Impact of care provider network characteristics on patient outcomes: Usage of social network analysis and a multi-scale community detection
Autoři:
Mina Ostovari aff001; Denny Yu aff002
Působiště autorů:
Value Institute, Christiana Care Health System, Newark, Delaware, United States of America
aff001; School of Industrial Engineering, Purdue University, West Lafayette, Indiana, United States of America
aff002
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0222016
Souhrn
Objective
We assess healthcare provider collaboration and the impact on patient outcomes using social network analysis, a multi-scale community detection algorithm, and generalized estimating equations.
Material and methods
A longitudinal analysis of health claims data of a large employer over a 3 year period was performed to measure how provider relationships impact patient outcomes. The study cohort included 4,230 patients with 167 providers. Social network analysis with a multi-scale community detection algorithm was used to identify groups of healthcare providers more closely working together. Resulting measures of provider collaboration were: 1) degree, 2) betweenness, and 3) closeness centrality. The three patient outcome measures were 1) emergency department visit, 2) inpatient hospitalization, and 3) unplanned hospitalization. Relationships between provider collaboration and patient outcomes were assessed using generalized estimating equations. General practitioner, family practice, and internal medicine were labeled as primary care. Cardiovascular, endocrinologists, etc. were labeled as specialists, and providers such as radiology and social workers were labeled as others.
Results
Higher connectedness (degree) and higher access (closeness) to other providers in the community were significant for reducing inpatient hospitalization and emergency department visits. Patients of specialists (e.g. cardiovascular) and providers specified as others (e.g. social worker) had higher rate of hospitalization and emergency department visits compared to patients of primary care providers.
Conclusion
Application of social network analysis for developing healthcare provider networks can be leveraged by community detection algorithms and predictive modeling to identify providers’ network characteristics and their impacts on patient outcomes. The proposed framework presents multi-scale measures to assess characteristics of healthcare providers and their impact on patient outcomes. This approach can be used by implementation experts for informed decision-making regarding the design of insurance coverage plans, and wellness promotion programs. Health services researchers can use the study approach for assessment of provider collaboration and impacts on patient outcomes.
Klíčová slova:
Medicine and health sciences – Health care – Health care facilities – Hospitals – Hospitalizations – Patients – Inpatients – Health care providers – Primary care – Critical care and emergency medicine – Computer and information sciences – Network analysis – Centrality – Physical sciences – Mathematics – Applied mathematics – Algorithms – Research and analysis methods – Simulation and modeling
Zdroje
1. Chronic diseases in America | CDC [Internet]. 2019 [cited 2019 Jan 29]. Available from: https://www.cdc.gov/chronicdisease/resources/infographic/chronic-diseases.htm
2. National Center for Chronic Disease Prevention and Health Promotion. Promoting health during the Holidays 7 Tips to stay healthy [Internet]. Centers for Disease Control and Prevention. 2018 [cited 2019 Jan 29]. Available from: https://www.cdc.gov/chronicdisease/index.htm
3. Wong ND, Lopez V, Tang S, Williams GR. Prevalence, treatment, and control of combined hypertension and hypercholesterolemia in the United States. Am J Cardiol. 2006 Jul 15;98(2):204–8. doi: 10.1016/j.amjcard.2006.01.079 16828593
4. O’Brien T, Nguyen TT, Zimmerman BR. Hyperlipidemia and diabetes mellitus. Mayo Clin Proc. 1998 Oct 1;73(10):969–76. doi: 10.4065/73.10.969 9787748
5. Ivbijaro GO, Enum Y, Khan AA, Lam SS-K, Gabzdyl A. Collaborative care: models for treatment of patients with complex medical-psychiatric conditions. Curr Psychiatry Rep. 2014;16(11):506–506. doi: 10.1007/s11920-014-0506-4 25218604
6. Wagner EH, Glasgow RE, Davis C, Bonomi AE, Provost L, McCulloch D, et al. Quality improvement in chronic illness care: a collaborative approach. Jt Comm J Qual Improv. 2001;27(2):63–80. 11221012
7. Jaarsma T. Inter-professional team approach to patients with heart failure. Heart Br Card Soc. 2005 Jun;91(6):832–8.
8. Boykin A, Wright D, Stevens L, Gardner L. Interprofessional care collaboration for patients with heart failure. Am J Health Syst Pharm. 2018 Jan 1;75(1):e45–9. doi: 10.2146/ajhp160318 29273612
9. Morley L, Cashell A. Collaboration in Health Care. J Med Imaging Radiat Sci. 2017 Jun 1;48(2):207–16. doi: 10.1016/j.jmir.2017.02.071 31047370
10. Wrobel JS, Charns MP, Diehr P, Robbins JM, Reiber GE, Bonacker KM, et al. The relationship between provider coordination and diabetes-related foot outcomes. Diabetes Care. 2003;26(11):3042–7. doi: 10.2337/diacare.26.11.3042 14578237
11. Feldman LS, Costa LL, Feroli ER Jr, Nelson T, Poe SS, Frick KD, et al. Nurse‐pharmacist collaboration on medication reconciliation prevents potential harm. J Hosp Med. 2012;7(5):396–401. doi: 10.1002/jhm.1921 22371379
12. Franklin CM, Bernhardt JM, Lopez RP, Long-Middleton ER, Davis S. Interprofessional teamwork and collaboration between community health workers and healthcare teams: An integrative review. Health Serv Res Manag Epidemiol [Internet]. 2015 Mar 16;2(2). Available from: https://www.ncbi.nlm.nih.gov/pubmed/28462254
13. Valentine MA, Nembhard IM, Edmonson AC. Measuring teamwork in health care settings: a review of survey instruments. Med Care. 2015;54(3):e16–30.
14. Walters SJ, Stern C, Roberston-Malt S. The measurement of collaboration within healthcare settings: a systematic review of measurement properties of instruments. JBI Database Syst Rev Implement Rep. 2016;14(4):138–97.
15. Sexton JB, Makary MA, Tersigni AR, Pryor D, Hendrich A,FAAN, Thomas EJ, et al. Teamwork in the operating room: frontline perspectives among hospitals and operating room personnel. Anesthesiol J Am Soc Anesthesiol. 2006 Nov 1;105(5):877–84.
16. Davenport DL, Henderson WG, Mosca CL, Khuri SF, Mentzer RM Jr. Risk-adjusted morbidity in teaching hospitals correlates with reported levels of communication and collaboration on surgical teams but not with scale measures of teamwork climate, safety climate, or working conditions. J Am Coll Surg. 2007 Dec 1;205(6):778–84. doi: 10.1016/j.jamcollsurg.2007.07.039 18035261
17. Jones TL, Baxter MAJ, Khanduja V. A quick guide to survey research. Ann R Coll Surg Engl. 2013 Jan;95(1):5–7. doi: 10.1308/003588413X13511609956372 23317709
18. Kennedy J, Vargus B. Challenges in survey research and their implications for philanthropic studies research. Nonprofit Volunt Sect Qtly. 2001;30:483–94.
19. Collie A, Prang K-H. Patterns of healthcare service utilisation following severe traumatic brain injury: An idiographic analysis of injury compensation claims data. Injury. 2013 Nov 1;44(11):1514–20. doi: 10.1016/j.injury.2013.03.006 23566704
20. Elbers NA, Cuijpers P, Akkermans AJ, Collie A, Ruseckaite R, Bruinvels DJ. Do claim factors predict health care utilization after transport accidents? Accid Anal Prev. 2013 Apr 1;53:121–6. doi: 10.1016/j.aap.2013.01.007 23411157
21. Hewner S, Chang Y-P, Xue Y, Somayaji D, Casucci S. Exploring Medicaid claims data to understand predictors of healthcare utilization and mortality for Medicaid individuals with or without a diagnosis of lung cancer: a feasibility study. Transl Behav Med. 2018 May 23;8(3):400–8. doi: 10.1093/tbm/iby023 29800414
22. Barnett ML, Landon BE, O’Malley AJ, Keating NL, Christakis N. Mapping physician networks with self-reported and administrative data. Health Serv Res. 2011;46(5):1592–609. doi: 10.1111/j.1475-6773.2011.01262.x 21521213
23. Landon BE, Keating NL, Barnett ML, Onnela J-P, Paul S, O’Malley AJ, et al. Variation in patient-sharing networks of physicians across the United States. JAMA. 2012;308(3):265–73. doi: 10.1001/jama.2012.7615 22797644
24. Barnett ML, Christakis N, O’Malley AJ, Onnela J-P, Keating NL, Landon BE. Physician patient-sharing networks and the cost and intensity of care in US hospitals. Med Care. 2012;50(2):152–60. doi: 10.1097/MLR.0b013e31822dcef7 22249922
25. Pollack C, Lemke K, Roberts E, Einer J. Patient sharing and quality of care: measuring outcomes of care coordination using claims data. Med Care. 2015;53(4):317–23. doi: 10.1097/MLR.0000000000000319 25719430
26. Casalino LP, Pesko MF, Ryan AM, Nyweide DJ, Iwashyna TJ, Sun X, et al. Physician networks and ambulatory care-sensitive admissions. Med Care. 2015;53(6):534–41. doi: 10.1097/MLR.0000000000000365 25906013
27. Pollack CE, Weissman G, Bekelman J, Liao K, Armstrong K. Physician social networks and variation in prostate cancer treatment in three cities. Health Serv Res. 2012 Feb;47(1 Pt 2):380–403.
28. Carson MB, Scholtens DM, Frailey CN, Gravenor SJ, Powell ES, Wang AY, et al. Characterizing teamwork in cardiovascular care outcomes: a network analytics approach. Circ Cardiovasc Qual Outcomes. 2016 Nov;9(6):670–8. doi: 10.1161/CIRCOUTCOMES.116.003041 28051772
29. Landon BE, Keating NL, Onnela J-P, Zaslavsky AM, Christakis N, O’Malley AJ. Patient-sharing networks of physicians and health care utilization and spending among Medicare beneficiaries. JAMA Intern Med. 2017;178(1):66–73.
30. Uddin S, Kelaher M, Piraveenan M. Impact of physician community structure on healthcare outcomes. Driv Reform Digit Health Everyones Bus. 2015;214:152–8.
31. Landon BE, Onnela J-P, Keating NL, Barnett M, Paul S, O’Malley AJ, et al. Using administrative data to identify naturally occurring networks of physicians. Med Care. 2013;51(8):715–21. doi: 10.1097/MLR.0b013e3182977991 23807593
32. Pollack CE, Wang H, Bekelman JE, Weissman G, Epstein AJ, Liao K, et al. Physician social networks and variation in rates of complications after radical prostatectomy. Value Health. 2014;17:611–8. doi: 10.1016/j.jval.2014.04.011 25128055
33. Fortunato S, Barthélemy M. Resolution limit in community detection. Proc Natl Acad Sci. 2007 Jan 2;104(1):36. doi: 10.1073/pnas.0605965104 17190818
34. Mandl KD, Olson KL, Mines D, Liu C, Tian F. Provider collaboration: cohesion, constellations, and shared patients. J Gen Intern Med. 2014;29(11):1499–505. doi: 10.1007/s11606-014-2964-0 25060655
35. DuGoff EH, Fernandes-Taylor S, Weissman GE, Huntley JH, Pollack CE. A scoping review of patient-sharing network studies using administrative data. Transl Behav Med. 2018 Jul 17;8(4):598–625. doi: 10.1093/tbm/ibx015 30016521
36. Johns Hopkins. The Johns Hopkins ACG® system version 11.0 technical reference guide. Bloomberg School of Public Health: Johns Hopkins; 2014.
37. Ostovari M, Yu D, Yih Y, Steele-Morris CJ. Impact of an onsite clinic on utilization of preventive services. J Occup Environ Med. 2017;59(7):615–23. doi: 10.1097/JOM.0000000000001034 28590271
38. 2014 ICD-9-CM diagnosis codes 250.*: diabetes mellitus [Internet]. [cited 2018 Aug 20]. Available from: http://www.icd9data.com/2014/Volume1/240-279/249-259/250/default.htm
39. 2012 ICD-9-CM Diagnosis Codes 401.*: Essential hypertension [Internet]. [cited 2019 Jan 30]. Available from: http://www.icd9data.com/2012/Volume1/390-459/401-405/401/default.htm
40. 2014 ICD-9-CM Diagnosis Code 272.4: Other and unspecified hyperlipidemia [Internet]. 2014 [cited 2019 Jan 30]. Available from: http://www.icd9data.com/2014/Volume1/240-279/270-279/272/272.4.htm
41. Ostovari M, Steele-Morris C-J, Griffin PM, Yu D. Data-driven modeling of diabetes care teams using social network analysis. J Am Med Inform Assoc [Internet]. 2019 May 2 [cited 2019 May 9]; Available from: https://doi.org/10.1093/jamia/ocz022
42. Ostovari M, Yu D, Steele-Morris CJ. Identifying key players in the care process of patients with diabetes using social network analysis and administrative data. Annu Symp Proc. 2018 Dec 5;1435–41.
43. Le Martelot E, Hankin C. Multi-scale community detection using stability optimisation within greedy algorithms. ArXiv Prepr ArXiv12013307. 2012;
44. Luke D. A user’s guide to network analysis in R. Switzerland: Springer, Cham; 2015.
45. Brandes U, Borgatti SP, Freeman LC. Maintaining the duality of closeness and betweenness centrality. Soc Netw. 2016 Jan 1;44:153–9.
46. Atkins DC, Baldwin SA, Zheng C, Gallop RJ, Neighbors C. A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychol Addict Behav J Soc Psychol Addict Behav. 2013 Mar;27(1):166–77.
47. Du J, Park Y-T, Theera-Ampornpunt N, McCullough JS, Speedie SM. The use of count data models in biomedical informatics evaluation research. J Am Med Inform Assoc JAMIA. 2012;19(1):39–44. doi: 10.1136/amiajnl-2011-000256 21715429
48. Desmarais BA, Harden JJ. Testing for zero inflation in count models: Bias correction for the Vuong test. Stata J. 2013;13(4):810–35.
49. Hardin JW, Hilbe JM. Generalized estimating equations. Chapman and Hall/CRC; 2002.
50. Csardi MG. Package ‘igraph.’ 2013.
51. Wickham H, Chang W. devtools: tools to make developing R packages easier. 2016.
52. Lambiotte R, Delvenne J-C, Barahona M. Laplacian dynamics and multiscale modular structure in networks. ArXiv Prepr ArXiv08121770. 2008;
53. Shrivastav M, Gibson W, Shrivastav R, Elzea K, Khambatta C, Sonawane R, et al. Type 2 diabetes management in primary care: The role of retrospective, professional continuous glucose monitoring. Diabetes Spectr. 2018 Aug 1;31(3):279. doi: 10.2337/ds17-0024 30140145
54. Rudnick KV, Sackett DL, Hirst S, Holmes C. Hypertension: The family physician’s role. Can Fam Physician Med Fam Can. 1978 May;24:477–84.
55. Nelson RH. Hyperlipidemia as a risk factor for cardiovascular disease. Prim Care. 2013 Mar;40(1):195–211. doi: 10.1016/j.pop.2012.11.003 23402469
56. Lewis Hunter AE, Spatz ES, Bernstein SL, Rosenthal MS. Factors influencing hospital admission of non-critically ill patients presenting to the emergency department: a cross-sectional study. J Gen Intern Med. 2016 Jan;31(1):37–44. doi: 10.1007/s11606-015-3438-8 26084975
57. Geva A, Olson KL, Liu C, Mandl KD. Provider connectedness to other providers reduces risk of readmission after hospitalization for heart failure. Med Care Res Rev. 2017 Jul 8;76(1):115–28. doi: 10.1177/1077558717718626 29148301
Článek vyšel v časopise
PLOS One
2019 Číslo 9
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy