Gary E. Rosenthal, MD

The learning health system (LHS) has gained traction as a powerful framework for improving the cost and quality of healthcare. The goal of an LHS is to systematically integrate internal data and experience with external evidence so patients receive higher-quality, safer, and more efficient care. However, if the goal of an LHS is to improve health, as well as healthcare, it must account for and mitigate the negative impact of social and economic factors on health, known as the social determinants of health. In this paper, we discuss the critical role the LHS can play in addressing patients’ social risk factors. We also discuss how integrating data on the social determinants and activities to reduce patients’ social risk factors could advance the mission of the LHS to enhance patient engagement, improve the delivery of personalized care, and more accurately evaluate the effectiveness of care. Without the collection and integration of data on the social determinants of health, the LHS may fail to reach its full potential to improve health and healthcare.

The learning health system has emerged as a framework for improving healthcare delivery, but it may fail to reach its full potential if it does not incorporate data on social determinants of health or activities to reduce social risk factors.

If a central goal of the learning health system is to improve health, it must mitigate the negative effects of social determinants.

Incorporating social determinants could enhance patient engagement, allow for tailoring of care, and more accurately evaluate the care experience.

A learning health system that is able to rapidly evaluate and improve care could be an ideal setting to pilot interventions to reduce patients’ social risk factors.

The learning health system (LHS) has emerged as a powerful framework for improving healthcare delivery.

 The Institute of Medicine (IOM) defined an LHS as “one in which science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the care process, patients and families active participants in all elements, and new knowledge captured as an integral by-product of the care experience.”

 An LHS can improve care at multiple points along a patient’s clinical trajectory by providing clinicians real-time access to the best available evidence, partnering with patients and their families, incentivizing high-value care, and creating a culture of continuous learning.

 Ultimately, this could lead to a patient-centered LHS on a national scale that allows data sharing across health systems and informs decisions that improve population health and reduce health disparities.

The LHS framework is beginning to take shape, and we believe that realizing its full potential is linked to a health system’s ability to effectively account for and mitigate the social and economic factors that affect health. Social determinants of health (SDH)—the conditions in which people are born, grow, live, and age&mdash;have a profound impact on morbidity and mortality. SDH can lead to social risk factors (eg, food insecurity, housing instability) that can negatively affect health.

 Historically, reducing social risk factors has been the focus of public policy and public health, not healthcare. Increasing attention has focused on health systems’ potential to reduce patients’ social risk factors.

 The National Academy of Medicine (NAM; formerly the IOM) has recommended a core set of social domains that health systems should document in the electronic health record (EHR).

 Increasing research has also shown that addressing patients’ unmet social needs in clinical settings can improve patients’ access to resources, which, in turn, may improve health outcomes and decrease costs.

 With this research as a backdrop, CMS implemented the Accountable Health Communities model to test whether addressing unmet social needs reduces healthcare utilization.

Eliminating the health disparities that emerge from SDH will require broad social, cultural, and policy changes.

 Nonetheless, health systems have an important role in improving health equity, and the LHS could fail to reach its mission of higher-quality, safer, and more efficient care if such systems do not integrate data on SDH or activities to reduce social risk factors.

In this paper, we discuss the role the LHS should play in addressing social risk factors. We further discuss 3 areas—patient engagement, personalized care, and effectiveness of care&mdash;in which the LHS is unlikely to reach its full potential without integrating SDH data.

Role of the LHS in Reducing the Impact of Social Risk Factors

If the goals of the LHS are to improve health and provide more efficient healthcare, it must mitigate the negative effects of social risk factors. Several lines of reasoning support this. First, a large body of evidence indicates that SDH have a greater impact on health than healthcare does. Even if health systems provide optimal healthcare for every patient, only a small improvement in population health would likely be seen.

 Second, although the United States spends more on healthcare, it ranks last among other developed nations for a number of important health outcomes, potentially due to lower levels of spending on social programs.

 Because the United States lacks broad bipartisan support for expansion of public health and social programs, health systems must build new partnerships with social services if they are to improve population health. Third, the drive to value-based purchasing, the emergence of accountable care organizations, and the creation of financial incentives for controlling expenditures have established a strong business case for the LHS to address the social factors that lead to increased utilization.

Although research on the effect of reducing social risk factors in clinical settings on healthcare utilization and health outcomes is limited, early results have shown promise.

 One example is Hennepin Health, which used care coordinators, social workers, and community health workers to connect patients to social services and found a 9% decrease in emergency department visits.

 Another study found that identifying and addressing patients’ unmet social needs using Health Leads, a community-based healthcare organization that connects patients with resources, led to small but significant improvements in blood pressure and cholesterol.

How Incorporating Data on SDH Could Advance the Mission of the LHS

A major goal of the LHS is to improve patient engagement so that patients have a more active role in the shared decision-making process. Understanding the social circumstances in which patients live is central to this goal. For example, advising a patient to increase physical activity when the patient does not have a safe space in which to exercise is unlikely to be effective. In one study that screened patients for social needs, clinicians reported that this information changed care delivery for almost a quarter of patients with an identified need and changed their interactions with more than half.

 However, few clinicians screen patients for unmet social needs, and there are limited data on how practitioners can incorporate interventions in clinical settings to reduce social risks.

 The LHS offers an opportunity to address these gaps by partnering with patients and the community. For example, to improve educational materials about cancer screening for patients with low health literacy and facilitate physician-patient communication, this social risk—informed care could utilize an LHS community model in which community members are involved in the development process.

In 2015, the National Institutes of Health launched the Precision Medicine Initiative (PMI) to understand the drivers of health and develop personalized treatments. A central element of the PMI is the All of Us Research Program, which plans to enroll 1 million adults to investigate how differences in genomics, environment, and lifestyle can contribute to the development of precision medicine treatments.

 Although not in the original LHS framework, the NAM published a 2015 workshop summary describing the inclusion of genomic data in the LHS.

 The LHS offers an opportunity to understand how integration of genomic data affects healthcare delivery.

 As an example, Geisinger Health is integrating individual genomic data within the EHR to inform patient care about disease risk.

 However, social risk factors often have a greater influence on disease occurrence than genetic risk factors. For the genomics-enabled LHS to provide precision medicine, it must account for these social factors that affect health and healthcare.

 Further, an LHS that combines data on individuals’ genomics and social risk factors can better elucidate causal pathways leading to health disparities and tailor the delivery of care.

A further goal of the LHS is to close the gap between evidence generation and clinical care, particularly the lack of generalizability of randomized controlled trials.

 One method to accomplish this is through pragmatic trials that evaluate the effectiveness of treatments in real-world settings. The LHS, with its EHR and robust data collection systems, is an ideal setting for pragmatic trials.

 However, without accounting for patients’ social factors, evaluating the effectiveness of treatments in the LHS could be incomplete. For example, one study evaluated the effect of neighborhood poverty on the impact of a primary care practice—based trial for depression. The authors found that the treatment effect weakened after the initial 4 months among participants living in high-poverty neighborhoods but not among participants in other neighborhoods.

 Thus, the failure to account for social risk factors could lead to effective treatments and novel healthcare delivery strategies showing minimal benefit as a result of unmeasured SDH. Similarly, collecting SDH data could shed light on ways in which social factors mediate the effect of treatments on health.

 Intermountain Healthcare is an LHS that is using an area-level deprivation index to evaluate heterogeneity in medication adherence among patients with hypertension.

The LHS is a powerful framework for improving the cost and quality of healthcare. However, realizing the full potential of the LHS will require moving beyond the traditional boundaries of healthcare and developing innovative approaches to reduce social risk factors. Although the evidence base for the effectiveness of such approaches on improving health is currently limited, early results suggest that addressing patients’ unmet social needs can decrease costs and improve outcomes. We believe it is imperative that the LHS add to this evidence. An LHS that is able to rapidly evaluate and improve care could be an ideal setting in which to pilot interventions reducing social risk factors. This process should begin with the routine collection of SDH data. Making these data a part of the LHS should enhance patient engagement, allow for tailoring of care, and more accurately evaluate the care experience.

 Department of Internal Medicine (DP, DPM, GER), Department of Pediatrics (DP), and Division of Public Health Sciences (DP, DPM), Wake Forest School of Medicine, Winston-Salem, NC.

 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 (DP, DPM, GER); drafting of the manuscript (DP, DPM, GER); and critical revision of the manuscript for important intellectual content (DP, DPM, GER).

 Deepak Palakshappa, MD, MSHP, Wake Forest School of Medicine, Medical Center Blvd, Winston-Salem, NC 27157. Email: dpalaksh@wakehealth.edu.

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Articles

Without integrating data on the social determinants of health, the learning health system could fail to reach its mission of higher-quality, safer, and more efficient care.

Advancing the Learning Health System by Incorporating Social Determinants

 To examine the receipt of colonoscopy through the Veterans Health Administration (VA) or through Medicare by older veterans who are dually enrolled.

The VA Outpatient Care Files and Medicare Enrollment Files were used to identify 1,060,523 patients 65 years and older in 15 of the 22 Veterans Integrated Service Networks nationally, who had 2 or more VA primary care visits in 2009 and who were simultaneously enrolled in Medicare. VA and Medicare files were used to identify the receipt of an outpatient colonoscopy. Patients were categorized as receiving care in community-based outpatient clinics (CBOCs) (n = 601,337; 57%) or VA medical centers (n = 459,186; 43%) based on where most patient-centered encounters occurred. Analyses used multinomial logistic regression to identify patient characteristics related to the odds of receiving a colonoscopy at the VA or through Medicare.

Patients had a mean age of 76.9 (SD = 7.0) years; 98% were male, 89% were white, and 21% resided in a rural location. Overall, 100,060 (9.4%) patients underwent outpatient colonoscopy either through the VA (n = 33,600; 35.5%) or Medicare providers (n = 65,716; 65.5%). The adjusted odds of receiving a colonoscopy from Medicare providers were higher (P <.001) for patients who were male, white, receiving primary care at CBOCs, and for residents of an urban location. The receipt of colonoscopy through the VA decreased dramatically by age; for example, the odds of colonoscopy by the VA in patients aged >85 years and 80 to 84 years, relative to patients aged 65 to 69 years, were 0.26 and 0.13, respectively. In contrast, the receipt of colonoscopy through Medicare did not decline as markedly with age.

 In a national analysis of the receipt of an outpatient colonoscopy by older veterans, more veterans received their colonoscopies through CMS than through the VA. The use of colonoscopy within the VA was found to be more concordant with age-related practice guidelines.

	The principal finding is that more dually eligible patients had a colonoscopy performed by Medicare providers.

		Patients receiving primary care in main Veterans Health Administration (VA) facilities, members of minority groups, and rural residents were significantly more likely to use the VA to obtain an outpatient colonoscopy.

		The use of colonoscopy through the VA decreased with age—as expected per guidelines&mdash;although such decreases by age were less evident through Medicare, thus providing indirect evidence that the use of colonoscopy within the VA may be more consistent with current practice guidelines than the use of colonoscopy by non-VA providers.

The Veterans Health Administration (VA) is the largest integrated healthcare system in the United States. In addition to having VA healthcare eligibility, a significant proportion of veterans are also eligible for healthcare benefits through private insurers, Medicare, Medicaid, or other government programs.

 While such dual eligibility may disrupt continuity of care, it also provides veterans with increased choices, flexibility, and access to care.

 The greater access may be particularly germane for certain types of specialty care, which may only be available in larger VA medical centers, and not through most VA community-based clinics.

The failure to account for out-of-system healthcare utilization by veterans poses challenges to effective care coordination. Out-of-system utilization may also lead to inaccurate assessments of the overall quality of care received by veterans, and to inaccurate estimates of the cost and efficiency of the VA and Medicare in general. Therefore, it is important to understand the overall utilization of services by veterans and their patterns of use of VA and non-VA services.

Although prior studies have assessed factors impacting the use of VA and non-VA inpatient and outpatient services by veterans,

 little research has examined how veterans use these 2 types of care to obtain outpatient procedures such as colonoscopy.

 Thus, the current study examines how older veterans who are dually eligible for VA and Medicare benefits use VA and non-VA providers to obtain an outpatient colonoscopy. In addition to determining where veterans obtain a colonoscopy, the study sought to identify factors related to the use of VA and non-VA care, and to determine the degree to which the use of colonoscopy by VA and non-VA providers varies according to important clinical factors such as age.

Study data were derived from 4 administrative data sources. The VA Outpatient Care File (OPC) contains administrative records for all encounters in VA clinics. Data elements include demographic information, including residential zip code; clinic identifier; clinic specialty (eg, primary care, mental health); principal and secondary diagnoses codes based on the 

International Classification of Diseases, Ninth Revision, Clinical Modification 

); and up to 20 Current Procedural Terminology (CPT) codes. The VA’s fee-basis program (2009) contains data on encounters provided outside the VA health system that are paid for by the VA. The VA OPC and fee-basis files were used to identify the study cohort and services received through VA benefits.

Information on Medicare enrollment and services received through Medicare was obtained from the VA-CMS Medicare merged data files available through the VA Information Resource Center.

 The VA-CMS merged data files contain the Medicare claims of veterans who were simultaneously enrolled in VA and CMS.8 Medicare data were obtained for fiscal year (FY) 2009 for all VA patients in 15 of the 22 Veterans Integrated Service Networks nationally. For this study, information about enrollment in Medicare was derived from the Medicare Beneficiary Summary File, while the Carrier Standard Analytic File (ie, Part B) contains claims for physician services provided outside the VA that were reimbursed by Medicare. To create an FY database, Medicare records were merged with the national OPC file and the VA fee-basis care file using a scrambled social security number that is unique across these 3 primary data sources.

The eligible population was drawn from 1,466,340 VA patients 65 years and older (as of October 1, 2009) in the 15 VISNs who had 2 or more visits to a VA primary care provider during FY 2009 (October 1, 2008, to September 30, 2009). Patients were excluded if they were not enrolled in Medicare Parts A and B at the start of the year or if they were enrolled in a Medicare health maintenance organization at any time during the year. The application of these criteria yielded a final study sample of 1,060,523 patients.

The dependent variable was receipt of an outpatient colonoscopy procedure that was performed or reimbursed by the VA or that was reimbursed through Medicare in FY 2010. Colonoscopies were identified by CPT codes. Additionally, colonoscopies that were not performed for diagnostic purposes (ie, screening colonoscopies) were identified based on a previously published algorithm.

The independent variables of interest included the type of clinic where patients received primary care (ie, a VA medical center [VAMC] or community-based outpatient clinic [CBOC]), age, gender, distance between the veteran’s residence and the nearest VAMC (based on zip code centroids), residential location (urban vs rural), and comorbid illnesses (as defined by widely used 

 For patients with primary care visits at both a VAMC and a CBOC, categorization as VAMC or CBOC was based on the site where the majority of primary care encounters occurred; patients with equivalent numbers of visits (n = 14,914) were categorized as VAMC patients. We used residential zip code and census data to initially classify the location into isolated rural, rural, small city, or large city, based on the Rural-Urban Commuting Area (RUCA) codes. RUCA codes are measures of rurality that incorporate population density as well as commuting patterns

; these codes were originally developed as a census tract—based classification scheme that combines US Census Bureau Urbanized Area definitions with commuter information to characterize census tracts.

 Our study used a zip code—level approximation to census tract RUCA codes.

 The RUCA algorithm creates 30 mutually exclusive categories representing population density and proximity to nearby urban centers. We categorized the 30 codes into 4 previously defined categories: urban areas, large towns, small rural towns, and isolated small rural towns. The 2 rural categories were then grouped as “rural.”

The analysis consisted of 2 steps. First, the characteristics of patients undergoing outpatient colonoscopy through the VA and through Medicare were compared using the χ

 or Wilcoxon signed-rank test. Second, a single multinomial logit model was used to identify patient characteristics related to the odds of receiving a colonoscopy through the VA or Medicare relative to receiving no colonoscopy. The model simultaneously controlled for patient age, gender, race, residential location, comorbid conditions, and type of primary care clinic. For each variable or characteristic, the model provided the simultaneous odds of receiving a colonoscopy either through the VA or Medicare, relative to patients without the characteristic. A separate analysis was conducted that only considered screening colonoscopies and found similar results (not presented).

All analyses were conducted using SAS statistical software, version 9.2 (SAS Institute, Cary, North Carolina). The study was approved by the University of Iowa Institutional Review Board and the Research and Development Committee at Iowa City VA Medical Center.

Study participants had a mean age of 76.9 (SD = 7.0) years; 98% (n = 1,038,849) were male and 20.8% (n = 220,861) resided in a rural location. Eighty-nine percent (n = 946,923) of the sample were white, 7% (n = 74,441) were black, and 2.5% (n = 27,036) were Hispanic; information on race was missing in less than 0.1% (n = 473) of the sample. Fifty-seven percent (n = 601,337) of patients were classified as receiving primary care at a CBOC, and 43.3% (n = 459,186) at a VAMC.

Overall, 100,060 (9.4%) patients underwent an outpatient colonoscopy, and of these, 65,716 (65.5%) received a colonoscopy from Medicare providers; 33,600 (35.5%) from VA providers; and 744 (0.1%) from both the VA and Medicare. These latter patients were excluded from subsequent analyses. Nearly three-fourths of procedures (n = 73,747; 73.7%) were screening examinations. Of the screening procedures, 46,167 (62.7%) were performed by CMS providers, and 27,580 (37.3%) by VA providers.

Compared with patients who obtained an outpatient colonoscopy through CMS, patients who obtained a colonoscopy through the VA were younger (

<.001) to receive primary care through a VAMC, as well as more likely to live closer to a VAMC and to have comorbid conditions (eg, chronic obstructive pulmonary disease, congestive heart failure, cerebral vascular disease, and chronic renal disease) (

In a multinomial logit model adjusting for demographic factors and comorbidity, a number of factors were independently related to receiving a colonoscopy either through providers from the VA or through Medicare (

). Patients receiving primary care at a CBOC, relative to patients receiving primary care at a VAMC, were more likely to undergo a colonoscopy through Medicare, as were patients residing in a large town or urban area. Minority patients were more likely to receive a colonoscopy through the VA, and less likely to receive one through Medicare. Women were less likely than men to have the procedure performed by both the VA and Medicare. Interestingly, the odds of receiving a colonoscopy through the VA decreased dramatically by age (Table 2); for example, the odds of colonoscopy being performed by the VA in patients 85 years or older and aged 80 to 84 years, relative to patients aged 65 to 69 years, were 0.24 and 0.11, respectively. In contrast, these odds were 1.07 and 0.63, respectively, for receiving a colonoscopy from CMS providers, indicating that age was much less of a factor in the provision of colonoscopy through Medicare.

The current study examines the patterns of cross-system utilization of VA and Medicare services for outpatient colonoscopy among a cohort of Medicare-eligible veterans who used VA primary care services. Several findings deserve emphasis. First, there were more veterans who received their colonoscopies from Medicare providers than from the VA. Second, the relative likelihood of receiving a colonoscopy from Medicare providers was higher for veterans receiving primary care at a CBOC, relative to veterans receiving primary care at a VAMC, and for veterans residing in an urban location. In contrast, racial minorities and females had higher relative odds of receiving a colonoscopy through the VA. Finally, the receipt of colonoscopy through the VA decreased dramatically with increasing age, but such a decline was less pronounced when the colonoscopy was obtained from Medicare providers.

It is important to consider our findings within the context of other related research. For example, nearly two-thirds of the outpatient colonoscopies were performed by Medicare providers in our study. Prior studies have documented temporal increases in utilization of colonoscopy as a screening modality in the Medicare population.

 However, several studies in VA populations found that although there has been an increase in the use of colonoscopy, fecal occult blood testing remains the predominant mode of colorectal cancer (CRC) screening in the VA,

 in contrast to the findings of studies in Medicare populations. These differences may reflect greater access to and availability of colonoscopy in private sector settings, as well as differences in financial incentives.

A prior study assessing demographic variations in CRC screening in a Washington state Medicare population showed that urban residents were 10% more likely to obtain a screening colonoscopy than rural residents.

 Our data also show that urban veterans were 20% more likely than rural veterans to obtain an outpatient colonoscopy from Medicare providers. In contrast, urban residents were 15% less likely than rural residents to obtain a colonoscopy from the VA—possibly due to the higher density of gastroenterology services in urban locations. It has been found that the reliance on the VA for outpatient care was lower for dually eligible veterans from counties with a higher number of hospital beds, where choices for healthcare tend to be greater.

Our study also found that patients receiving primary care at CBOCs had markedly lower odds (0.54) of having a colonoscopy performed by VA providers compared with veterans receiving primary care at a VAMC. In contrast, patients at CBOCs had higher odds (1.23) of getting a colonoscopy from a Medicare provider. These findings are consistent with our prior work showing that CBOC patients are less likely to have procedures for CRC screening provided through the VA,

 as well as other studies showing that CBOC patients are more likely to use non-VA services.

 For instance, Liu and colleagues reported that Medicare-eligible veterans receiving primary care in CBOCs had 22% fewer VA specialty care visits and 21% more Medicare-reimbursed specialty care visits than patients receiving primary care at VAMCs.

We also found that black and Hispanic patients were more likely than white patients to obtain a colonoscopy through the VA, and were less likely to obtain one through Medicare. These findings are also consistent with studies showing that minority patients are more likely to be reliant on VA outpatient care services. In a study by Hynes and colleagues,6 black veterans were increasingly likely to rely on VA care—they were more likely to rely on VA care exclusively than were nonblacks (OR, 2.32)&mdash;as well as to rely on some form of VA care than to use Medicare exclusively.

 Previous survey research also showed that blacks, compared with nonblacks, are more likely to prefer VA to non-VA providers for their outpatient care.18 A substantial amount of research documents racial/ethnic disparities in access to healthcare in non-VA settings, with minorities likely to face greater access barriers.

 Since VA care is available without payment of an annual insurance premium, nonfinancial access barriers may help to explain our findings. This raises concerns that policy initiatives aimed at restructuring VA services may disproportionately affect minority veterans. Our further finding that women were less likely to receive colonoscopy through both the VA and Medicare is also consistent with prior analyses of Medicare data showing gender-related disparities in the use of colonoscopy.

Lastly, our data provide indirect evidence that the use of colonoscopy within the VA is more concordant with age-related practice guidelines. For instance, the odds of obtaining a colonoscopy within the VA decreased markedly with increasing age. For example: the adjusted odds of receiving colonoscopy in patients 85 years or older and aged 80 to 84 years, relative to patients aged 65 to 69 years, were 0.24 and 0.11, respectively. In contrast, these odds were 1.07 and 0.63, respectively, for patients receiving colonoscopy by CMS providers. Patient requests and preferences for colonoscopy may play a role in utilization patterns. However, current age-related guidelines for the use of colonoscopy do not explicitly consider patient requests for a procedure independent of physician judgment.

A number of other factors may drive age-related utilization patterns among patients receiving colonoscopy through Medicare, including financial incentives that do not exist within the VA. The US Preventive Services Task Force advises against routine screening in those aged 75 to 85 years and against any screening of those 85 years or older.

 Prior work from Goodwin and colleagues assessing overuse of screening colonoscopy in the Medicare population, showed that among patients aged 75 to 79 years and 80 years or older at the time of initial negative screening colonoscopy, 46% and 33%, respectively, received a repeated examination within 7 years.

 The higher rate of colonoscopy in the elderly patients is of special concern, given increased potential for complications and decreased benefit.

It is important to acknowledge several potential limitations of our study. Importantly, while our study relied on administrative data, prior studies have shown that administrative data may not reliably capture major procedures.

 In addition, information on comorbid conditions was captured from 

codes, which may lack sensitivity and specificity. Moreover, administrative data may not adequately assess severity of illness or categorize patients according to their risk for colon cancer

 and do not capture important domains, such as functional status, that may impact the use of colonoscopy. We assessed the use of outpatient colonoscopy and then stratified the data into screening and diagnostic colonoscopies using a previously established algorithm. The sensitivity and specificity of this algorithm may be limited.

 Nevertheless, our results were almost identical. The study used a cross-sectional design and did not account for colonoscopies that patients may have received prior to 2009; thus, eligibility for CRC screening was not directly examined. Similarly, we were unable to assess other factors related to the clinical appropriateness of any of the procedures. We did not have access to the data more recent than this at the time the study was conducted. Therefore, given recent changes in VA resources over the past 5 years for colonoscopy, utilization patterns for non-VA colonoscopy may have changed. Finally, we only studied veterans who were 65 years and older, so these results may not be applicable to younger veterans.

In a national analysis of the receipt of an outpatient colonoscopy by elderly veterans who are dually enrolled in both the VA and Medicare, more veterans obtained their colonoscopies through CMS than through the VA. Patients receiving primary care through a CBOC and residing in an urban setting were more likely to receive a colonoscopy through CMS. In contrast, females and minority patients were more likely to use the VA for obtaining their outpatient colonoscopy. Lastly, the use of colonoscopy through the VA decreased with age, as expected per guidelines, although such decreases by age were less evident through Medicare, providing indirect evidence that the use of colonoscopy within the VA may be more consistent with current practice guidelines than the use of colonoscopy by non-VA providers.

Center for Comprehensive Access and Delivery Research and Evaluation, Iowa City VA Medical Center (AM, MV-S, GER), IA; Division of General Internal Medicine, Department of Internal Medicine, University of Iowa Carver College of Medicine (AM, MVS, GER), Iowa City, IA; Institute for Clinical and Translational Science, University of Iowa (GER), Iowa City, IA.

Center of Innovation Award (CIN 13-412) from the Health Services Research and Development Service, Veterans Health

Administration; and a Clinical and Translational Science Award (2 UL1TR000442-06) from the National Center for Advancing Translational Science.

Dr Malhotra was supported by a Quality Scholars Fellowship from the Office of Academic Affiliations, Veterans Health Administration.

Drs Malhotra, Vaughan-Sarrazin, and Rosenthal are Veterans Health Administration (VA) employees. Dr Rosenthal has received numerous VA grants and has attended VA-sponsored meetings. Dr Vaughan-Sarrazin reports no relationship or financial interest withany entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (AM, MV-S, GER); acquisition of data (MV-S, GER); analysis and interpretation of data (AM, MV-S, GER); drafting of the manuscript (AM, MV-S); critical revision of the manuscript for important intellectual content (AM, GER); statistical analysis (AM, MV-S); and supervision (MV-S, GER).

 Ashish Malhotra, MD, CADRE, Bldg 42, Iowa City VA Medical Center, 601 US Highway 6, Iowa City, IA 52246. E-mail: ashish.malhotra@va.gov.

1. Shen Y, Hendricks A, Zhang S, Kazis LE. VHA enrollees’ health care coverage and use of care. 

2. Wolinsky FD, An H, Liu L, Miller TR, Rosenthal GE. Exploring the association of dual use of the VHA and Medicare with mortality: separating the contributions of inpatient and outpatient services. 

3. Wolinsky FD, Miller TR, An H, Brezinski PR, Vaughn TE, Rosenthal GE. Dual use of Medicare and the Veterans Health Administration: are there adverse health outcomes? 

4. Petersen LA, Byrne MM, Daw CN, Hasche J, Reis B, Pietz K. Relationship between clinical conditions and use of Veterans Affairs health care among Medicare-enrolled veterans. 

This study examines factors impacting the receipt of an outpatient colonoscopy by VA or non-VA providers in older veterans dually eligible for VA/Medicare benefits.

Elderly Veterans With Dual Eligibility for VA and Medicare Services: Where Do They Obtain a Colonoscopy?

 Prior studies of hospitalist services have suggestedimproved efficiency and quality of care compared with traditionalinpatient services.

 To compare outcomes of patients on a new hospitalistservice with those on traditional inpatient services and todetermine the impact of hospitalists on particular patient subgroups.

 Prospective, quasiexperimental, observational.

 The study was conducted on the general medicineservice at an academic teaching hospital, staffed by hospitalistphysicians (HP) and nonhospitalist physicians (NHP), and included1706 consecutive, directly admitted patients over 1 year.

 The 447 HP patients and 1259 NHP patients had similarrates of in-hospital mortality (1.3% vs 2.1%, respectively; 

 =.29) and 30-day readmission (7.8% vs 8.7%, respectively; 

 = .55).Mean hospital length of stay (LOS) was 1 day shorter for HPpatients in unadjusted analyses (5.5 vs 6.5 days, respectively; 

 =.009) and in multivariable analyses adjusting for clustering andpatient factors. Physician experience was not correlated (

 < .2)with LOS. In stratified analyses, differences in LOS between HPand NHP patients were greater for patients residing closer to thehospital. Mean total costs were $917 less for HP patients (

 = .04) in multivariable analyses. Decreases in costswere significant (

 = .04). Nonetheless, mean costs per daywere $122 higher (

 Patients managed by hospitalists had shorter LOSand lower costs than patients managed by nonhospitalists, but hadhigher costs per day. These results suggest that hospitalists increasethe intensity of care and may have their greatest impact on specifictypes of patients and classes of hospital costs.

Since the term hospitalist was coined 7 years ago

to describe physicians who provide inpatient carein place of primary care physicians or academic1-month-per-year attendings, several studies have comparedoutcomes of patients managed by hospitalist andnonhospitalist physicians. These comparisons have typicallyrevealed that hospitalist care reduced length ofstay (LOS) by approximately 1 day and costs by 10% to15%.

 In a recent review of the growing hospitalist movement,Wachter and Goldman concluded that "researchsupports the premise that hospitalists improve inpatientefficiency without harmful effects on quality or patientsatisfaction."

 Although multiple studies have shownreductions in hospital LOS and costs, the number ofstudies and hospitals upon which the above conclusionswere based are limited. In addition, hospitals are activelyrestructuring inpatient care services including theexpansion of hospitalist services, nurse case management,and other concurrent interventions that mayaffect LOS and costs. Evidence of the impact of hospitalistson individual hospitals is still needed.

An important determination to make is the factorsthat contribute to this shorter LOS and lower costs.Prior studies have not provided insight into the mechanismsby which hospitalist services achieved thisimproved efficiency. Some have argued that "practicemakes perfect," although the relationship betweenexperience and efficiency has not been proven.Reductions in laboratory and radiology testing or druguse may be another mechanism for reductions in costsof care. In addition, the relative impact of the cost associatedwith nursing care and excess LOS has not beenwell described. Finally, patient-specific factors have notbeen evaluated for associations with outcomes andresource utilization on hospitalist services.

Herein we report the findings from an observationalstudy of the first year of a hospitalist program at an academicmedical center, and compare outcomes andresource utilization for patients managed by hospitalistand nonhospitalist physicians. Whereas the primaryaim of the study was to add to the body of evidenceabout the effect of hospitalist programs, we also soughtto provide greater detail than prior studies on the natureof cost differences and whether differences were consistent across different cost categories (eg, nursing, laboratory).We further sought to examine whether particularpatient groups were somehow more or less affected bythe hospitalist model.

The study was conducted at the University of IowaHospitals and Clinics, an 831-bed academic teachinghospital and tertiary referral center. The general internalmedicine ward service has 4 teams consisting of 1attending physician, 1 senior resident, 1 intern, and 1or 2 medical students. Residents and students are randomlyassigned to the 4 teams. All teams admit patientsto the same hospital floors, have the same nursing staff,and work with the same social service and other hospitalpersonnel.

In July 2000, the Division of General InternalMedicine implemented a hospitalist physician (HP)service for inpatient general internal medicine. Theservice was developed to meet the professional interestsof faculty. No implicit or explicit goals for resource use,clinical outcomes, or patient satisfaction were set.During the 2000-2001 academic year, 3 "hospitalist"faculty staffed 1 of the 4 general internal medicine servicesin month-long blocks and were provided with coveragefor 2 to 3 weekends per month by 12 other generalmedicine faculty in an effort to prevent burnout. Eachhospitalist faculty staffed for 3 to 6 months.

Thirty-four faculty from various divisions in internalmedicine staffed the nonhospitalist physician (NHP)services for the same year. Of the 3 NHP services, 1 wasstaffed by 7 endocrinologists; 1 by 12 nephrologists;and 1 by a combination of 2 rheumatologists, 6 infectiousdisease specialists, and 7 general internists. TheNHP services varied in their approach to weekend coverage.Some staff attended for the entire month (n = 9;26%), others split the month in half (n = 9; 26%), andall the others had variable coverage throughout themonth (n = 16; 47%). Of the 34 nonhospitalist faculty,1 spent 3 months on the service, 5 others spent a rangeof 5 to 8 weeks, and the remainder 1 month or less onthe service.

The eligible sample included all 1887 consecutivedischarges between July 1, 2000, and June 30, 2001,from the 4 general internal medicine services. Allpatients were sequentially admitted in a quasiexperimentalmanner to one of the teaching teams in an alternatingmanner without regard to diagnosis or to complexity.The teams took "long-call" every fourth day,during which teams admitted up to 10 patientsovernight. In addition, on weekdays teams took up to 4admissions during the day on "short-call," whichoccurred 2 days before and after long-call. All admissionsto general internal medicine from university- andcommunity-based physicians were assigned to the 4teaching services, including admissions from primarycare physicians. During the study period, there was no"nonteaching" service. Patients requiring an intensivecare unit (ICU) bed were admitted to a closed medicalor cardiovascular ICU. Separate cardiology and hematology/oncology teams also existed.

Of the 1887 consecutive admissions, 1706 (90%)patients were admitted directly to the general internalmedicine service (ie, direct admissions) from either theemergency room (n = 801) or from university clinics,referring physicians' offices, or other acute care hospitals(n = 905). The remaining 181 (10%) admissionswere transferred to general internal medicine (ie, transferadmissions) from the medical ICU (n = 105) or fromsurgical or other nonmedicine services (n = 76).Because costs incurred by transfer admissions prior totheir transfer to general internal medicine could not beseparated from costs after the transfer, our primaryanalyses were conducted in the 1706 direct admissions.

All study data, including outcome variables andmeasures of resource utilization, were obtained fromthe hospital's information systems. The principal outcomevariables were hospital mortality and 30-day readmissionrate. Resource utilization was measured usinghospital LOS and hospital costs. Because of the potentialbias introduced by outlier LOS values, all patientswith a LOS of longer than 60 days were truncated at 60days. This truncation included 1 patient in the hospitalistgroup and 5 in the nonhospitalist group.

Hospital costs were measured using the TSI costaccounting system (Transition Systems, Inc, Boston,Mass). This widely used methodology determines thefixed and variable costs of all billable hospital services.This system also allocates to these services indirectcosts associated with nonbillable services giving a totalcost by category (eg, nursing, physicians, medications)and a total cost for the hospital stay. Separate analyseswere performed on nursing, laboratory, pharmacy, andradiology services, as these cost categories representedthe top 4 categories in total cost and together more than70% of all inpatient costs in our study sample. Totalcosts were reported in the analyses because of the complexitiesof determining direct and indirect costs andwhat proportion may be attributed as fixed and variable.Total costs are more generalizable to other hospitalsand systems.

International Classificationof Diseases, Ninth Revision, Clinical Modification

Demographic and clinical variables available includedage, sex, race, health insurance status, admissionsource (eg, home, nursing home), discharge destination(ie, home, home healthcare, other hospital, nursinghome, died, against medical advice, or other care facility),admission and discharge date, discharge diagnosis-relatedgroup (DRG), and principal diagnosis (asmeasured by taxonomy of the 

[ICD-9-CM] In addition, for each patient, distance fromthe hospital was determined using a function thatmathematically estimates the "straight line" distancebetween 2 coordinates (longitude and latitude).

Author | Gary E. Rosenthal, MD

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