To compare hospital-level, risk-adjusted inpatient and 90-day postdischarge adverse outcomes in Medicare patients undergoing elective percutaneous coronary interventions (PCIs).

 Develop predictive risk models for adverse outcomes during inpatient and 90-day postdischarge care, and use those models to compare hospital performance in elective PCI.

 Elective PCIs in the 2010 to 2012 Medicare Limited Data Set were used to develop logistic prediction models for inpatient deaths, inpatient prolonged-length-of-stay outliers, 90-day postdischarge deaths without readmission, and 90-day readmissions. Observed versus predicted differences for risk-adjusted adverse outcomes were then performed among all hospitals with 50 or more cases during the study period.

There were 978 hospitals with 168,518 patients that qualified for this study; 25.9% of all patients had 1 or more adverse outcome. There were 67 hospitals with adverse outcome rates that were 2 or more standard deviations (SDs) better than predicted and 81 hospitals with rates that were more than 2 SDs worse than predicted. The best- and worst-performing deciles of hospitals had median risk-adjusted adverse outcome rates of 17.6% and 35.5%, respectively. Hospital case volume was not independently associated with better outcomes.

Comparison of risk-adjusted adverse outcome rates demonstrated the existence of opportunities for substantial improvements in quality among suboptimal-performing hospitals.  

The American Journal of Accountable Care. 2017;5(2): e7-e15

Inpatient percutaneous coronary intervention (PCI) was performed on an estimated 515,000 patients in 2013, 50% of whom were 65 years or older.

 The results of this procedure have progressively improved from the early efforts with coronary angioplasty to the current use of stents. The inpatient mortality rate with PCI is less than 1%, and the rate of inpatient complications, which include technical problems with coronary vessels, hematoma at the vascular entry site, stroke, and contrast-associated nephropathy, is less than 7%.

In a study of Medicare coronary artery bypass grafting (CABG) and valve replacement surgery,

 we found that inpatient adverse outcomes of these major cardiac procedures constituted less than 35% of total adverse events (AEs) when 90-day postdischarge deaths and readmissions were included. Furthermore, there were dramatic differences among hospitals in their rates of overall inpatient and postdischarge adverse outcomes. These data on comparative hospital risk-adjusted outcomes among the best and poorest performances strongly suggested that many of these adverse outcomes were preventable.

In the current study, we similarly evaluated risk-adjusted outcomes among inpatient Medicare patients who underwent PCI to identify comparative hospital performance in both inpatient and 90-day postdischarge events. Differences between top and suboptimal performances among all hospitals should define the margin of preventable AEs. Because it was anticipated that readmissions would be the predominant adverse outcome, we have examined the causes of readmission to better define opportunities for improvement.

The Medicare Limited Data Set for 2010 to 2012 was used in this study. Patients with an 

International Classification of Diseases, 9th Revision, Clinical Modification

) procedure code of 00.66, 36.06, 36.07, 36.09, and 37.34 for PCI were identified. All patients included in the study were required to have a principal diagnosis code of 414.00-414.05. Patients who had had an acute myocardial infarction were excluded. Cases were also excluded if patients were younger than 65 years, had missing patient or hospital identifiers, were transferred from another facility, or were discharged against medical advice.

Two overlapping databases were designed. Risk-adjustment models were derived using a developmental database that consisted of patients from hospitals that satisfied quality coding criteria and had the 20 or more qualifying cases needed to construct hospital-level moving-range control charts.

 Final models were used to compare the overall adverse outcome rates of all hospitals that met the minimum volume criteria of 50 evaluable cases required for inclusion in a study database. Hospitals with fewer than 50 qualifying cases were excluded from the study database to avoid skewing final comparisons of hospitals’ performance by including hospitals with few qualifying cases and very low predicted numbers of adverse outcomes.

Using the developmental database, prediction models were designed for: 1) inpatient deaths (IpD), 2) prolonged risk-adjusted lengths-of-stay (prLOS), 3) 90-day postdischarge deaths without readmission (PD-90), and 4) 90-day readmission (RA-90). Models were derived using stepwise logistic regression on more than 500 candidate risk factors, including patient age and coded comorbid conditions that were present at admission. Hospital variables were employed to account for hospital effects. The Schwarz criterion was used to avoid overfitting final models,

 and the discrimination of final models was measured with C statistics. All analyses were performed with SAS version 9.4 (SAS Institute; Cary, North Carolina).

The IpD model included only inpatient deaths. PrLOS was used as a surrogate for severe complications of inpatient care among live discharges. Cases with prLOS were identified by first developing a linear model for inpatient length of stay (LOS) among cases without any coded complications. Predicted LOS for all cases in the developmental database were subtracted from observed LOS, differences were temporally aligned within each hospital, and moving-range control charts were created to identify outliers that exceed the 3 sigma upper control limit.

 Previous studies including cardiac procedures have associated prLOS with severe complications, increased costs,

 and higher rates of postdischarge deaths and readmissions.

The PD-90 and RA-90 models included AEs occurring within 90 days of discharge. We used 90 days because our previous work demonstrated that following surgical procedures, more than 40% of relevant deaths and readmissions occur between 31 and 90 days after discharge.

 To study RA-90, we excluded all 90-day readmissions for Medical Diagnostic Categories (MDCs) 2 (Eye Diseases), 17 (Myeloproliferative Diseases), and 22/24 (Burns/Major Trauma), as well as all Medicare-Severity, Diagnosis-Related Groups (MS-DRGs) related to the management of trauma or cancer regardless of MDC. PrLOS events during the index hospitalization were included in PD-90 and RA-90 models to clarify the relationship between inpatient complications and postdischarge AEs and to remove the effects of inpatient complications on coefficients in postdischarge models. MS-DRGs were identified for each readmission. All valid readmissions were divided into those occurring within 30, 60, and 90 days of discharge, but only 90-day readmissions were used in RA-90 model development.

The 4 PCI prediction models for adverse outcomes from the developmental database were applied to all patients in hospitals in the study database. All hospitals had more than 4.5 predicted total adverse outcomes and more than 4.5 predicted 90-day readmissions; 90% had more than 4.5 predicted prolonged LOS outliers. The IpD model was applied to all cases. The prLOS model was applied to all live inpatient discharges. Live discharges without prLOS were used to predict PD-90, and live discharges without prLOS or PD-90 were used to predict readmissions.

Among all hospitals, total predicted adverse outcomes were adjusted to equal total observed adverse outcomes. For each hospital, a 

 score was computed as [observed adverse outcomes — predicted adverse outcomes] 

 standard deviation (SD), where the SD equals and N equals the number of qualifying cases at that hospital. Negative 

 scores indicated that outcomes were better than predicted; positive 

 scores indicated that outcomes were poorer than predicted. Each hospital’s risk-adjusted adverse outcome (RAAO) rate was computed as [overall observed adverse outcomes rate] 

 hospital-predicted adverse outcomes]. RAAO rates were grouped into deciles for comparison. To better define the contribution of inpatient and postdischarge adverse outcomes to overall performance, each hospital’s total adverse outcomes were subdivided into observed and predicted prLOS and observed and predicted RA-90. To evaluate the influence of case volume on RAAO rates, hospitals were grouped into deciles based on their case volume during the study period.

Of the 150,903 patients in the developmental database, 503 (0.33%) died during their index admission; 10,341 (6.8%) had prLOS; 730 (0.5%) died within 90 days of discharge without a readmission; and 31,124 (20.6%) were readmitted within 90 days of discharge. The significant risk factors (

 <.001) in each of the predictive models for these outcomes are summarized in 

). The IpD model had 10 significant risk factors and a C statistic of 0.748 after removal of hospital variables. The prLOS model had 66 significant risk factors and a final C statistic of 0.747. The PD-90 model had 13 significant risk factors and a final c-statistic of 0.768. The RA-90 model had 43 risk factors and a final C statistic of 0.644. PrLOS was a significant predictor of 90-day readmissions (odds ratio [OR], 1.62) and 90-day postdischarge mortality without readmission (OR, 3.99).

 details the MS-DRGs of qualifying readmissions in the developmental dataset. There were 31,124 patients readmitted 41,274 times, with 18,696 (45.3%) readmissions occurring during the first 30 days following discharge; 12,411 (30.1%) between days 31 and 60; and 10,167 (24.6%) between days 61 and 90. The majority of readmissions (58%) had cardiovascular principal diagnoses.

The study database consisted of 168,518 patients from 978 hospitals, with each having 50 or more qualifying cases (average of 172 cases per hospital; median of 119 cases). There were 571 (0.34%) inpatient deaths; 11,603 (6.9%) patients with prLOS; 1613 (1.0%) postdischarge deaths without readmission; and 34,841 (20.7%) patients with 1 or more readmissions. There were 820 deaths after readmissions. Inpatient and 90-day postdischarge deaths totaled 3004 (1.8%). There were 43,613 (25.9%) patients who had 1 or more adverse outcomes after PCI.

 scores for observed versus expected adverse outcomes rates. 

 scores for study hospitals ranged from —6.2 for the best performing hospital to +6.3 for the poorest performing hospital. There were 67 hospitals with differences more than 2 SDs better than the average and 81 hospitals with differences more than 2 SDs poorer than average.

demonstrates the distribution of RAAO rates among hospitals by decile of performance. The best performing decile had a median adverse outcome rate of 17.6%, while the poorest performing decile had a median adverse outcome rate of 35.5%. Error bars represent the interquartile range within each decile.

 demonstrates the relationship between risk-adjusted rates of prLOS and comparable rates of RA-90. The ratio of observed to predicted length of stay outliers are strongly correlated with the ratio of observed to predicted 90-day readmissions. Hospitals that have low risk-adjusted rates of inpatient complications of care have comparable low risk-adjusted rates of postdischarge readmissions. Hospitals with higher risk-adjusted inpatient complication rates have higher rates of risk-adjusted 90-day postdischarge readmissions.

 illustrates the relationship of adverse outcomes and hospitals’ case volumes. There is very little variation in outcomes across decile groups organized by volume. Each decile had a standard error of the mean of 0.5%. Analysis of variance demonstrated no significant relationship between the case volume of each hospital and its RAAO rates (

In this study, 4 objectively defined, risk-adjusted adverse outcomes were used to compare hospital performance in PCI. These adverse outcomes were treated as no-fault events, each of which may or may not have been preventable. However, when risk-adjusted outcomes at hospitals were compared, remarkable differences in measured performance suggested that there were substantial opportunities for quality improvement at many facilities.

The adverse outcomes of PCI have many similarities to those that we have reported previously in open CABG surgery.

 The overall adverse outcomes rate was 25.9% for PCI and 27.2% for CABG. There was a 1.8% inpatient and 90-day postdischarge mortality rate with PCI, and an overall 3.4% death rate for CABG. PrLOS was identified in 6.9% of PCI cases and 8.2% in CABG. A total of 20.9% of PCI patients were readmitted compared with 20.0% of CABG patients. Analysis of variance identified no difference in adverse outcomes between PCI and CABG. The comparable adverse outcome rates between the 2 populations of patients illustrates the high risk that is posed by coronary artery patients for any intervention.

In this study, 90-day postdischarge adverse outcomes exceeded the inpatient adverse outcomes. This finding may be due, in part, to progressive reductions in inpatient LOS, which result in more AEs first becoming evident after patients have been discharged. Increased awareness of the importance of postdischarge adverse outcomes has resulted in CMS imposing penalties to hospitals for excessive readmission rates for selected patient groups.

 This new focus on postdischarge AEs and the rapid evolution of accountable care organizations and bundled payment initiatives have put a premium on successful care redesign that improves important postdischarge clinical outcomes. To meet this challenge, hospitals must first institute effective methods for measuring their postdischarge adverse outcome rates. They then must devise and implement methods to link these results to specific processes of care for redesign of overall management when and where it is indicated.

Using risk-adjusted readmission rates as a measure of the quality of inpatient care is controversial because analytic methods have not been standardized and many readmissions have been judged to be clinically unavoidable or unrelated to the index hospitalization.

 The most common follow-up period for unscheduled postdischarge readmissions is 30 days, with reported rates after PCI ranging from 9% to12%.

 Wasfy et al reported that 30-day post-PCI readmissions are due mainly to chest pain and anxiety,

 are seldom due to complications of the PCI,

 and, in nearly half of cases, could be prevented with improved decision making.

 They also noted a modest improvement in predictions of 30-day readmissions when postprocedural risk factors, such as bleeding and prolonged LOS, were included in predictive models.

 extended the follow-up period for readmissions after PCI to 60 days based on their belief that 8 weeks were required to recover from the effects of myocardial damage. These investigators found overall readmission rates of 4.4% for 30 days and 8.4% for 60 days of postdischarge follow-up. They also found that readmission within 60 days of discharge was associated with increased mortality during 2 years after discharge. Similarly, Kharvaja et al found that 30-day readmissions after PCI were associated with an increased 1-year death rate.

 The only studies to examine 90-day readmissions following PCI were in clinical trials examining anticoagulation

 Both of these studies were performed on very restricted populations of patients.

The choice of a 90-day follow-up period for readmissions in this study is supported by the fact that of all readmissions that satisfied criteria for inclusion, 55% occurred 31 to 90 days after discharge. Cardiac-related events were the most common causes for readmission throughout the follow-up interval. Furthermore, Medicare’s bundled payment program is including 90 days of postdischarge care in its initial implementation,

 and PCI and coronary artery bypass surgery are expected to be included in the next set of scheduled episodes.

 Providers of inpatient PCI will likely have cost accountability for 90 days following discharge of the patient. The follow-up period of accountability for readmissions for 90 days will likely remain controversial.

Analysts have found risk-adjusted predictive models for readmission difficult to design.

 It has been difficult to obtain data about nonmedical factors that may have an even greater influence on readmission rates than conventional medical risk factors identified at the beginning of an index hospitalization. We have consistently found that prolonged risk-adjusted LOS during the index hospitalization is a powerful predictor of 90-day readmissions.

 In the current study, we also found that when hospitals are grouped by decile of performance, risk-adjusted prolonged LOS are highly correlated with readmission rates. Thus, it appears that hospitals with low risk-adjusted rates of severe inpatient complications have correspondingly low rates of risk-adjusted adverse outcomes following discharge.

In the current study, there was no significant difference in RAAO rates between low-volume and high-volume centers. In interpreting this finding, it is important to note that only inpatient, nonemergent PCIs were performed on Medicare patients older than 64 years who qualified for inclusion in the study, and hospitals with fewer than 50 such cases were excluded from comparative analyses. Therefore, almost all truly low-volume centers were probably excluded from this analysis.

This study has several important limitations. Its reliance on administrative data raises concerns about the accuracy and completeness of the diagnostic information used to construct and apply risk factors. Complete abstraction of clinical records would yield better predictive models, and previous studies have shown that even modest enhancements of administrative data with numerical laboratory results that were obtained at the time of admission improve predictions of inpatient medical and surgical mortality

Other studies have found that a patient’s ejection fraction and preprocedure serum creatinine are important independent variables in predictive models for mortality and complications in CABG

 In its favor, administrative data has the distinct advantage of consistently capturing postdischarge AEs, such as readmissions, that often are not consistently documented in clinical registries. For example, 20% to 40% of postsurgical readmissions occur at hospitals other than the hospital at which the initial operation was performed, which probably is also the case for readmissions after PCI.

 Continuing evolution of the electronic health record (EHR) may result in the creation of new hybrid databases that combine the advantages of administrative and detailed clinical data. The use of EHRs as a source of laboratory results and other clinical data acquired at the time of discharge, rather than at admission, may further enhance the predictive power of readmission models derived from hybrid databases. EHRs have the promise of improving the accuracy of administrative data.

This study was also limited by the absence of a refined set of criteria for excluding all readmissions that were unrelated to the index procedure, by the exclusion of emergency and outpatient PCIs, and PCIs performed on patients younger than 65 years. Additional research is needed to determine whether current findings extend to these populations. The availability of all-payer claims databases with encrypted patient identifiers should facilitate expanded studies that provide accurate information about important postdischarge AEs in all patients undergoing PCIs.

This study of elective PCIs in Medicare beneficiaries revealed marked differences in hospital-level risk-adjusted adverse outcomes. These findings provide a realistic estimate of benefits that could be achieved by properly focused care redesign based on accurate knowledge of comparative hospital performance and successful linking of clinical outcomes to specific clinical judgments and technical proficiencies. Improved outcomes in both inpatient and postdischarge care will be very important for providers to meet the challenge of bundled payment initiatives.

MPA Healthcare Solutions (MP, DEF, SMN, DGL, AMR, GP), Chicago, IL; Department of Surgery (DEF) and Department of Emergency Medicine (SMN), Northwestern University Feinberg School of Medicine, Chicago, IL; Department of Surgery, University of New Mexico School of Medicine (DEF), Albuquerque, NM.

This study was internally funded by MPA Healthcare Solutions, Chicago, IL.

 Dr Fry is on the research advisory board of IrriMax Corp, and has received grants from the company for surgical site infection studies; he has also received lecture fees for speaking at CareFusion. The remaining 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 (MP, DEF, SMN, DGL, AMR, GP); acquisition of data (MP, DEF, SMN, DGL, AMR); analysis and interpretation of data (MP, DEF, SMN, DGL, AMR, GP); drafting of the manuscript (DEF); critical revision of the manuscript for important intellectual content (MP, DEF, SMN, DGL, AMR; GP); statistical analysis (MP, DEF, SMN, DGL, AMR, GP); provision of study materials or patients (GP); administrative, technical, or logistic support (MP, GP); and supervision (MP, DEF, GP).

Donald E. Fry, MD, MPA Healthcare Solutions, 1 East Wacker Dr, #1210, Chicago, IL 60601. E-mail: dfry@consultmpa.com.

1. HCUPnet [inpatient descriptive statistics, 2013 DRGs 246-251]. Agency for Healthcare Research and Quality website. https://hcupnet.ahrq.gov/#setup. Accessed May 1, 2017.

Articles

The American Journal of Accountable Care

An understanding of risk-adjusted outcomes for percutaneous coronary interventions for both inpatient and 90-day postdischarge events is necessary for the redesign of care outcomes.

Medicare Inpatient and Postdischarge Outcomes of Elective Percutaneous Coronary Interventions 

From its inception, the ACO & Emerging Healthcare Delivery Coalition

 has worked to stay abreast of what’s happening in payment reform, and the spring meeting, which took place on May 4-5, 2017, certainly lived up to that promise.

 somehow divined when the House would vote on the American Health Care Act (AHCA), the 2 days in Scottsdale, Arizona, were packed with discussions that highlighted what has been happening in Washington, DC, and beyond—from what will come of the Affordable Care Act (ACA), to population health, cyberattacks, and using precision medicine to plan ahead.

Avik Roy, Forbes opinion editor and president of the Foundation for Research on Equal Opportunity, and Darius Lakdawalla, PhD, University of Southern California health economist and chief scientific officer at Precision Health Economics, led an all-star lineup gathered for the second spring at the JW Marriott Scottsdale Camelback Inn in Arizona. Attendees and sponsors enjoyed thought-provoking panels and outstanding networking opportunities, capped by a starlit dinner at the foot of Mummy Mountain.

Throughout, presenters and long-time Coalition members observed that the uncertainty over the ACA makes one thing clear: how patients pay for coverage may change, but the movement toward value-based care is well under way and must proceed. Said moderator Cliff Goodman, PhD, of the Lewin Group, “The key takeaway from this meeting is that we are making a lot of progress…in understanding what value means and how we might bridge the better understanding of value into decision making—clinical decision making and other&mdash;into how we organize and deliver and finance healthcare that is increasingly value-based.”

There were a number of discussions on how to create the right partnerships to address issues of care delivery or develop better ways to delivery care. The meeting kicked off with a presentation from Jeff Spight, president of Collaborative Health Systems, a Wellcare Company, who highlighted how his company has partnered with skilled nursing facilities to improve care. They have brought the nursing homes into initiatives that improve care.

For instance, they are working to diagnose dementia and Alzheimer’s disease earlier with a cognitive test that can take as long as 45 minutes. Getting someone to sit and take that test can be difficult in the community, but becomes easier if he or she is simply sitting in a nursing home for longer than a week.

Dennis Scanlon, PhD, of Pennsylvania State University, followed up with a presentation on creating a model of care to address social determinants of health. He presented a case study between Pennsylvania State University and WellSpan to determine how to structure such a model and make it work. First they had to decide the right area to get into: housing, food, transportation, job assistance, etc. Finally, they decided on addressing how to avoid excessively long stays in the hospital for people who don’t have housing to go to once they are discharged.

In south-central Pennsylvania, they were dealing with 600 avoidable hospital days, which resulted in hospitals reaching capacity and unable to make new admissions, due to patients who couldn’t be discharged. They sought to reduce those days and came up with a number of possible solutions, such as shifting to long-term managed care support services, waiver funds and federal and state dollars to provide services to these eligible individuals, and funds to place these individuals in long-term care in the community setting. Unfortunately, these solutions cannot be achieved with just the healthcare systems. State and local partners need to step up, he said.

All health systems try to plan for the population that’s coming, but Renown Health, based in Reno, Nevada, has taken it to another level through a partnership with the Desert Research Institute (DRI) and the DNA testing company 23andMe. The partnership was featured in an afternoon session on the meeting’s first day.

Renown faces unique planning challenges—it’s the only tertiary facility for a vast 11-county area the size of New York, New Jersey, and Pennsylvania. Vast stretches have almost no people, and those who do live in the area have higher-than-normal rates of pancreatic and liver cancer, as well as high age-adjusted death rates for cardiovascular disease.

DRI gathers immense amounts of environmental data, which, combined with genetic data, could give Renown insight into what types of diseases it could expect in the future—which would aid hiring and budget decisions, according to Anthony Slonim, MD, DrPH, Renown’s president and CEO and the ACO Coalition chair. “It was the ultimate strategic planning process,” he said.

When the partnership launched in September 2016, Renown was not entirely sure what to expect when it opened enrollment for people to have their genetic information collected and tested by 23andMe. Within 48 hours, 10,000 individuals had signed up.

Joe Grzymski, PhD, senior director of Applied Innovation at DRI, said a great feature of the partnership is that the participants get information back from 23andMe, and will have the opportunity to modify behavior based on the results. “This is something that we hope will end up going back into the community through investments in everything from rural access to care to new research and development given to our own specific problems,” he said.

Sharing data to improve population health is a foundation of an accountable care organization (ACO). But sharing such data comes with risks, and each instance of a new provider, patient portal, wearable device, or contractor logging into the system creates an opportunity for trouble.

“All these different streams and connection points add to the potential risk of any of this data being hacked, or having a breach,” said Lee Barrett, the executive director for the Electronic Healthcare Network Accreditation Commission, who gave an overview of the new risks for ACOs under the Medicare Access and CHIP [Children’s Health Insurance Program] Reauthorization Act (MACRA) before joining the panel, moderated by Slonim.

With 93 major attacks reported to HHS’ Office of Civil Rights, 2016 was a record year for data breaches to health systems, Barrett said. The panelists, who included Dan Konzen, campus chair for College Information Systems and Technology at the University of Phoenix, and Dan Hurley, vice president of Information Technology at Solera Health, agreed that cyberattacks will happen—it is simply a matter of how well-prepared health systems are and whether they are ready to respond.

Understanding liability for third parties is a huge concern as ACOs connect with more and more partners; as Konzen explained, the health system is responsible for what a third party does. The panelists said it is a more than a matter of “checking the box,” as health systems must have an ongoing system of education and documentation that they have a protection plan—and that they follow it so if a breach occurs, they can show they took steps to prevent it. Using third parties to help test your system is important, Hurley said.

Lakdawalla’s keynote address, “An Economic Perspective on Value Frameworks,” opened the second day. He began with the one part of healthcare that brings people together: the broad agreement that prescription drugs cost too much, and that the government should do something about it. Even Republicans agree, which for decades was not true, Lakdawalla said.

Enter the discussion of value, and the tricky part begins. “It’s never clear what people mean by value,” he said, and that is what makes the transition to a value-based pricing system to challenging.

“Even if we had a single-payer system in the United States, this would still be an issue,” Lakdawalla said. “Patients often view the world very differently than a single payer would.” Health plans measure value based on the mean effect of a drug across a population, but a patient is looking at the outlier—and hoping he’s the one.

Value frameworks allow health systems to assign weights to a drug’s attributes, giving a way to score its cost, survival benefits, and side effects, but Lakdawalla also said it is important to realize that these frameworks include assumptions, and some of the things most important to patients are the hardest to measure.

With the public paying for so much of healthcare, politics enters the discussion. “The problem is that payment is divorced from the consumption” of healthcare in the United States, he said. “In some sense, we are all the payers, the premium-paying beneficiary, the taxpaying voter. That’s where I would start the discussion.”

Next up was Avik Roy. A conservative who had advised 3 Republican candidates for president, he had spent the night before making the TV rounds after the House vote, and he was on tap to make another appearance that afternoon, with an engaging discussion with the ACO Coalition audience somewhere in between. Roy agrees that the consumer is too far removed from healthcare purchasing decisions—and that a system that started with good intentions became completely insensitive to costs. “We have all the inefficiencies of a highly public system without any of the actual coverage gains that one would expect from a truly government system,” he said.

Thus, although Republicans complain about the added costs of the ACA—and the law does add around $100 billion in 2018&mdash;that’s not the real problem. Here, Roy wowed the audience by taking the slide that shows the spending on ACA, and overlaying the “legacy” federal spending on healthcare&mdash;and the bars just kept climbing and climbing, like a skyscraper, all the way to $2.025 trillion.

“If the goal of conservative health reform is to really make the healthcare system in America fiscally sustainable, it can’t just be about Obamacare,” Roy said. “It has to be about reforming Medicare and Medicaid as well.”

His 4 key steps to reform are: 1) fixing the flaws of the exchanges to avoid premium spikes while keeping guaranteed issue, 2) raising the eligibility age of Medicare, 3) privatizing and restructuring Medicaid, and 4) creating market reforms to rein in drug prices and hospital consolidation.

Panelists with different views of the ACA joined Roy for a discussion, including Sally Pipes, president of the Pacific Research Institute; Cheryl Gardner, of beWellnm, the New Mexico exchange, and Ninez Ponce, MPP, PhD, of the University of California, Los Angeles.

Gardner said 880,000 people are enrolled in Medicaid in her state, but access is becoming an issue—and Pipes, who grew up in Canada, said the same was true there. “We reduced uninsurance, but they can’t get care,” Gardner said. By contrast, when she was working under the “private option” in Arkansas&mdash;which used federal dollars to let people buy private coverage&mdash;people had better access.

Ponce said in California, Medicaid expansion has driven uninsured rates to an all-time low, and fewer people are delaying care due to cost.

Roy said a system that gives coverage to everyone who wants it is possible. He doesn’t believe in the individual mandate, but he does think everyone who wants insurance should be able to afford it—it should be a private system, with subsidies based on income level.

Moving Toward 2020, and Treating the Whole Patient

The 2020 Panel Discussion series, a forward-looking panel that concludes each ACO Coalition meeting, brought together Michael Griffin, MS, CEO of the Daughters of Charity Services of New Orleans; Jonathan Hirsch, MS, founder and president of the precision medicine company Syapse; and Sachin Jain, MD, MBA, FACP, the CEO of CareMore.

Moderated by Goodman, the discussion explored how health systems must look beyond the walls of the clinic to take on the social and behavioral problems that drive up rates of diabetes, obesity, and dementia. These “social determinants of health” cannot be ignored if systems are going to take on risk, and they force systems to deal with issues of intergenerational poverty, transportation, and even loneliness among seniors, which CareMore is now addressing.

Griffin said the launch of Medicaid expansion in New Orleans has been a game changer—enrollment statewide by early May was 432,000, well ahead of the 375,000 projected for the first year. Griffin is struggling to replace the value-based payment systems introduced after Katrina under a $100 million federal grant designed to restore healthcare infrastructure after the city lost Charity Hospital, which had provided primary and emergency care to the poor for generations. Now, he’s working with the state’s managed care organizations one by one to restore per member, per month payment contracts, and has succeeded with 2.

“We’ve seen an increase in preventive services, focused through a private system managed for the public benefit,” Griffin said. By historic standards in Louisiana, this is revolutionary; it means the city’s poor are learning to avoid the emergency room for basic care (and Daughters of Charity gets a notice through the electronic health record if they show up unnecessarily).

Jain, too, is using tools like senior gyms and managing chronic disease in a dental chair to keep patients out of the hospital. “In the event they are admitted, we view most hospitalizations as a failure of the healthcare system,” he said.

Hirsch said Syapse is helping health systems take on risk by giving physicians data on multiple patients with similar conditions, so they can see how different treatment methods for a similar condition worked—or didn’t. The use of data simply speeds up what doctors would do on their own&mdash;make decisions based on experience with similar situations. “Physicians are pattern recognizers,” he said. “The issue here is, we’re reaching a scale and complexity of data they could not reach on their own.”

Goodman concluded the session by asking how the AHCA, which was passed during day 1 of the meeting by the US House of Representatives, might affect the panelists going forward. Griffin said if it limited access to care, he was not optimistic.

“We need to focus on the high costs of high-need complex patients,” Jain said. “That’s about as nonpartisan an issue as there is.”

Coverage from the 2017 Spring Live Meeting of the ACO & Emerging Healthcare Delivery CoalitionÂ® on May 4-5, 2017, in Scottsdale, Arizona.

Defining Value for Better Decision Making: Lessons From the Spring ACO Coalition Meeting

Telemedicine technology promises to improve patient outcomes, lessen the burden of visits on patients, increase access to physicians, increase physician efficiency, and decrease costs. However, it must be incorporated into medical practice safely without impeding patient—doctor interactions. This will require an effective triage system, efficient physician workflow, and a HIPAA-compliant communication method, among others. Payment reform is a barrier to more widespread adoption: physicians must be able to care for their patients while being reimbursed. Technologies that enable real-time access to medical records, have the ability to capture high-quality images, and supply a comprehensive medical history will help further the success of telemedicine.

The American Journal of Accountable Care. 2017;5(2):24-26

 We are practicing in a time of rapid technological innovation. Telemedicine technology not only exists, but is increasingly being welcomed by healthcare consumers. It is imperative, however, that this technology be incorporated into medical practice in the safest and most efficient ways possible. Knowing how exactly, with whom, and by whom this technology should be used to improve patient outcomes while avoiding harms is an important area of inquiry.

By 2025, a significant shortfall in physicians is expected. Although the shortage in primary care physicians has been most publicized, according to the American Association of Medical Colleges, a shortfall of 30,000 to 64,100 non—primary care physicians is also projected.

 Increasing mid-level provider supply and responsibility is underway, but this will certainly meet a ceiling as well and cannot be the only solution.

Technology, in the form of giving patients access to their labs, electronic messaging, and electronic prescribing, has been shown to reduce the need for in-person visits.

 Traditional electronic health record (EHR) messaging and telephone encounters have their limitations, however, as mobile access to full EHR capabilities is often not available and sending information and pictures to mobile devices is not reliably secure. One promise of EHR-based telemedicine is that it could create visits that may increase physician efficiency. A study of telemedicine utilized for retinopathy of prematurity reported similar outcomes at lower cost than traditional ophthalmoscopy.

 The results of a British study demonstrated remarkably good diagnostic accuracy in rheumatology telemedicine visits; notably, these study patients were not randomized, but selected to participate in this study.

Whether or not these visits took less time on the part of the physician was not clear. However, office visits can be inefficient from the perspective of a patient who has to take time off of work, perhaps find childcare, and spend much of his or her time waiting for the doctor. For rural patients, the needed subspecialist may be an hour or more away. Taking technology one step further, in the form of real-time electronic visits, may not only help to increase timely access to a primary care or sub-specialist physician, but make that telemedicine visit much more efficient for the patient. Importantly, there is evidence to suggest that patients with certain chronic conditions who are engaged with telemedicine technologies have moderately improved outcomes.

Most of the telemedicine literature that has demonstrated successful outcomes has carefully selected patients and/or conditions for which the technology is being used. However, these results serve to caution that telemedicine cannot always work for all patients at all times from any healthcare provider. In addition, as with in-person visits, patients must know who is delivering their care. Done properly with high-quality images and complete histories, telemedicine applications may be well suited for conditions such as rashes, acne, or postexcision care in established dermatology patients. Chronic disease management through telemedicine in the areas of eczema or psoriasis, for example, represents a promising area for care delivered through telemedicine applications.

Further, if a stated aim of telemedicine is to increase physician efficiency, electronic visits need to be organized with a specific workflow that does so, just as an office visit is. For telemedicine to be successful, electronic visits should have ancillary medical staff to triage the visit to ensure that only patients who can be safely managed electronically are scheduled for a telemedicine consultation, just as they would be for an office visit. In addition, the staff member or the telemedicine system itself should be able to ensure that the initial medical history is filled out, medication lists and allergies are updated, and the photographs submitted are of high quality. An electronic appointment or expectation of when the doctor will be able to get back to the patient should also be set so that there are no frustrations created before the visit has occurred.

During the telemedicine consultation, it is important that the patient—doctor interaction is not impeded. Whether done in real time or by store-and-forward technology, where the patient and physician send messages back and forth, both patients must have a HIPAA-compliant method to communicate so the process of history-taking can contribute to making the correct diagnosis. Further, it is vital that the physician is able to ensure that the patient understands what was communicated and is able to follow through with the treatment plan.

Although there clearly exists an online marketplace for healthcare services, telemedicine services provided by traditional medical offices—which may have a longitudinal relationship with patients and are therefore able to leverage knowledge of the patient and continuity of care&mdash;are hindered by lack of payment reform around this technology. According to the American Telemedicine Association, only 22 states and the District of Columbia have mandated no provider, technology, or patient setting restrictions for insurance reimbursement.

 The 28 remaining states have myriad rules from various insurers that will present a challenge to physicians who attempt to bill for their services. Because of reimbursement uncertainty, the patients who are able to most easily access telemedicine services are the ones who are billed directly for them out of pocket. Physicians who use telemedicine technologies in their practice will need to pay close attention to the changes in reimbursement laws in their states.

In states where telemedicine is a noncovered entity, patients may be willing to pay out-of-pocket for timely diagnosis and treatment to avoid missing work hours, having to find childcare, or driving to the clinic. The key to physicians being able to offer telemedicine services rests in their ability to achieve good patient care while being reimbursed for their time. Technologies that provide patients and their physicians with real-time access to the medical record, have high-quality image capture ability, and provide the means to supply a comprehensive medical history will help further the success of telemedicine.

Once they are confident that they have the technology and the ability to be reimbursed for their services, medical practices will need procedures in place to handle appropriate triaging and vetting of telemedicine encounters. The ability to communicate with the patient’s primary physician and potentially escalate the telemedicine visit to an in-person visit if needed will also be imperative to seamlessly integrate telemedicine into medical practice.

 Making telemedicine accessible ultimately relies on a partnership between payers, physicians, and technology companies with the attainable goal of improving practice efficiencies and patient outcomes. 

 Charles E. Schmidt College of Medicine, Florida Atlantic University (DS), Boca Raton, FL; Modernizing Medicine, Inc (MS), Boca Raton, FL.

 Dr Dawn Sherling is a stock owner and Dr Michael Sherling is the co-founder and chief medical officer of Modernizing Medicine, a healthcare software company that provides electronic medical records and telemedicine solutions to physicians and medical practices. The authors report no other relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (MS); acquisition of data (DS); analysis and interpretation of data (DS); drafting of the manuscript (DS, MS); critical revision of the manuscript for important intellectual content (DS, MS); and supervision (MS).

Michael Sherling, MD, MBA, Modernizing Medicine, Inc, 3600 FAU Blvd, Boca Raton, FL 33431. E-mail: Michael.sherling@modmed.com. 

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2. Jackson KM, Scott KE, Graff Zivin J, et al. Cost-utility analysis of telemedicine and ophthalmoscopy for retinopathy of prematurity management. 

. 2008;126(4):493-499. doi: 10.1001/archopht.126.4.493.

3. Leggett P, Graham L, Steele K, et al. Telerheumatology—diagnostic accuracy and acceptability to patient, specialist, and general practitioner. 

4. Dellifraine JL, Dansky KH. Home-based telehealth: a review and meta-analysis. 

. 2008;14(2):62-66. doi: 10.1258/jtt.2007.070709.

5. Thomas L, Capistrant G. State telemedicine gaps analysis: coverage and reimbursement. American Telemedicine Association website. https://thesource.americantelemed.org/viewdocument/state-telemedicine-g-2. Published January 2016. Accessed June 10, 2016.

6. Position statement on teledermatology. American Academy of Dermatology website. https://www.aad.org/Forms/Policies/Uploads/PS/PS-Teledermatology.pdf. Published March 7, 2016. Accessed June 17, 2016.

Telemedicine technology promises to improve patient outcomes and increase physician efficiency. Adoption depends on physicians’ ability to safely care for patients while being reimbursed.

The Promises and Pitfalls of Telemedicine

Community and population health are 2 similar yet distinct approaches to promoting the public’s health through the use of upstream practice and prevention strategies. Recent discussion among the healthcare community has noted conceptual confusion in the means by which each term and their respective strategies are utilized in areas of patient care, public health, and research.

 Given the importance of these terms for those who run accountable care organizations (ACOs), we felt it was necessary to attempt to bring clarity to the understanding of each concept by providing an overview of select definitions from the public health literature and highlighting select areas where we see distinction and overlap. Finally, we provide a model that integrates both conceptual approaches to optimize health in the communities and patient populations we serve.

Community and Population Health: Overview

 reviews select definitions from the public health literature that are used to describe both community and population health.

Community health is generally rooted in the collective efforts of individuals and organizations who work to promote health within a geographically or culturally defined group.

 Community health initiatives function as “multi-sector and multi-disciplinary collaborative enterprises”

 that use evidence-based strategies to “engage and work with communities, in a culturally appropriate manner.”

 The progress and success of these initiatives originate from the community members, who are collectively empowered to address self-identified vulnerabilities (eg, education, employment, public safety). In other words, the community and its relevant characteristics are—in and of themselves&mdash;considered to be “an essential determinant of health”

 for each individual who is part of, or becomes affiliated with, a community’s given membership.

Population health, alternatively, uses an outcome-driven approach to “manage” health for a specific group of individuals, typically defined by attribution.

 These interventions involve the tracking and measurement of “health status indicators”

 (eg, high blood pressure, cholesterol) within these groups to provide insight and direction on how to best prevent the onset or future development of certain health conditions (eg, ischemic cardiac disease). Health determinants, such as healthcare access, genetics, and individual behavior, also tend to be included in this description, as they play an influential role in an individual’s history and current health status.

 Given their nature, the majority of population health interventions tend to be led by healthcare organizations, including ACOs, who have a responsibility—financial or otherwise&mdash;to report outcomes involving the patients under their management or care.

Community and Population Health: Areas of Distinction and Overlap

 provides a list of dimensions (ie, key descriptors, populations targeted, interventions utilized, and measurement) that were selected to further highlight areas of distinction and overlap between these terms. Each of these dimensions has been outlined in further detail below.

Based on the overview provided above, there are key descriptors that may collectively describe each concept. The terms “collaborative,” “initiatives,” and “empowerment” are foundational to community health, as they emphasize the integration of multiple entities working together. Population health descriptors, on the other hand, are more generally aligned with “outcomes,” “management,” and the notion of “accountability,” as they outline the methods by which health is managed for a targeted group of individuals. Overlap amongst each strategy can be recognized in the shared mission of enhancing disease prevention through the use of upstream health promotion strategies.

Although community and population health strategies may be created to promote health for separate groups, there are cases in which a natural overlap occurs. Community health initiatives are developed by and for individuals who either live in a common geographical region (eg, shared neighborhood or city block), or demonstrate unity with regard to a shared cultural practice (eg, speak the same language, practice the same religion). Population health interventions are generally driven by healthcare organizations to target health in patients whom they may be held accountable for (eg, members of a health plan, individuals categorized by a health status indicator). Overlap among each strategy can be recognized when members of a given community (eg, neighborhood in Baltimore County, Maryland) are also members of a prioritized patient population (eg, individuals who suffer from mental illness) within a given health plan (eg, Health Plan B).

Community and population health programs may be utilized to target similar health outcomes, yet their strategies for implementation often differ. In community health, broader-scoped initiatives are generally introduced to reduce health inequities involving the social determinants—or the circumstances in which people are born, grow, live, work, and age.

 Examples of community health programs may include actions to improve education, reduce unemployment, or enhance a community’s built environment. Population health interventions, alternatively, are typically developed with a more specific focus to promote health in a prioritized or “at-risk” population. Examples of population health programs may include efforts to increase the frequency of child vaccinations, reduce the rate of teen pregnancy, or enhance smoking cessation for adults with chronic obstructive pulmonary disease.

Given the nature of the groups they serve, community and population health interventions may overlap in their exposure to certain individuals. As an example, consider Maria Corral who lives in City Block A and is a beneficiary of Health Plan B. In January, a community health program was introduced to provide education on dense breast tissue for women who live in City Block A. Also, during that time, a population health intervention (eg, health plan incentives) was used to increase the number of breast cancer screenings for women older than 40 years who are members of Health Plan B. This interaction of both community and population health efforts will likely increase the probability that Corral schedules and completes her preventive screening appointment and, as a result, further reduces her risk of late breast cancer detection.

The methods and necessity by which community and population health is measured and evaluated contrasts for both conceptual models. Community health can be broadly examined with respect to the overall health and interactive health efforts of its community members. As an example, the Community Tool Box, developed at the University of Kansas, provides a framework to assess community health based on 2 basic premises: the community’s comprehensive view of health and its commitment to health promotion.

 The former is examined with respect to prerequisites such as shelter, education, food, income, and equity that exist within the community. The latter is considered with respect to the steps taken to improve and sustain a community’s well-being, such as building health policy, creating supportive environments (natural, built, economic, social, etc), and strengthening community action.

The impact of individual community health initiatives may not need to be rigorously evaluated. Individuals who participate in many of the community health programs (eg, vaccination education) often do so voluntarily, and are not required to provide any follow-up data after the program’s completion (eg, 30-, 60-, 90-day reports). Measurement, in these cases, can be assured at the broader community level. An alternative measurement strategy, for example, might involve the consideration of community trust, their understanding of distributed findings from a recent study, or the improvement of environmental conditions in which the community lives. In either case, community health initiatives must be measured and evaluated such that results may be considered at the broader community level.

Unlike community health, population health is grounded in the science of epidemiology, or the measurement and comparison of risks and health outcomes in different patients over time. Therefore, the measurement of population health interventions requires more rigor in the tools and methods used for evaluation. The management of population health data can be used to determine: a) the types interventions introduced to priority populations and b) the impact of an intervention on a targeted health outcome. As important, is the consideration of intermediate outcomes, which can be managed early on, to prevent the advancement in future health conditions. The management of obesity or a high body mass index, for example, may influence the future risk of developing diabetes or hypertension.

Healthcare organizations that use population health interventions must be able to evaluate healthcare outcomes to ensure they are providing their patients with valued care. ACOs, for example, which aim to drive value in healthcare by improving health quality and reducing healthcare costs, have an assured responsibility to optimize patient health through the use of effective health interventions and strategies. Without the critical ability to track, evaluate, and benchmark patient outcomes, ACOs would be unable to demonstrate, or be held accountable for, the valued care they provide to the patients and families they serve.  

An overlap between community and population health strategies may be observed when community health initiatives are used to enhance the measurement and assurance of health outcomes in the management of population health. Nonprofit health systems serve as an important platform for this interaction to occur. Given the tenets under which they operate, nonprofit hospitals are required to provide community benefit through both community-building strategies (eg, creating physical and environmental improvements, supporting economic development) and improvement of health services (eg, hospital subsidized programs to provide free or reduced-cost clinical care).

Integrating Community and Population Health

 provides an example of how nonprofit health systems may capitalize on the use of an integrated community and population health approach to meet current regulatory requirements, and most effectively deliver health to the communities and patient populations they serve. Figure 1 panel A, lists the regulatory requirements under which nonprofit hospitals must provide community benefit to maintain their 501(c)(3) tax-exempt status.

 Section 501(r), introduced via the enactment of the Affordable Care Act, requires that this benefit also be explicitly and publicly demonstrated by conducting a Community Health Needs Assessment (CHNA) at least once every 3 years.

 Figure 1 panel B shows how the CHNA can yield a Community Health Improvement Plan (CHIP), which identifies the top priorities and strategies for health improvement within that particular community (eg, increase healthcare access, increase healthy behaviors, and increase preventative care strategies).

Although there may be variation in the methods by which each nonprofit healthcare system addresses the identified unique community needs, they will most likely share a common foundational approach for doing so. As an example (Figure 1 panel C), the nonprofit healthcare organization might work to develop a strategic plan, identify priorities and goals, establish partnerships, implement initiatives, and measure progress. The partnership component of this approach plays a particularly important role, as the methods by which each initiative is targeted will be navigated by the clinical-, community-, and population health—focused partners involved.

Figure 1 panel D provides an example of how community and population health programs can be integrated to improve health in the targeted community CHIP areas.

Following the introduction of community and population health interventions, an evaluation of their combined impact may be conducted at the population health level. Targeted health outcomes, such as decreasing obesity rates and increasing immunizations, for example, can be evaluated with respect to the prior health histories of each targeted patient population (eg, overweight adults and children, respectively). Assurance of this progression must also be considered, as the nonprofit hospital driving these initiatives will need to file an annual report (ie, Schedule H, Form 990) of recent progress toward meeting the identified health priorities (Figure 1 panel A). This includes, but is not limited to, providing a review of what the CHNA described, indicating whether or not the information has been distributed to the community, and listing what has been done to improve the identified needs.

Community and Population Health: A Case Study Model

The imperative for nonprofit healthcare organizations to demonstrate how they provide benefits and deliver health to the patients and communities they serve has never been greater. Now, more than ever before, there is a growing number of health systems across the country that have had their tax-exempt status challenged because of a failure to meet 501(c)(3) requirements.

 Morristown Medical Center, within the Atlantic Health System in New Jersey, is one recent example where a nonprofit hospital was asked to back-pay property taxes from 2006 to 2014.

A model currently being used by Renown Health to help drive community benefit in Washoe County, Nevada, involves the integration of community and population health strategies to create health “Institutes.” In this model, an Institute serves as a decentralized and partnership-based initiative that works to leverage and coordinate regional expertise to improve health in a prioritized patient population. Tactical centers within each Institute then work to deliver support and solutions for these populations in 5 core areas: care delivery, social justice, advocacy, education, and research. Community benefit from the combined efforts can then be evaluated with respect to reported changes in disparities found between the targeted groups’ overall health and wellness compared with local, regional, or national averages.

To best deliver benefit to the Washoe County community, Renown Health uses the Institute model to target 3 priority populations: children (birth to 18 years), seniors (55 years or older), and individuals who suffer from mental illness and addiction (all ages). The Child Health Institute (CHI) targets socioeconomic factors and areas of healthcare delivery influencing pediatric health. The Healthy Aging Institute (HAI) works to build safe and supportive community environments to enrich the lives and well-being of seniors. The Behavioral Health Institute (BHI) strives to create an emotionally resilient and thriving community by addressing the mental health and additional needs of all individuals. Although the focus of each Institute is specialized to address the nuances of a targeted population, the shared used of a common tactical strategy helps to promote collaboration and seamless support for individuals who fall into more than 1 area (eg, a senior with mental illness).

 provides a visual representation of how each Institute model is used to impact health within its targeted population. To begin, the target population/individual is strategically placed at the center. Using the Institutes at Renown Health, as an example, these populations/individuals include children (CHI), seniors (HAI), or individuals who suffer from mental illness or addiction (BHI) within Washoe County. Moving outward, the bi-directional arrows on the left illustrate the environments in which an individual’s health may be affected and influenced during decisions in their daily life, including community group, work, school, and emergency service environments. To target health beyond the individual and create impact at the broader community level, community health initiatives (eg, exercise or nutrition programs) may be introduced in similar environments through the community. Also, given the nature of community health initiatives, the individuals who benefit from these community health programs (eg, walking programs at work) may have played a role in their development.

Next, located to the right of the target population in Figure 2, are the methods by which an individual receives healthcare. As a nonprofit health system, healthcare is delivered to patients via primary, acute, transitional, emergency, or specialty care services. The bi-directional arrows are provided to illustrate the essential interactive nature of patients and families throughout this healthcare delivery process. By engaging patients and their families with pertinent healthcare decisions, healthcare providers will likely see greater compliance and, as a result, greater improvements in the patients’ overall health outcomes. Further, to best manage similar outcomes at the greater population level, population health management interventions (eg, flu vaccinations) may be utilized during and between visits within the nonprofit health system.

Finally, located along the bottom of Figure 2, is the given health Institute. Each Institute serves as integral coordinating entity, which combines community and population health strategies, to most effectively promote engagement, advocacy, support, and access for the Institutes’ target population. For the CHI, this may include connecting caregivers and families with community resources, establishing a pediatric residency with the local medical school, building partnerships with social services, or working to create healthcare access for children without homes. Strategies for the HAI may include building community structures to support active senior living, partnering with local organizations to provide opportunities for social interaction, or delivering education to families about how to best care for their aging family members. Lastly, for the BHI, objectives may be formed to integrate mental health and addiction care into regular primary care services, measure and leverage outcomes research for ongoing improvement, integrate best practices with partners who provide recovery-focused housing, or develop psychoeducation materials for the community and general public.   

Community health and population health are 2 similar yet distinct concepts that attempt to enhance health in a defined group of individuals. Each strategy is uniquely valuable and, when integrated, can provide significant benefit to the community. Through the use of innovative strategies that incorporate community health initiatives and population health interventions, nonprofit hospitals and ACOs will be able to most effectively deliver health in the communities and patient populations they serve. 

Renown Health (ADS, AMM), Reno, NV; University of Nevada, Reno, School of Medicine (ADS), Reno, NV.

 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 (ADS, AMM); drafting of the manuscript (ADS, AMM); critical revision of the manuscript for important intellectual content (ADS, AMM); administrative, technical, or logistic support (ADS); and supervision (ADS).

Amber M. Maraccini, PhD, Renown Health, 50 W. Liberty St, Ste 1100, Reno, NV 89501. E-mail: AMaraccini@renown.org.

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This paper aims to bring clarity to the conceptual confusion between community and population health, which currently impacts progress in both research and clinical practice.

June 2017

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