Among Medicare beneficiaries, the relationship between fragmented ambulatory care and subsequent emergency department visits and hospital admissions varies with the number of chronic conditions. Check out our website’s new table/figure pop-up feature! Click on the name of a table or figure in the text to see it in your browser.
Objectives: We sought to determine the associations between fragmented ambulatory care and subsequent emergency department (ED) visits and hospital admissions, while considering possible interactions between fragmentation and number of chronic conditions.
Study Design: We conducted a cohort study over 3 years among 117,977 fee-for-service Medicare beneficiaries who were attributed to primary care physicians in a 7-county region of New York and had 4 or more ambulatory visits in the baseline year.
Methods: We calculated fragmentation scores using a modified Bice-Boxerman Index and, because scores were skewed, divided them into quintiles. We used Cox regression models to determine associations between fragmentation and ED visits and, separately, hospital admissions, stratifying by number of chronic conditions and adjusting for age, gender, number of ambulatory visits, and case mix.
Results: Among those with 1 to 2 or 3 to 4 chronic conditions, having the most (vs the least) fragmented care significantly increased the hazard of an ED visit and, separately, increased the hazard of an admission (adjusted P <.05 for each comparison). Among those with 5 or more chronic conditions, having the most fragmented care significantly increased the hazard of an ED visit but decreased the hazard of an admission (adjusted P <.05 for each comparison). Among those with 0 chronic conditions, having fragmented care was not associated with either outcome.
Conclusions: The relationship between fragmented ambulatory care and subsequent utilization varies with the number of chronic conditions. Beneficiaries with a moderate burden of chronic conditions (1-2 or 3-4) appear to be at highest risk of excess ED visits and admissions due to fragmented care.
Am J Manag Care. 2018;24(9):e278-e284Takeaway Points
Among Medicare beneficiaries, the relationship between fragmented ambulatory care and subsequent healthcare utilization varies with the number of chronic conditions.
Patients routinely receive care from multiple ambulatory providers, especially if they have chronic conditions.1 Receiving care from multiple providers may be appropriate, but it often leads to gaps in communication across providers,2 which can result in suboptimal care. Previous studies have found that more fragmented care (that is, care spread across many providers with no dominant provider) is associated with more testing, more overuse of procedures, and lower patient satisfaction compared with less fragmented care.3-5 Prior studies have also found associations between more fragmented care and higher rates of emergency department (ED) visits, higher rates of hospital admissions, and higher costs compared with less fragmented care.6-8
However, the association between fragmentation and these outcomes is not yet sufficiently described to enable the design of interventions to address it. For example, fragmentation has typically been measured as a continuous variable, which is appropriate but assumes that the relationship between fragmentation and outcomes is linear,6,8 which may not be the case; rather, a threshold effect may be present, in which a certain amount of fragmentation increases the risk of an outcome. Also, previous studies have adjusted for the number of chronic conditions or case mix, which is appropriate but may mask the possibility that fragmentation affects patients differently depending on how many chronic conditions they have.8
Thus, we sought to determine the associations between care fragmentation in the ambulatory setting and subsequent ED visits and hospital admissions, while considering whether those associations vary with number of chronic conditions.
We conducted a cohort study (2010-2012) of fee-for-service (FFS) Medicare beneficiaries who received care from physicians in the Hudson Valley of New York to determine associations between fragmented ambulatory care and subsequent ED visits and hospital admissions. The Institutional Review Board of Weill Cornell Medicine approved the protocol.
The Hudson Valley consists of 7 counties immediately north of New York City (Dutchess, Orange, Putnam, Rockland, Sullivan, Ulster, and Westchester). Approximately 85% of the people in this region live in urban or suburban areas.9,10 Approximately 85% of residents have health insurance, and approximately 10% live at or below the federal poverty level.9,11 At the time of the study, most healthcare in the region was delivered by physicians in small- and medium-size private practices, using FFS reimbursement from multiple payers.12
We used Medicare FFS claims data for 2010-2012, extracting the following claim-level variables: patient study identifier (ID), patient date of birth, patient gender, date of service, rendering provider ID, Current Procedural Terminology (CPT) codes, and International Classification of Diseases, Ninth Revision (ICD-9) codes. We also extracted monthly patient-level enrollment data.
We first identified primary care physicians (general internists and family medicine physicians) in the claims who had billing zip codes in the Hudson Valley (Figure). We determined which Medicare beneficiaries 65 years and older could be attributed to those primary care physicians, based on 2010 claims, using previously defined logic.13 Of those, we identified beneficiaries who were continuously enrolled that year and had 1 or more ambulatory visits. Ambulatory visits were defined by CPT codes, using a modified version of the definition by the National Committee for Quality Assurance (NCQA).14 Modifications restricted the definition to evaluation-and-management visits for adults in an office setting, excluding management-only visits (eg, physical therapy) and non—office-based visits (eg, visits in nursing homes). This definition also excluded ED visits.
We excluded beneficiaries with outlier observations (>99.9th percentile) for number of ambulatory visits or unique providers, because those observations may have been erroneous. Next, we restricted the cohort to those with 4 or more ambulatory visits in the baseline year, because calculating fragmentation with 3 or fewer ambulatory visits can lead to statistically unstable estimates.8 We required that beneficiaries be continuously enrolled in Medicare for at least 1 more consecutive year, contributing data for 2 years (2010-2011) or 3 years (2010-2012). Finally, we excluded those who were in the hospital on January 1, 2011, because they were not at risk of an ED visit or hospital admission at the start of follow-up.
Independent variable. Our base-case analysis measured fragmentation with the Bice-Boxerman Index (BBI) (eAppendix A [eAppendices available at ajmc.com]),15,16 a previously validated measure.3,4,6,8,16 We reversed raw BBI scores, so that higher scores would reflect more fragmentation. Patterns of care that reflect high dispersion (many providers) and low density (a relatively low proportion of ambulatory visits by each provider) yield worse (higher) scores. Because the distribution of BBI scores is inherently skewed, we divided scores into quintiles, an approach we successfully used previously,3 to maximize clarity of interpretation. We conducted sensitivity analyses with 2 other fragmentation indices, the Herfindahl-Hirschman Index and the Usual Provider Continuity Index (eAppendix A).16
Dependent variables. We identified ED visits and hospital admissions in the claims, using definitions from NCQA.14 An “ED visit” resulted in discharge to home or elsewhere. If an ED visit resulted in hospital admission, it was considered part of that admission and counted only as an admission.
Potential confounders. We used ICD-9 codes to calculate the number of chronic conditions for each beneficiary (0, 1-2, 3-4, or ≥5)3 of 26 unique chronic conditions defined by CMS (eAppendix B).17 We also calculated a severity of illness index.18,19 We considered beneficiary age and gender as potential confounders. In addition, we considered the number of ambulatory visits as a potential confounder, because the number of ambulatory visits was weakly correlated with fragmentation score (Spearman correlation coefficient, 0.22; P <.0001).
Descriptive statistics. We characterized beneficiaries in terms of age, gender, and number of chronic conditions. We compared the characteristics of the study sample with those who were excluded using pairwise Wilcoxon rank-sum tests for continuous variables and χ2 tests for categorical variables.
For the study sample, we calculated descriptive statistics regarding the number of ambulatory visits, number of unique providers, and proportion of ambulatory visits with the most frequently seen provider in the baseline year (overall and stratified by fragmentation quintile). We also determined the proportions of beneficiaries who had 1 or more ED visits and, separately, 1 or more hospital admissions during follow-up.
Statistical models. Because fragmentation can change over time and because the hypothesized consequences of fragmented care may unfold relatively quickly, we used Cox models and treated fragmentation as time-dependent. That is, we first calculated fragmentation in the first 12 months (calendar year 2010) and determined whether the beneficiary had an ED visit in month 13 (January 2011) or not. We then moved this window of observation by 1 month, recalculating the model using fragmentation in months 2 to 13 as a potential predictor of an ED visit in month 14, and so on. If the number of ambulatory visits in any 12-month window fell below 4 (making it difficult to calculate fragmentation scores), we carried forward the last fragmentation score that was based on 4 or more ambulatory visits. We used the same approach in separate models predicting hospital admissions.
For each model, observation continued until an outcome or censoring occurred. For the ED visit models, censoring occurred (1) if a beneficiary was admitted to the hospital (with the reasoning that the beneficiary was not at risk of an ED visit) or (2) at the end of the beneficiary’s continuous enrollment. For the hospital admission models, censoring occurred only at the end of the beneficiary’s continuous enrollment; any ED visit had no effect on the hospital models, because the beneficiary was still at risk of a hospital admission.
We adjusted for beneficiary gender and for the following time-varying covariates: age, Charlson-Deyo score, and number of ambulatory visits. By adjusting for number of ambulatory visits, we sought to fully tease apart fragmentation (the diffuseness of care) from the volume of ambulatory visits, as we have done previously.3 We calculated an interaction term for fragmentation category*chronic condition count and then, because this term was statistically significant for at least 1 model, we stratified our analyses by chronic condition count.
To test the appropriateness of the statistical assumptions of our models, we generated weighted plots of Schoenfeld residuals by time and calculated zph tests for nonproportional hazards (which is recommended in situations with time-dependent predictors and/or covariates).20-22
We considered P values <.05 to be statistically significant. We used SAS version 9.4 (SAS Institute; Cary, North Carolina).
We identified 139,614 adults 65 years or older who were continuously enrolled in Medicare FFS in 2010, were attributed to a primary care physician in the Hudson Valley, and had 1 or more ambulatory visit that year (Figure). The average age of this group was 76.8 (SD = 7.5) years, which is fairly similar to that of Medicare beneficiaries 65 years or older nationally (average [SD] age, 75.1 [7.8] years).23
Our sample was composed of the 117,977 (85%) beneficiaries who did not have outlier observations, had 4 or more ambulatory visits, had continuous enrollment for at least 1 more consecutive year, and were not hospitalized on the first day of 2011 (Figure).
Among the 117,977 beneficiaries in our sample, the mean age was 77.2 years (Table 1). More than half (60.8%) were women. The distribution of counts of chronic conditions was as follows: 1.4% had 0 chronic conditions, 18.6% had 1 to 2 chronic conditions, 39.1% had 3 to 4, and 41.0% had 5 or more. Most beneficiaries (93%) contributed data for all 3 years of the study, whereas the remainder (7%) contributed data for the first 2 years only.
The 13,439 beneficiaries (10% of the total) who were excluded because they had 3 or fewer ambulatory visits were younger and healthier than those who were included (eAppendix C). The 7978 beneficiaries (6% of the total) who were excluded because they were not continuously enrolled in 2011 (including due to death that year) or were hospitalized on January 1, 2011, were older, less likely to be female, and sicker than those who were included (eAppendix C).
Ambulatory Care at Baseline
Among those who were included, the typical (median) beneficiary had 12 ambulatory visits with 5 unique providers in the baseline year (Table 1). The typical beneficiary also had 40% of ambulatory visits with their most frequently seen provider. As the proportion of visits with the most frequently seen provider decreased, fragmentation increased (Table 2). Just 2.5% of the sample had 1 provider for all visits (resulting in a fragmentation score equal to 0.00).
Associations With ED Visits and Hospital Admissions
One-fourth of beneficiaries (25%) had 1 or more ED visits during follow-up. The median observation time until an ED visit or censoring (for the ED visit models) was 1.7 years. One-third of beneficiaries (33%) had 1 or more hospital admissions during follow-up. The median observation time until a hospital admission or censoring was 2.0 years. Schoenfeld residuals and zph tests showed that our models did not violate the underlying statistical assumptions.
Among those with 0 chronic conditions, having fragmented care did not increase the hazard of an ED visit (Table 3). Among those with 1 or more chronic conditions, having the most (vs the least) fragmented care significantly increased the hazard of an ED visit, by a magnitude of 13% for those with 1 to 2 chronic conditions (P <.01), 14% for those with 3 to 4 chronic conditions (P <.0001), and 10% for those with 5 or more chronic conditions (P = .001). Some but not all of the intermediate fragmentation categories were also associated with an increased hazard of an ED visit.
Among those with 0 chronic conditions, having fragmented care did not increase the hazard of a hospital admission (Table 4). Among those with 1 to 2 or 3 to 4 chronic conditions, having the most (vs the least) fragmented care significantly increased the hazard of a hospital admission, by a magnitude of 14% for those with 1 or 2 chronic conditions (P <.01) and 6% for those with 3 or 4 chronic conditions (P <.05). Among those with 5 or more chronic conditions, having the most fragmented care decreased the adjusted hazard of a hospital admission by 5% (P = .03). Most intermediate fragmentation categories were not associated with a significant difference in the hazard of admission.
Sensitivity analyses using 2 alternative fragmentation indices (eAppendices D and E) showed results consistent with our base-case analyses, both for ED visits and hospital admissions.
In this study of Medicare FFS beneficiaries 65 years and older, having highly fragmented care (in the highest quintile of fragmentation scores) seemed to have the greatest impact on subsequent outcomes, suggesting a potential threshold effect. Those with highly fragmented care had a median of 13 ambulatory visits with 8 providers, and their most frequently seen provider typically accounted for just 24% of ambulatory visits. The relationship between highly fragmented care and subsequent outcomes varied by the number of chronic conditions.
Among those with 1 to 2 or 3 to 4 chronic conditions, having the most (vs the least) fragmented care significantly increased the hazard of an ED visit and, separately, increased the hazard of a hospital admission (P <.05 for each comparison). Among those with 5 or more chronic conditions, having the most (vs the least) fragmented care significantly increased the hazard of an ED visit but decreased the hazard of a hospital admission (P <.05 for each comparison). This observation could mean that having many providers for these complex patients is often necessary and sometimes protective. We did not find an association between fragmentation and ED visits or hospital admissions among those with 0 chronic conditions, perhaps because those who are relatively healthy are not as vulnerable to the adverse effects of fragmented care.
Our finding of an increased hazard of ED visits for those with fragmented care and at least 1 chronic condition is consistent with findings from 2 other studies, which were disease-specific: 1 that focused on patients with diabetes7 and another that focused on patients with diabetes, congestive heart failure, or chronic obstructive pulmonary disease.6 Our finding of an increased hazard of hospital admissions for those with fragmented care and 1 to 4 chronic conditions is also consistent with studies that expressed the increased hazard as a function of a 0.1-point increase on a continuous fragmentation scale.6,8 Thus, our work expands the literature by including Medicare beneficiaries regardless of disease type and by quantifying the magnitude of the hazard for the most fragmented care.
The results have implications for the design of future interventions to improve healthcare, in terms of which patients to target for intervention. Many previous efforts to improve healthcare quality and efficiency have targeted the sickest patients,24,25 which makes sense, because these patients account for a disproportionate amount of healthcare utilization.26 However, the effectiveness of these programs has been mixed,24,27 perhaps in part because it is difficult to modify the need for care among the sickest patients. Our study findings suggest that there may be inefficient utilization of healthcare services among those with a moderate number of chronic conditions. Inefficient utilization may be modifiable, and decreasing inefficiency among the many people with a moderate disease burden may have a large aggregate impact.
The results of this study also have implications for what kinds of future interventions to test. Previous efforts to improve care coordination, such as the patient-centered medical home model of care, have not specifically measured or targeted patterns of ambulatory care within individual patients.28 The few previous studies that tried to explicitly decrease fragmentation were small trials but had promising results. A randomized controlled trial of 776 men 55 years and older at a Veterans Administration hospital in Vermont found that those who were randomized to “continuity” (routinely scheduled appointments with the same provider) had fewer emergent hospital admissions and shorter average length of stay than those randomized to “discontinuity” (a die tossed at each scheduled follow-up visit, with a 33% chance of being sent to a different provider).29 A randomized controlled trial of 409 pediatric patients in Seattle, Washington, was successful in decreasing fragmentation through the use of custom alerts built into electronic health records, notifying providers in real time if they were seeing patients with highly fragmented care.30
This study has several limitations. First, it is observational, and we cannot rule out unmeasured confounding. Second, we cannot draw conclusions about the medical appropriateness of the care patterns observed. It is possible that fragmentation increases appropriately when patients become acutely ill and that this appropriate use of multiple providers occurs just prior to an ED visit or hospital admission. Future studies with more granular clinical data are needed to help clarify issues of appropriateness. Third, we did not measure communication across physicians. Although having fragmented care may increase the risk of gaps in communication across providers, its presence should not be interpreted as a definite lack of care coordination. Fourth, we did not have data on practice characteristics or practice affiliation, so we were not able to account for those. Fifth, this study took place in 1 region, which may limit generalizability; however, this region is a multipayer, multiprovider healthcare market, which may make it similar to other communities. Future studies could include market characteristics (such as rural vs urban and number of providers per population) as additional explanatory variables. Sixth, this study included only FFS Medicare beneficiaries; results may not apply to patients with other insurance types.
Even with these limitations, this study is relevant, because the importance of healthcare fragmentation has grown with national changes in provider reimbursement. Medicare is moving away from FFS reimbursement toward alternative payment models.31,32 These models require providers to be clinically and financially responsible for all of a patient’s care, not just the care that they themselves provide. Thus, an excess burden of ED visits and hospitalizations from fragmented ambulatory care would be highly relevant to providers seeking to succeed under these new payment models. Large studies to explicitly test and compare the effectiveness of strategies for decreasing fragmentation are warranted. Additional studies could also consider the effect of fragmentation on rates of readmission, given that that is a time when patients are especially vulnerable to the effects of suboptimal care delivery.33-35
Highly fragmented care can independently increase the hazard of an ED visit or hospital admission, even among those with a moderate number of chronic conditions. Reducing fragmentation for those with a moderate number of chronic conditions may both improve quality and reduce costs.
This study was funded by the Commonwealth Fund (grant #20140960). The authors thank Leah Hellerstein, BA, and Yesenia Miranda, BA, for their assistance with reviewing the literature. The authors thank the New York State Department of Health for facilitating access to the data. The conclusions do not necessarily reflect the views of the Commonwealth Fund or New York State Department of Health.Author Affiliations: Department of Medicine (LMK, MR), and Department of Healthcare Policy & Research (LMK, JKS, RJ), Weill Cornell Medicine, New York, NY; Lake Fleet Consulting (SSS), Irvington, NY.
Source of Funding: This work was supported by the Commonwealth Fund (grant #20140960).
Author Disclosures: 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.
Authorship Information: Concept and design (LMK, SSS); acquisition of data (LMK, RJ, SSS); analysis and interpretation of data (LMK, JKS, MR, RJ); drafting of the manuscript (LMK); critical revision of the manuscript for important intellectual content (LMK, JKS, MR, SSS); statistical analysis (JKS, MR, RJ); obtaining funding (LMK); administrative, technical, or logistic support (LMK); and supervision (LMK).
Address Correspondence to: Lisa M. Kern, MD, MPH, Weill Cornell Medicine, 525 E 68th St, Box 331, New York, NY 10065. Email: email@example.com.REFERENCES
1. Pham HH, Schrag D, O’Malley AS, Wu B, Bach PB. Care patterns in Medicare and their implications for pay for performance. N Engl J Med. 2007;356(11):1130-1139. doi: 10.1056/NEJMsa063979.
2. O’Malley AS, Reschovsky JD. Referral and consultation communication between primary care and specialist physicians: finding common ground. Arch Intern Med. 2011;171(1):56-65. doi: 10.1001/archinternmed.2010.480.
3. Kern LM, Seirup JK, Casalino LP, Safford MM. Healthcare fragmentation and the frequency of radiology and other diagnostic tests: a cross-sectional study. J Gen Intern Med. 2017;32(2):175-181. doi: 10.1007/s11606-016-3883-z.
4. Romano MJ, Segal JB, Pollack CE. The association between continuity of care and the overuse of medical procedures. JAMA Intern Med. 2015;175(7):1148-1154. doi: 10.1001/jamainternmed.2015.1340.
5. van Walraven C, Oake N, Jennings A, Forster AJ. The association between continuity of care and outcomes: a systematic and critical review. J Eval Clin Pract. 2010;16(5):947-956. doi: 10.1111/j.1365-2753.2009.01235.x.
6. Hussey PS, Schneider EC, Rudin RS, Fox DS, Lai J, Pollack CE. Continuity and the costs of care for chronic disease. JAMA Intern Med. 2014;174(5):742-748. doi: 10.1001/jamainternmed.2014.245.
7. Liu CW, Einstadter D, Cebul RD. Care fragmentation and emergency department use among complex patients with diabetes. Am J Manag Care. 2010;16(6):413-420.
8. Nyweide DJ, Anthony DL, Bynum JP, et al. Continuity of care and the risk of preventable hospitalization in older adults. JAMA Intern Med. 2013;173(20):1879-1885. doi: 10.1001/jamainternmed.2013.10059.
9. QuickFacts: United States. US Census Bureau website. census.gov/quickfacts/fact/table/US/PST045217. Accessed June 4, 2018.
10. Geography: urban and rural. US Census Bureau website. census.gov/geo/reference/urban-rural.html. Accessed June 4, 2018.
11. New York State Community Health Indicator Reports (CHIRS). New York State Department of Health website. health.ny.gov/statistics/chac/indicators. Accessed June 4, 2018.
12. Stuard SS, Blair AJ. Interval examination: regional transformation of care delivery in the Hudson Valley. J Gen Intern Med. 2011;26(11):1371-1373. doi: 10.1007/s11606-011-1817-3.
13. Kern LM, Edwards A, Kaushal R. The patient-centered medical home, electronic health records, and quality of care. Ann Intern Med. 2014;160(11):741-749. doi: 10.7326/M13-1798.
14. HEDIS 2015: volume 2: technical specifications. National Committee for Quality Assurance website. ncqa.org/HEDISQualityMeasurement/HEDISMeasures/HEDIS2015.aspx. Accessed June 4, 2018.
15. Bice TW, Boxerman SB. A quantitative measure of continuity of care. Med Care. 1977;15(4):347-349.
16. Pollack CE, Hussey PS, Rudin RS, Fox DS, Lai J, Schneider EC. Measuring care continuity: a comparison of claims-based methods. Med Care. 2016;54(5):e30-e34. doi: 10.1097/MLR.0000000000000018.
17. Chronic Conditions Data Warehouse website. ccwdata.org. Accessed June 4, 2018.
18. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383.
19. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-619. doi: 10.1016/0895-4356(92)90133-8.
20. Allison PD. Chapter 5: estimating Cox regression models with PROC PHREG. In: Allison PD. Survival Analysis Using SAS: A Practical Guide. 2nd ed. Cary, NC: SAS Institute Inc; 2010:125-200.
21. SAS Institute Inc. The PHREG procedure. In: SAS Institute Inc. SAS/STAT 14.2 User’s Guide. Cary, NC: SAS Institute Inc; 2016.
22. Thomas L, Reyes EM. Survival estimation for Cox regression models with time-varying coefficients using SAS and R. J Stat Softw. 2014;61:1-23. doi: 10.18637/jss.v061.c01.
23. Krumholz HM, Nuti SV, Downing NS, Normand SL, Wang Y. Mortality, hospitalizations, and expenditures for the Medicare population aged 65 years or older, 1999-2013 [erratum in JAMA. 2015;314(8):837. doi: 10.1001/jama.2015.10005]. JAMA. 2015;314(4):355-365. doi: 10.1001/jama.2015.8035.
24. Fireman B, Bartlett J, Selby J. Can disease management reduce health care costs by improving quality? Health Aff (Millwood). 2004;23(6):63-75. doi: 10.1377/hlthaff.23.6.63.
25. Haas LR, Takahashi PY, Shah ND, et al. Risk-stratification methods for identifying patients for care coordination. Am J Manag Care. 2013;19(9):725-732.
26. Blumenthal D, Chernof B, Fulmer T, Lumpkin J, Selberg J. Caring for high-need, high-cost patients—an urgent priority. N Engl J Med. 2016;375(10):909-911. doi: 10.1056/NEJMp1608511.
27. Peikes D, Chen A, Schore J, Brown R. Effects of care coordination on hospitalization, quality of care, and health care expenditures among Medicare beneficiaries: 15 randomized trials. JAMA. 2009;301(6):603-618. doi: 10.1001/jama.2009.126.
28. Sinaiko AD, Landrum MB, Meyers DJ, et al. Synthesis of research on patient-centered medical homes brings systematic differences into relief. Health Aff (Millwood). 2017;36(3):500-508. doi: 10.1377/hlthaff.2016.1235.
29. Wasson JH, Sauvigne AE, Mogielnicki RP, et al. Continuity of outpatient medical care in elderly men: a randomized trial. JAMA. 1984;252(17):2413-2417. doi: 10.1001/jama.1984.03350170015011.
30. Christakis DA, Wright JA. Can continuity of care be improved? results from a randomized pilot study. Ambul Pediatr. 2004;4(4):336-339.
31. Burwell SM. Setting value-based payment goals—HHS efforts to improve U.S. health care. N Engl J Med. 2015;372(10):897-899. doi: 10.1056/NEJMp1500445.
32. Doherty RB. Goodbye, Sustainable Growth Rate—hello, Merit-based Incentive Payment System. Ann Intern Med. 2015;163(2):138-139. doi: 10.7326/M15-0992.
33. Naylor M, Brooten D, Jones R, Lavizzo-Mourey R, Mezey M, Pauly M. Comprehensive discharge planning for the hospitalized elderly: a randomized clinical trial. Ann Intern Med. 1994;120(12):999-1006. doi: 10.7326/0003-4819-120-12-199406150-00005.
34. Naylor MD, Aiken LH, Kurtzman ET, Olds DM, Hirschman KB. The care span: the importance of transitional care in achieving health reform. Health Aff (Millwood). 2011;30(4):746-754. doi: 10.1377/hlthaff.2011.0041.
35. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613-620. doi: 10.1001/jama.281.7.613.