the risk of first severe exacerbation, their findings were consistent with those of the current study in that factors such as increasing age and exacerbation history were highly relevant.

 Moreover, the performance of the claims-based model compares favorably with these models. In an exacerbation risk model developed by Bertens et al, in which exacerbation history, COPD severity, smoking, and vascular disease history were identified as risk factors, a C-statistics value of 0.66 was reported.

 In a model for frequent exacerbations published by Miravitiles et al, in which chronic mucus hypersecretion, COPD severity, and increasing age were associated risk factors, C-statistics values of 0.601 to 0.655 were obtained.

Certain methodologic limitations in this study should be noted. First, the identification of exacerbation events was based on definitional algorithms using health insurance claims data that have not been validated. These may have less than 100% sensitivity and specificity and may have misclassification errors for the outcome variables. Second, health insurance claims data are subject to inaccuracies and mistakes. In particular, an 

 diagnosis code on a medical claim is not an attestation that the patient has the diagnosis, because the code may represent a rule-out diagnosis or may be recorded incorrectly. This limitation applies to both the exacerbation events and the comorbidities used as risk factors in the models. Additionally, these records do not confirm that patients took the dispensed medication. A third study limitation was that included patients might have experienced severe exacerbations prior to the index date of this study. Finally, the covariates for the risk equations were limited to health insurance claims data only, which lack disease severity information (eg, GOLD stage). Nevertheless, using a subset of the Reliant sample, the performance of the risk model, including GOLD stage, was assessed and showed only a marginal improvement in incremental predictive accuracy.

A health insurance claims-based risk assessment tool to predict patients’ first severe chronic obstructive pulmonary disease exacerbation has been developed and validated.

Claims-Based Risk Model for First Severe COPD Exacerbation

 Patient-centered care initiatives have proliferated, but assessing their effectiveness requires measures tailored to their likely effects. In this article, we describe the development and pilot testing of patient surveys used to assess change in patients’ cancer care experiences over time in response to a patient-centered care initiative.

 Domains of patient-centered care were informed by the goals of the initiative and a review of existing tools. Items were selected and modified from 6 domains of validated or semivalidated instruments. Items were piloted with patients with cancer in waiting room settings to further assess the relevance and clarity of items, whether important concepts were missing, and acceptability regarding place and timing of the surveys and to estimate baseline top box scores (percentage of patients scoring an item the highest quality level) to minimize likely ceiling effects. The instrument was then administered to a consecutive sample of Stanford Cancer Center patients. Baseline item responses, Cronbach’s alpha, and response bias were estimated.

Items were modified based on patient feedback, top box scores, and reassessment of the domains. Over 6 months, 11,273 patients were surveyed, with a 49.7% response rate. Baseline top box scores ranged from 41.7% to 75.0% for any given item. Reliability and internal consistency were high for all domains (Cronbach’s alpha ≥0.80) except for the access domain.

We developed reliable instruments to evaluate the essential elements of a patient-centered care initiative at an academic medical center, which minimized patient burden and maximized the response rate.

Assessing the effectiveness of patient-centered initiatives requires measures tailored to their likely effects.

Our method to develop a patient-relevant sensitive survey involved identification and modification of existing instruments followed by an iterative process of pilot testing with patients, measuring top box responses, and modifying further as needed.

This process resulted in 4 unique short instruments with high response rates and internal validity that measured patients’ cancer care experiences on domains important to them.

The methods could be applied in any context, resulting in more meaningful data than using a preexisting validated tool that does not meet the needs of the local community or situation.

Assessing care through the eyes of patients and their families has become increasingly important. A 2001 Institute of Medicine report, “Crossing the Quality Chasm: A New Health System for the 21st Century,” set patient-centered care as an explicit goal and called for measures to support that goal.

 Since then, significant efforts have been implemented to better understand the key healthcare issues patients face, their experiences with their healthcare, and ways to measure those experiences. These have resulted in development, broad implementation, and public reporting of routine patient surveys, such as the Consumer Assessment of Healthcare Providers and Systems (CAHPS), which is specific to hospitalizations

Existing tools assessing patient satisfaction with their healthcare experiences have several practical limitations, including ceiling effects, poor responsiveness to interventions, lack of specificity to condition/disease, and infeasible administration times.

 Ceiling effects, which occur when a high proportion of patients give the optimal score for that question, limit variation and make items insensitive to improvements. Furthermore, the general domains of experience most commonly included in surveys may miss either the disease or the targets of improvement efforts, also limiting their utility to detect change.

 For instance, healthcare concerns and experiences of patients receiving complex care for life-threatening chronic conditions are different from those with acute conditions.

We developed short patient experience surveys that were sensitive to our broad quality initiative, were meaningful and acceptable to patients, and had good response rates.

February 2018

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