The nature of teamwork in healthcare is complex and interdisciplinary
Supporting Lecture:
Review the following lecture:
Developing High-Performing Teams and Energizing Staff
Project
The project assignment provides a forum for analyzing and evaluating relevant topics of this week on the basis of the course competencies covered.
Introduction
Building a high-performing team is a process. The leaders who cultivate and build such teams understand the importance of common goals and success in achieving those goals. As a leader, you will ultimately strive to build your own high-performing team.
Tasks
Read the following article for assistance:
Characterizing Teamwork in Cardiovascular Care Outcomes: A Network Analytics Approach
You will make a plan to develop your high-performing team. In developing your team, be sure to include the following:
Describe how you will communicate common goals.
Describe how you will address conflict in a way that is positive within the team.
Assist each member embrace his or her role within the team.
Explain the things you will do to energize your team.
Be sure to provide specific examples and rationale behind your steps.
To support your work, use your course and textbook readings and also use the South University Online Library. As in all assignments, cite your sources in your work and provide references for the citations in APA format.
Characterizing Teamwork in Cardiovascular Care Outcomes:
Heres reading before doing homework
A Network Analytics Approach
Matthew B. Carson , Denise M. Scholtens , Conor N. Frailey , Stephanie J. Gravenor , Emilie S. Powell , Amy Y. Wang , Gayle Shier Kricke , Faraz S. Ahmad , R. Kannan Mutharasan , and Nicholas D. Soulakis
Originally published1 Nov 2016Circulation: Cardiovascular Quality and Outcomes. 2016;9:670–678
Abstract
Background—
The nature of teamwork in healthcare is complex and interdisciplinary, and provider collaboration based on shared patient encounters is crucial to its success. Characterizing the intensity of working relationships with risk-adjusted patient outcomes supplies insight into provider interactions in a hospital environment.
Methods and Results—
We extracted 4 years of patient, provider, and activity data for encounters in an inpatient cardiology unit from Northwestern Medicine’s Enterprise Data Warehouse. We then created a provider–patient network to identify healthcare providers who jointly participated in patient encounters and calculated satisfaction rates for provider–provider pairs. We demonstrated the application of a novel parameter, the shared positive outcome ratio, a measure that assesses the strength of a patient-sharing relationship between 2 providers based on risk-adjusted encounter outcomes. We compared an observed collaboration network of 334 providers and 3453 relationships to 1000 networks with shared positive outcome ratio scores based on randomized outcomes and found 188 collaborative relationships between pairs of providers that showed significantly higher than expected patient satisfaction ratings. A group of 22 providers performed exceptionally in terms of patient satisfaction. Our results indicate high variability in collaboration scores across the network and highlight our ability to identify relationships with both higher and lower than expected scores across a set of shared patient encounters.
Conclusions—
Satisfaction rates seem to vary across different teams of providers. Team collaboration can be quantified using a composite measure of collaboration across provider pairs. Tracking provider pair outcomes over a sufficient set of shared encounters may inform quality improvement strategies such as optimizing team staffing, identifying characteristics and practices of high-performing teams, developing evidence-based team guidelines, and redesigning inpatient care processes.
Footnotes
The Data Supplement is available at http://circoutcomes.ahajournals.org/lookup/suppl/doi:10.1161/CIRCOUTCOMES.116.003041/-/DC1.
Correspondence to Matthew Carson, PhD, Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 750 N, Lakeshore Dr, 11th Floor, Chicago, IL 60611. E-mail [email protected]
References
1. Centers for Medicare and Medicaid Services. Readmissions Reduction Program. http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html. Accessed August, 2015.Google Scholar
2. Care Coordination Measures Atlas Update. 2014. Agency for Healthcare Research and Quality (AHRQ). http://www.ahrq.gov/professionals/prevention-chronic-care/improve/coordination/atlas2014/index.html. Accessed August, 2015.Google Scholar
3. Benham-Hutchins MM, Effken JA. Multi-professional patterns and methods of communication during patient handoffs.Int J Med Inform. 2010;79:252–267. doi: 10.1016/j.ijmedinf.2009.12.005.CrossrefMedlineGoogle Scholar
4. Weenink JW, van Lieshout J, Jung HP, Wensing M. Patient Care Teams in treatment of diabetes and chronic heart failure in primary care: an observational networks study.Implement Sci. 2011;6:66. doi: 10.1186/1748-5908-6-66.CrossrefMedlineGoogle Scholar
5. Nageswaran S, Ip EH, Golden SL, O’Shea TM, Easterling D. Inter-agency collaboration in the care of children with complex chronic conditions.Acad Pediatr. 2012;12:189–197. doi: 10.1016/j.acap.2012.02.007.CrossrefMedlineGoogle Scholar
6. Chambers D, Wilson P, Thompson C, Harden M. Social network analysis in healthcare settings: a systematic scoping review.PLoS ONE. 2012;7:e41911. doi: 10.1371/journal.pone.0041911.CrossrefMedlineGoogle Scholar
7. Ong MS, Olson KL, Cami A, Liu C, Tian F, Selvam N, Mandl KD. Provider patient-sharing networks and multiple-provider prescribing of benzodiazepines.J Gen Intern Med. 2016;31:164–71. doi: 10.1007/s11606-015-3470-8.CrossrefMedlineGoogle Scholar
8. Christakis NA, Fowler JH. Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives. 1st ed. New York, NY: Little, Brown and Co; 2009.Google Scholar
9. Pah AR, Rasmussen-Torvik LJ, Goel S, Greenland P, Kho AN. Big data: what is it and what does it mean for cardiovascular research and prevention policy.Current Cardiovascular Risk Reports. 2015;(9), 424:1–9.MedlineGoogle Scholar
10. McDonald KM, Sundaram V, Bravata DM, Lewis R, Lin N, Kraft SA, McKinnon M, Paguntalan H, Owens DK. Closing the Quality Gap: A Critical Analysis of Quality Improvement Strategies (Vol 7: Care Coordination), AHRQ Technical Reviews.Agency for Healthcare Research and Quality.Rockville, MD; 2007.Google Scholar
11. Gray JE, Davis DA, Pursley DM, Smallcomb JE, Geva A, Chawla NV. Network analysis of team structure in the neonatal intensive care unit.Pediatrics. 2010;125:e1460–e1467. doi: 10.1542/peds.2009-2621.CrossrefMedlineGoogle Scholar
12. Hripcsak G, Vawdrey DK, Fred MR, Bostwick SB. Use of electronic clinical documentation: time spent and team interactions.J Am Med Inform Assoc. 2011;18:112–117. doi: 10.1136/jamia.2010.008441.CrossrefMedlineGoogle Scholar
13. Vawdrey DK, Wilcox LG, Collins S, Feiner S, Mamykina O, Stein DM, Bakken S, Fred MR, Stetson PD. Awareness of the care team in electronic health records.Appl Clin Inform. 2011;2:395–405. doi: 10.4338/ACI-2011-05-RA-0034.CrossrefMedlineGoogle Scholar
14. Soulakis ND, Carson MB, Lee YJ, Schneider DH, Skeehan CT, Scholtens DM. Visualizing collaborative electronic health record usage for hospitalized patients with heart failure.J Am Med Inform Assoc. 2015;22:299–311. doi: 10.1093/jamia/ocu017.CrossrefMedlineGoogle Scholar
15. U.S. News & World Report’s Best Hospitals 2015–16, as published on usnews.com.Google Scholar
16. Coffman J, Yale P. Would you recommend this hospital to a friend?Bain & Company, Inc. 2007.Google Scholar
17. Singh SC, Sheth RD, Burrows JF, Rosen P. Factors influencing patient experience in pediatric neurology.Pediatr Neurol. 2016;60:37–41. doi: 10.1016/j.pediatrneurol.2016.04.002.CrossrefMedlineGoogle Scholar
18. Bible JE, Shau DN, Kay HF, Cheng JS, Aaronson OS, Devin CJ. Are low patient satisfaction scores always due to the provider? Determinants of patient satisfaction scores during spine clinic visits.Spine (Phila Pa 1976). 2016. [Epub ahead of print].Google Scholar
19. Likert R. A technique for the measurement of attitudes. Archives of psychology. New York, NY; 1932.Google Scholar
20. Wuerz RC, Milne LW, Eitel DR, Travers D, Gilboy N. Reliability and validity of a new five-level triage instrument.Acad Emerg Med. 2000;7:236–242.CrossrefMedlineGoogle Scholar
21. Starren JB, Winter AQ, Lloyd-Jones DM. Enabling a learning health system through a unified enterprise data warehouse: the experience of the Northwestern University Clinical and Translational Sciences (NUCATS) Institute.Clin Transl Sci. 2015;8:269–271. doi: 10.1111/cts.12294.CrossrefMedlineGoogle Scholar
22. Microsoft SQL Server Management Studio [computer program]. 2012. Accessed November, 2015.Google Scholar
23. The Neo4j Manual v2.2.5. http://www.neotechnology.com [computer program]. 2015. Accessed November, 2015.Google Scholar
24. Python Software Foundation. Python Language Reference, version 2.7. http://www.python.org [computer program]. 2015. Accessed November, 2015.Google Scholar
25. Py2neo version 2.0.8. http://py2neo.org/2.0/ [computer program]. 2015. Accessed November, 2015.Google Scholar
26. Hagberg AA Schult, Daniel A Swart, Pieter J. Exploring network structure, dynamics, and function using NetworkX. Paper presented at: Proceedings of the 7th Python in Science Conference (SciPy2008); August 2008; Pasadena, CA.Google Scholar
27. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. [computer program]. 2015. Accessed September, 2015.Google Scholar
28. Jaccard P. Nouvelles recherches sur la distribution florale.Bull Soc Vaudoise Sci Nat. 1908;44:223–270.Google Scholar
29. Kricke GS, Carson MB, Lee Y, Benacka C, Mutharasan RK, Ahmad FS, Kansal P, Yancy CW, Anderson AS, Soulakis ND. Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification.J Am Med Inform Assoc. 2016. DOI: 10.1093/jamia/ocw083.CrossrefGoogle Scholar
30. Haas LR, Takahashi PY, Shah ND, Stroebel RJ, Bernard ME, Finnie DM, Naessens JM. Risk-stratification methods for identifying patients for care coordination.Am J Manag Care. 2013;19:725–732.MedlineGoogle Scholar
Collepals.com Plagiarism Free Papers
Are you looking for custom essay writing service or even dissertation writing services? Just request for our write my paper service, and we'll match you with the best essay writer in your subject! With an exceptional team of professional academic experts in a wide range of subjects, we can guarantee you an unrivaled quality of custom-written papers.
Get ZERO PLAGIARISM, HUMAN WRITTEN ESSAYS
Why Hire Collepals.com writers to do your paper?
Quality- We are experienced and have access to ample research materials.
We write plagiarism Free Content
Confidential- We never share or sell your personal information to third parties.
Support-Chat with us today! We are always waiting to answer all your questions.
