Reexamining the Association of Care Continuity and Health Care Outcomes

ABSTRACT

Objectives: This study examined the relationship between claims-based and patient-reported continuity of care (COC) measures and investigated the effects of the 2 types of COC measures on subjective and objective health care outcomes.

Study Design: A prospective, cross-sectional, correlational survey design was used. A nationwide face-to-face interview survey of community-dwelling older adults was conducted, and the survey participants’ health claims records were retrieved and linked under the universal health insurance system of Taiwan in 2018.

Methods: Health care outcomes were measured subjectively (patient satisfaction and perceived lack of coordination) and objectively (likelihood of hospital admissions and emergency department [ED] visits). COC was measured using claims-based and multidimensional patient-reported COC. Ordered logit and logit models were used to examine the relationship between the 2 types of COC measures, and health care outcomes were measured subjectively and objectively. Average marginal effects with bootstrapped SEs were computed for health care outcomes.

Results: This study demonstrated that the correlations of claims-based and patient-reported COC measures were quite low and mainly insignificant. A higher claims-based COC was significantly associated with a lower likelihood of hospital admissions, ED visits, and perceived lack of coordination. No significant relationship was identified between claims-based COC and patient satisfaction. Participants reporting higher COC had better patient satisfaction and less perceived lack of coordination. However, no relationship was identified between patient-reported COC and the likelihood of hospital admissions and ED visits.

Conclusions: The correlation between claims-based and patient-reported COC measures is low, and claims-based and patient-reported COC measures are associated with different subjective and objective health care outcomes. We suggest that claims-based COC indicators representing the pattern of physician visits might be considered a unique dimension of COC.

Am J Manag Care. 2023;29(8):e242-e249. https://doi.org/10.37765/ajmc.2023.89411

Takeaway Points

• This study simultaneously examined the association between claims-based and patient-reported care continuity measures and their effects on subjective and objective health care outcomes.
• The association between claims-based and patient-reported care continuity measures was low.
• Claims-based and patient-reported continuity measures showed distinct effects on subjective and objective health care outcomes.
• Claims-based continuity indicators represent the pattern of physician visits, which might be considered a unique dimension of care continuity.

Continuity of care (COC) is considered a fundamental element of health care systems. Study findings have indicated that better COC is associated with higher patient satisfaction,¹ fewer hospitalizations, and lower health care expenses.^2,3 Many countries have recently introduced policies to increase the quality of health care and reduce health care expenditures through improved COC, such as patient-centered medical homes in the United States.⁴ Adequate measures of COC are critical to ascertain the effects of COC on health care outcomes and the impact of these programs.

Previous studies have identified that COC has multidimensional features, including longitudinal, informational, and interpersonal/relational dimensions.^5-7 Despite decades of research, there is little consensus on the measurement of COC. Before the 1970s, COC was measured as a dichotomous variable, such as whether a patient had a usual or regular source of care.⁸ Due to the increasing availability of claims data in the mid-1970s, several claims-based COC indicators were developed and have been commonly employed in empirical studies since 2000.^2,3,9 Claims-based COC measures assume that frequent contact with a particular physician or with several providers is equivalent to better longitudinal COC in patient-physician relationships.⁷ On the contrary, Freeman et al in 2001 proposed the evaluation of patients’ experiences with the progression and coordination of care; since then, measuring COC from the patient’s perspective has attracted significant attention.⁵ Patients’ reports of COC using a survey method have gradually been adopted,¹⁰ and the multidimensional concept of COC has been stressed.^11-16 It is important to understand whether claims-based COC measures (indicating the patient’s physician visit pattern) are consistent with the various dimensions of patients’ experiences of COC. Only a few studies have examined the association between claims-based and patient-reported COC measures, and they had inconsistent results.^17-20

Over the past 2 decades, several studies have demonstrated the association between COC and health outcomes.^2,3 The majority of these studies have examined the association between claims-based COC measures and objective health care outcomes, such as hospitalizations or emergency department (ED) visits, using administrative data.^21-28 Nevertheless, several studies have investigated the association between patient-reported COC measures and subjective (patient-reported) health care outcomes, such as patient satisfaction1 or medication adherence.²⁹ However, few studies have assessed the association of both claims-based and patient-reported COC with health care outcomes for either subjective or objective outcome measures.^17,30 This study aimed to examine the relationship between claims-based COC measures and patient-reported COC measures. Furthermore, this study investigated the effects of the 2 types of COC measures on health care outcomes, which were measured subjectively and objectively in a health care system without a formal referral arrangement.

METHODS

Data Source and Study Sample

The Combined Outpatient Care Continuity and Coordination Assessment (COCCCA) questionnaire was developed to measure the experiences of care continuity and coordination from a patient perspective under the universal coverage health care system in Taiwan.³¹ A nationwide face-to-face interview survey was conducted among community-dwelling older adults selected via a stratified multistage approach proportional to the size sampling process. Details of the stratified multistage approach proportional to the size sampling process are available in our previous literature.³¹

A COCCCA instrument measuring both the concepts of care continuity and care coordination in outpatient care settings from a patient perspective has been validated. The 16 items of the COCCCA questionnaire were identified via item analysis and principal component analysis (PCA). The PCA generated 5 dimensions: 3 continuity oriented (interpersonal, information sharing, and longitudinal between patients and physicians) and 2 coordination oriented (information exchange and communication/cooperation among multiple physicians). The second-order confirmatory factor analysis supported the factor structure and indicated that distinct constructs of care continuity and coordination can be identified. In addition, we found that the 5 dimensions of the COCCCA had acceptable internal consistency. Detailed description of the validity and reliability of this instrument can be found in a previously published article.³¹

A total of 2144 participants completed the questionnaire with a response rate of 44.67% from November 19, 2018, to January 25, 2019. Among the respondents, 1815 signed the consent form to link National Health Insurance (NHI) claims data. Subsequently, we linked the survey data to their NHI claims data for 1 year prior to the date of the interview at the Health and Welfare Data Science Center of the Ministry of Health and Welfare, Taiwan. This study was approved by the Institutional Review Board of the National Taiwan University Hospital (No. 201603076RINA).

The participants were included in the analysis if they had (1) more than 3 physician visits in the year prior to the date of the interview, due to the inapplicability of claims-based COC measures for participants with very few visits,²¹ and (2) complete data for the 10 patient-reported continuity-related items and 2 subjective health care outcomes items (patient satisfaction and perceived lack of coordination) in the COCCCA instrument. In total, 1303 participants were included in the analysis. The characteristics of the included (n = 1303) and excluded (n = 512) participants were somewhat different. Those excluded from the analyses were more likely to be female, have lower income, be illiterate/without formal education, and have a better perception of their health status (eAppendix Table 1 [eAppendix available at ajmc.com]).

Variables of Interest

Dependent variables: health care outcomes. Health care outcomes were measured subjectively and objectively. The subjective health care outcomes were satisfaction with care, measured by the question, “In general, how satisfied are you with the health care you received?” and the perception of lack of coordination, measured by the question, “Do you feel the health care you received is lacking coordination?” Patient satisfaction was classified into 3 categories: low (very dissatisfied, dissatisfied, neither satisfied nor dissatisfied), intermediate (satisfied), and high (very satisfied). The perception of the lack of coordination was classified into high (always, usually), intermediate (sometimes), and low (rarely or never). The objective health care outcomes were whether the participant was hospitalized or had an ED visit in 1 year. They were derived from the NHI claims data. However, certain causes of hospitalizations or ED visits were not related to a higher or lower level of COC, such as injury, poisoning, or chemotherapy; therefore, these hospitalizations and ED visit records were deleted from the analysis.

COC measures. In this study, patient-reported COC measures consisted of the responses to 10 questions that were a subset of the COCCCA instrument.³¹ Patient experience referred to the health care services provided by the patients’ most frequently seen physicians in either community clinics or hospital outpatient departments. The 10 questions belonged to 3 dimensions of the COC: longitudinal (2 items), informational (4 items), and interpersonal (4 items) continuity, as shown in eAppendix Table 2. The response categories were rated on a 5-point Likert scale. Regarding the 3 dimensions of patient-reported COC (ie, longitudinal, informational, and interpersonal continuity), the scores of the items under each dimension were summed and categorized into 3 groups (low, intermediate, and high) according to the distribution of scores across the entire study population.

Regarding the claims-based COC, after reviewing the indicators used to measure COC, Jee and Cabana classified the COC into 5 types: duration, density, dispersion, sequence, and subjective measures.⁹ Using claims data, this study constructed 3 types of COC measures: density, dispersion, and sequence.⁹ Regarding the density type, the usual provider of care (UPC) index was utilized.³² Regarding the dispersion type, the continuity of care index (COCI) was utilized to measure the dispersion patterns of visits.³³ Regarding the sequence type, the sequential continuity (SECON) index was utilized to measure the sequences of physicians visited.³⁴ Please see the detailed operational definitions of the claims-based COC measures in eAppendix Table 3. The values of the 3 claims-based measures range from 0 to 1, with a higher value representing a better COC status. They were then categorized into 3 groups according to the distribution of scores across the entire study population. Questions concerning the experiences of subjective health care outcomes and patient-reported COC were limited to the 12 months before the interview.

Covariates. Several covariates that may have influenced the relationship between COC and health care outcomes were incorporated in this study’s analysis. The basic characteristics were sex, age, individual income groups (with US$1 equivalent to approximately NT$29 in 2020), level of education, and rural-urban residence.³⁵ In addition, 3 proxy variables were used in the regression models to represent a participant’s health status: the patient’s perception of their health status, a modified Charlson Comorbidity Index (CCI) score,³⁶ and the number of physician visits in the 12 months before the interview. The participants’ perception of their health status was classified into 2 categories: good (very good and good) and poor (fair, poor, and very poor).

Statistical Analyses

Pearson correlation coefficients were employed to examine the relationships among the 3 claims-based COC indicators and the 3-dimensional COC measures based on patient reports. We utilized a logit model to explore the effects of both claims-based and patient-reported COC measures on the likelihood of hospitalizations and ED visits. In addition, we used an ordered logit model to investigate the effects of both claims-based and patient-reported COC measures on the degree of satisfaction with care and the perceived lack of care coordination. The average marginal effects and employed bootstrapping with 1000 replications were calculated to obtain the SEs. All statistical analyses were conducted using SAS version 9.4 (SAS Institute) and Stata version 15.1 (StataCorp).

RESULTS

Table 1 [part A and part B] presents the demographic characteristics, health status, and variables of interest in this study (n = 1303). There were fewer female participants (46.82%) in the study sample, and the participants in the sample had a mean age of 71.34 years. Regarding the health care outcome variables, 17.11% of participants were hospitalized at least once and 23.87% had at least 1 ED visit. In addition, 9.98% experienced low satisfaction with care, and 26.17% reported a high level of lack of care coordination.

Table 2 presents the correlations between the 2 types of COC measures. The 3 claims-based COC indicators were highly correlated with each other, with correlation coefficients (γ) ranging from 0.782 to 0.954. For the patient-reported COC measures, the correlation between the 3-dimensional measures was low to moderate, with γ ranging from 0.181 to 0.441. Furthermore, all claims-based COC indicators used in this study were weakly correlated with the 3 patient-reported COC measures, with γ ranging from –0.057 to 0.090.

Table 3 presents the average marginal effects of claims-based and patient-reported COC on objectively measured health care outcomes from a logit model. Participants with intermediate or high claims-based UPC index scores were significantly associated with a lower likelihood of hospitalizations and ED visits than those with low UPC index scores. Participants with a high UPC index score were 8.3% less likely to experience hospitalizations than their counterparts with a low UPC index score. The claims-based COCI exhibited similar results, except for participants with intermediate COCI scores, who showed no significant difference in hospitalization. However, the claims-based SECON index was not significantly associated with the likelihood of hospitalizations or ED visits. Furthermore, most patient-reported COC measures, including longitudinal, informational, and interpersonal continuity, were not significantly associated with the likelihood of hospitalizations or ED visits.

Table 4 presents the average marginal effects of claims-based and patient-reported COC on health care outcomes measured subjectively using an ordered logit model. Regarding the level of satisfaction with care, none of the claims-based COC indicators were significantly associated with the level of patient satisfaction. In contrast, participants reporting intermediate or high levels of COC measures for the longitudinal, informational, and interpersonal dimensions had significantly higher patient satisfaction than those reporting low COC scores, except for intermediate levels of longitudinal COC. Participants with a high level of interpersonal COC were 33.9% more likely to experience high satisfaction with care than those with a low level of interpersonal COC and were approximately 13.6% less likely to experience low satisfaction with care. Regarding the level of perceived lack of coordination, participants with intermediate or high claims-based UPC index scores were less likely to report a lack of coordination than those with low UPC index scores. Similar results were found for COCI, but not for the SECON indicator. On the contrary, participants with intermediate or high levels of informational and interpersonal COC were less likely to perceive a lack of coordination than those reporting low COC scores, except for those with intermediate levels of informational COC.

Sensitivity Analysis

We conducted several sensitivity analyses to improve the robustness of this study. First, we conducted PCA with varimax rotation to distinguish between multidimensional patient-reported COC measures and claims-based COC indicators indicating actual physician visit patterns. The results of the PCA generated 4 factors, and all factor loadings exceeded 0.4. The claims-based COC indicators represented the pattern of physician visits, which may capture another dimension of the COC (eAppendix Table 2).

Second, regarding subjective health care outcome measures, we treated patient satisfaction and perception of lack of coordination as binary variables rather than ordinal variables. The results were similar to previous results (eAppendix Table 4). Third, we considered each dimensional scale of patient-reported COC measures as a continuous variable instead of an ordinal one. Because the 3-dimensional scale of patient-reported COC scores does not have the same number of survey items, we normalized each dimensional scale of patient-reported COC measures by calculating the z score of its sum. We found that the findings were similar to previous results (eAppendix Table 5 and eAppendix Table 6). In summary, these sensitivity analyses indicated that the results of this study were robust.

DISCUSSION

This study comprehensively examined the relationship between claims-based COC indicators representing patterns of physician visits and patient-reported COC measures reflecting patients’ experiences of health care. In addition, it investigated the effects of both claims-based and patient-reported COC measures on subjective and objective health care outcomes. This study may be the first in this specific research field to do so. We found that the correlation of claims-based COC indicators and patient-reported COC measures was quite low. The results also indicated that claims-based and patient-reported COC measures had different effects on subjective and objective health care outcomes.

Regarding the relationship among various claims-based COC indicators and multidimensional patient-reported COC measures, this study found that the correlation was very low. The results were similar to those reported by previous studies revealing that claims-based COC indicators have limited correspondence with patient-reported COC measures.^17,19,20 However, Nyweide demonstrated that when patients had a usual care provider, a high level of claims-based COC measures was associated with better patient-reported COC.¹⁸ Additionally, claims-based COC indicators could not represent the patient-reported COC in this study, which may be due to the characteristics of the participants of this study. For example, we included participants 60 years or older, and many of them had multiple chronic conditions (eg, 39.60% of the participants had a CCI score ≥ 2) and received care from various specialists. Thus, the pattern of their physician visits might lead to low scores in claims-based COC indicators^37,38 even though the participants perceived good information sharing, longitudinal continuity, and interpersonal relationships with their health care providers. In addition, COC possesses multiple dimensions.^5-7 We considered that the claims-based COC indicators represent the actual physician visit patterns, which may be a unique dimension in measuring COC.

Regarding the relationship among the various types of claims-based COC and health care outcomes, we noted that participants with higher UPC and COCI scores were less likely to be hospitalized or to have ED visits with dose-response trends. These findings were consistent with those of previous studies and systematic reviews.^21-28 However, Bentler et al found no significant dose-response trends for the effects of claims-based COC on the risk of ED visits. They also held that claims-based COC measures increased the risk of hospitalization and suggested that the unexpected association might be attributable to the confounding effect of unmeasured characteristics such as comorbidity or disease severity.³⁰ In addition, we found that the claims-based SECON index indicated that the sequence of physician visits was not associated with the likelihood of hospitalizations or ED visits. The SECON index may be useful for considering immediate follow-up with the same provider and has been used less frequently to measure COC in previous studies.⁹

Moreover, we found no association between patient-reported COC and objective outcomes. The results implied that patient-reported COC with the most frequently seen physicians was not associated with the likelihood of hospitalizations or ED visits. A plausible explanation is that the relationship between patient-reported COC and objective health care outcomes such as the risk of ED visits or hospitalizations is quite distant. Other outcome measures such as medication adherence may be more sensitive indicators than hospitalizations or ED visits. Previous studies have reported a positive association between patient-reported COC and medication adherence.^29,39 However, Bentler et al established that instrumental and affective patient-reported COC were associated with a decreased risk of ED visits, and only affective patient-reported COC was associated with reduced hospitalization.³⁰

Furthermore, regarding the 2 subjective health care outcome measures of patient satisfaction and perceived lack of care coordination, this study discovered that claims-based COC indicators were not associated with patient satisfaction with care. Previous research has reported mixed results regarding the relationship between claims-based COC and patient satisfaction.^17,40-44 Rodriguez et al examined the association between patient-reported and administratively derived continuity measures and the quality of physician-patient interaction, and they found positive relationships between the COC measures and the quality of interaction.¹⁷ In contrast, this study demonstrated that claims-based COC indicators were associated with a perceived lack of care coordination; participants with a higher UPC index and COCI scores representing a more concentrated or less dispersed physician visit pattern were less likely to report a lack of care coordination. In Taiwan, the lack of formal referral arrangements may hamper information exchange and communication regarding a patient’s condition among multiple physicians. Therefore, the results of this study support the argument that a better COC represented by a less-dispersed visit pattern is associated with a lower likelihood of perceiving a lack of care coordination.

Finally, regarding the relationship between patient-reported COC and subjective health care outcomes, we found that informational and interpersonal continuity measures were significantly associated with patient satisfaction with care and perception of lack of coordination. The findings were consistent with those from previous studies conducted in health care systems with referral arrangements.^1,7,45,46 Additionally, we proved that patient-reported interpersonal continuity had a stronger effect on patient satisfaction and perceived lack of coordination than longitudinal and informational continuity. The results corresponded with the argument proposed by previous research that interpersonal continuity was the most important aspect of COC.^7,32,47 However, the literature has noted that a complicated relationship exists between patient-reported COC and satisfaction with care.^1,17,45,46 Patients with a high level of COC tend to be more satisfied with the health care they receive, and a comfortable relationship with health care providers may promote their experience of COC.⁴⁸ In addition, Rodriguez et al argued that COC measures rely on self-reported data and might be subject to response bias; they may, therefore, overestimate the association between patient-reported continuity measures and subjective health care outcomes.¹⁷ The interpretation of the relationship between patient-reported COC and satisfaction with care should be made with caution.

Limitations

This study has several limitations. First, due to the nature of the cross-sectional study design, we were unable to determine the temporal relationship between the 2 types of COC measures and subjective and objective health care outcomes. No causal relationships could be drawn from these findings. Second, this study focuses on older adults. The effects of COC may depend on the health needs and characteristics of the participants, which may limit the generalization of the study results to other age groups. Third, we successfully linked survey data with NHI claims data and facilitated the inclusion of COC measures and confounding variables. However, we were unable to obtain unobserved characteristics or unavailable variables, which might have simultaneously affected patients’ COC and health care outcomes. Therefore, several proxy indicators of health status were incorporated into the regression models, such as the CCI score. These proxy variables might have decreased bias due to confounders that were not included in the regression models. Furthermore, the longitudinal relationship between patients and their most frequently seen physician was one of the dimensions in measuring the concept of continuity of care^5-7; therefore, this study did not include the duration of the ongoing patient-physician relationship, which might be a confounder in the regression model.

CONCLUSIONS

This study established that claims-based COC indicating patients’ physician visit patterns may not reflect the multidimensional COC, including longitudinal, informational, and interpersonal continuity. Claims-based and patient-reported COC measures have different effects on subjective and objective health care outcomes. Whether claims-based COC indicators, representing actual physician visits of patients, can be considered a unique dimension in measuring COC is worthy of further investigation.

Author Affiliations: Department of Public Health, College of Medicine, Fu-Jen Catholic University (CCC), New Taipei City, Taiwan; Institute of Health Policy & Management, College of Public Health, and Population Health Research Center, National Taiwan University (SHC), Taipei, Taiwan.

Source of Funding: This study was supported by the grant from the National Health Research Institutes (NHRI-EX-110-11001PI) in Taiwan. The funding source had no role in the study.

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 (CCC, SHC); acquisition of data (SHC); analysis and interpretation of data (CCC, SHC); drafting of the manuscript (CCC); critical revision of the manuscript for important intellectual content (SHC); statistical analysis (CCC); provision of patients or study materials (SHC); obtaining funding (SHC); and supervision (SHC).

Address Correspondence to: Shou-Hsia Cheng, PhD, National Taiwan University, 17 Xu-Zhou Rd, Taipei, Taiwan, 100. Email: shcheng@ntu.edu.tw.

REFERENCES

1. Saultz JW, Albedaiwi W. Interpersonal continuity of care and patient satisfaction: a critical review. Ann Fam Med. 2004;2(5):445-451. doi:10.1370/afm.91

2. Saultz JW, Lochner J. Interpersonal continuity of care and care outcomes: a critical review. Ann Fam Med. 2005;3(2):159-166. doi:10.1370/afm.285

3. 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

4. Iglehart JK. No place like home—testing a new model of care delivery. N Engl J Med. 2008;359(12):1200-1202. doi:10.1056/NEJMp0805225

5. Freeman G, Shepperd S, Robinson I, Ehrich K, Richards S. Report of a scoping exercise for the National Co-ordinating Centre for NHS Service Delivery and Organisation R & D (NCCSDO). Revised May 2001. Accessed December 13, 2021. National Institute for Health and Care Research. https://njl-admin.nihr.ac.uk/document/download/2027166

6. Haggerty JL, Reid RJ, Freeman GK, Starfield BH, Adair CE, McKendry R. Continuity of care: a multidisciplinary review. BMJ. 2003;327(7425):1219-1221. doi:10.1136/bmj.327.7425.1219

7. Saultz JW. Defining and measuring interpersonal continuity of care. Ann Fam Med. 2003;1(3):134-143. doi:10.1370/afm.23

8. Alpert JJ, Kosa J, Haggerty RJ, Robertson LS, Heagarty MC. Attitudes and satisfactions of low-income families receiving comprehensive pediatric care. Am J Public Health Nations Health. 1970;60(3):499-506. doi:10.2105/ajph.60.3.499

9. Jee SH, Cabana MD. Indices for continuity of care: a systematic review of the literature. Med Care Res Rev. 2006;63(2):158-188. doi:10.1177/1077558705285294

10. Uijen AA, Schers HJ, van Weel C. Continuity of care preferably measured from the patients’ perspective. J Clin Epidemiol. 2010;63(9):998-999. doi:10.1016/j.jclinepi.2010.03.015

11. Gulliford MC, Naithani S, Morgan M. Measuring continuity of care in diabetes mellitus: an experience-based measure. Ann Fam Med. 2006;4(6):548-555. doi:10.1370/afm.578

12. Gulliford M, Cowie L, Morgan M. Relational and management continuity survey in patients with multiple long-term conditions. J Health Serv Res Policy. 2011;16(2):67-74. doi:10.1258/jhsrp.2010.010015

13. Uijen AA, Schellevis FG, van den Bosch WJHM, Mokkink HGA, van Weel C, Schers HJ. Nijmegen Continuity Questionnaire: development and testing of a questionnaire that measures continuity of care. J Clin Epidemiol. 2011;64(12):1391-1399. doi:10.1016/j.jclinepi.2011.03.006

14. Uijen AA, Schers HJ, Schellevis FG, Mokkink HGA, van Weel C, van den Bosch WJ. Measuring continuity of care: psychometric properties of the Nijmegen Continuity Questionnaire. Br J Gen Pract. 2012;62(600):e949-e957. doi:10.3399/bjgp12X652364

15. Haggerty JL, Roberge D, Freeman GK, Beaulieu C, Bréton M. Validation of a generic measure of continuity of care: when patients encounter several clinicians. Ann Fam Med. 2012;10(5):443-451. doi:10.1370/afm.1378

16. Aller MB, Vargas I, Garcia-Subirats I, et al. A tool for assessing continuity of care across care levels: an extended psychometric validation of the CCAENA questionnaire. Int J Integr Care. 2013;13:e050. doi:10.5334/ijic.1160

17. Rodriguez HP, Marshall RE, Rogers WH, Safran DG. Primary care physician visit continuity: a comparison of patient-reported and administratively derived measures. J Gen Intern Med. 2008;23(9):1499-1502. doi:10.1007/s11606-008-0692-z

18. Nyweide DJ. Concordance between continuity of care reported by patients and measured from administrative data. Med Care Res Rev. 2014;71(2):138-155. doi:10.1177/1077558713505685

19. Bentler SE, Morgan RO, Virnig BA, Wolinsky FD. Do claims-based continuity of care measures reflect the patient perspective? Med Care Res Rev. 2014;71(2):156-173. doi:10.1177/1077558713505909

20. DuGoff EH. Continuity of care in older adults with multiple chronic conditions: how well do administrative measures correspond with patient experiences? J Healthc Qual. 2018;40(3):120-128. doi:10.1097/JHQ.0000000000000051

21. Gill JM, Mainous AG III. The role of provider continuity in preventing hospitalizations. Arch Fam Med. 1998;7(4):352-357. doi:10.1001/archfami.7.4.352

22. Mainous AG III, Gill JM. The importance of continuity of care in the likelihood of future hospitalization: is site of care equivalent to a primary clinician? Am J Public Health. 1998;88(10):1539-1541.
doi:10.2105/ajph.88.10.1539

23. Christakis DA, Mell L, Koepsell TD, Zimmerman FJ, Connell FA. Association of lower continuity of care with greater risk of emergency department use and hospitalization in children. Pediatrics. 2001;107(3):524-529. doi:10.1542/peds.107.3.524

24. Menec VH, Sirski M, Attawar D, Katz A. Does continuity of care with a family physician reduce hospitalizations among older adults? J Health Serv Res Policy. 2006;11(4):196-201. doi:10.1258/135581906778476562

25. Knight JC, Dowden JJ, Worrall GJ, Gadag VG, Murphy MM. Does higher continuity of family physician care reduce hospitalizations in elderly people with diabetes? Popul Health Manag. 2009;12(2):81-86. doi:10.1089/pop.2008.0020

26. Cheng SH, Chen CC, Hou YF. A longitudinal examination of continuity of care and avoidable hospitalization: evidence from a universal coverage health care system. Arch Intern Med. 2010;170(18):1671-1677.
doi:10.1001/archinternmed.2010.340

27. Lin W, Huang IC, Wang SL, Yang MC, Yaung CL. Continuity of diabetes care is associated with avoidable hospitalizations: evidence from Taiwan’s National Health Insurance scheme. Int J Qual Health Care. 2010;22(1):3-8. doi:10.1093/intqhc/mzp059

28. Worrall G, Knight J. Continuity of care is good for elderly people with diabetes: retrospective cohort study of mortality and hospitalization. Can Fam Physician. 2011;57(1):e16-e20.

29. Uijen AA, Bosch M, van den Bosch WJHM, Bor H, Wensing M, Schers HJ. Heart failure patients’ experiences with continuity of care and its relation to medication adherence: a cross-sectional study. BMC Fam Pract. 2012;13:86. doi:10.1186/1471-2296-13-86

30. Bentler SE, Morgan RO, Virnig BA, Wolinsky FD. The association of longitudinal and interpersonal continuity of care with emergency department use, hospitalization, and mortality among Medicare beneficiaries. PLoS One. 2014;9(12):e115088. doi:10.1371/journal.pone.0115088

31. Chen CC, Chiang YC, Lin YC, Cheng SH. Continuity of care and coordination of care: can they be differentiated? Int J Integr Care. 2023;23(1):10. doi:10.5334/ijic.6467

32. Breslau N, Reeb KG. Continuity of care in a university-based practice. J Med Educ. 1975;50(10):965-969. doi:10.1097/00001888-197510000-00006

33. Bice TW, Boxerman SB. A quantitative measure of continuity of care. Med Care. 1977;15(4):347-349. doi:10.1097/00005650-197704000-00010

34. Steinwachs DM. Measuring provider continuity in ambulatory care: an assessment of alternative approaches. Med Care. 1979;17(6):551-565. doi:10.1097/00005650-197906000-00001

35. Chen CC, Tseng CH, Cheng SH. Continuity of care, medication adherence, and health care outcomes among patients with newly diagnosed type 2 diabetes: a longitudinal analysis. Med Care. 2013;51(3):231-237. doi:10.1097/MLR.0b013e31827da5b9

36. D’Hoore W, Bouckaert A, Tilquin C. Practical considerations on the use of the Charlson comorbidity index with administrative data bases. J Clin Epidemiol. 1996;49(12):1429-1433. doi:10.1016/s0895-4356(96)00271-5

37. Maciejewski ML, Powers BJ, Sanders LL, et al. The intersection of patient complexity, prescriber continuity and acute care utilization. J Gen Intern Med. 2014;29(4):594-601. doi:10.1007/s11606-013-2746-0

38. Hempstead K, Delia D, Cantor JC, Nguyen T, Brenner J. The fragmentation of hospital use among a cohort of high utilizers: implications for emerging care coordination strategies for patients with multiple chronic conditions. Med Care. 2014;52(suppl 3):S67-S74. doi:10.1097/MLR.0000000000000049

39. Brookhart MA, Patrick AR, Schneeweiss S, et al. Physician follow-up and provider continuity are associated with long-term medication adherence: a study of the dynamics of statin use. Arch Intern Med. 2007;167(8):847-852. doi:10.1001/archinte.167.8.847

40. Shortell SM, Richardson WC, LoGerfo LP, Diehr P, Weaver B, Green KE. The relationships among dimensions of health services in two provider systems: a causal model approach. J Health Soc Behav. 1977;18(2):139-159.

41. Breslau N, Haug MR. Service delivery structure and continuity of care: a case study of a pediatric practice in process of reorganization. J Health Soc Behav. 1976;17(4):339-352.

42. Shear CL, Gipe BT, Mattheis JK, Levy MR. Provider continuity and quality of medical care. a retrospective analysis of prenatal and perinatal outcome. Med Care. 1983;21(12):1204-1210. doi:10.1097/00005650-198312000-00007

43. Flocke SA. Measuring attributes of primary care: development of a new instrument. J Fam Pract. 1997;45(1):64-74.

44. Adler R, Vasiliadis A, Bickell N. The relationship between continuity and patient satisfaction: a systematic review. Fam Pract. 2010;27(2):171-178. doi:10.1093/fampra/cmp099

45. Hjortdahl P, Laerum E. Continuity of care in general practice: effect on patient satisfaction. BMJ. 1992;304(6837):1287-1290. doi:10.1136/bmj.304.6837.1287

46. Fan VS, Burman M, McDonell MB, Fihn SD. Continuity of care and other determinants of patient satisfaction with primary care. J Gen Intern Med. 2005;20(3):226-233. doi:10.1111/j.1525-1497.2005.40135.x

47. Freeman GK, Olesen F, Hjortdahl P. Continuity of care: an essential element of modern general practice? Fam Pract. 2003;20(6):623-627. doi:10.1093/fampra/cmg601

48. Pandhi N, Bowers B, Chen FP. A comfortable relationship: a patient-derived dimension of ongoing care. Fam Med. 2007;39(4):266-273.