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The Impact of Electronic Health Records and Teamwork on Diabetes Care Quality

The American Journal of Managed CareDecember 2015
Volume 21
Issue 12

Patients with diabetes that are cared for by primary care teams with higher cohesion experienced greater EHR-related outcome improvements, compared with patients cared for by lower cohesion teams.

ABSTRACTObjectives: Evidence of the impact electronic health records (EHRs) have on clinical outcomes remains mixed. The impact of EHRs likely depends on the organizational context in which they are used. This study focuses on one aspect of the organizational context: cohesion of primary care teams. We examined whether team cohesion among primary care team members changed the association between EHR use and changes in clinical outcomes for patients with diabetes.

Study Design: Retrospective longitudinal study.

Methods: We combined provider-reported primary care team cohesion with lab values for patients with diabetes collected during the staggered EHR implementation (2005-2009). We used multivariate regression models with patient-level fixed effects to assess whether team cohesion levels changed the association between outpatient EHR use and clinical outcomes for patients with diabetes. Subjects were comprised of 80,611 patients with diabetes, in whom we measured changes in glycated hemoglobin (A1C) and low-density lipoprotein cholesterol (LDL-C).

Results: For A1C, EHR use was associated with an average decrease of 0.11% for patients with higher-cohesion primary care teams compared with a decrease of 0.08% for patients with lower-cohesion teams (difference = 0.02% in A1C; 95% CI, 0.01%-0.03%). For LDL-C, EHR use was associated with a decrease of 2.15 mg/dL for patients with higher-cohesion primary care teams compared with a decrease of 1.42 mg/dL for patients with lower-cohesion teams (difference = 0.73 mg/dL; 95% CI, 0.41-1.11 mg/dL).

Conclusions: Patients cared for by higher cohesion primary care teams experienced modest but statistically significantly greater EHR-related health outcome improvements, compared with patients cared for by providers practicing in lower cohesion teams.

Am J Manag Care. 2015;21(12):878-884

Take-Away Points

Patients with diabetes cared for by higher cohesion primary care teams experienced modest but statistically significantly greater health outcome improvements related to electronic health records (EHRs), compared with patients cared for by providers practicing in lower cohesion teams.

  • Previous studies of the effects of EHR use on diabetes clinical outcomes have been mixed, and none examined how the organizational environment may change the EHR impact on clinical care.
  • Understanding the conditions necessary to maximize the potential benefits of EHR use is important.
  • Our results suggest that team cohesion plays a critical role in fully realizing potential gains in care quality from EHR use.

Adoption of electronic health records (EHRs) has been promoted as a policy goal to improve the quality and efficiency of the American healthcare system. Starting in 2011, qualified healthcare providers in the United States began receiving federal incentive payments through the Meaningful Use program for the specified utilization of certified EHRs.1-3 Although the meaningful use criteria were developed to target improvements in the overall quality of healthcare,4 they do not address the organizational environment in which EHRs are used. The healthcare system consists of a myriad of organizational settings that affect how various technological innovations are implemented and used.5 Recent calls for the adoption of patient-centered medical homes and the increasing demand for primary care are propelling the adoption of multidisciplinary, team-based care.6-8 Therefore, it is important to understand how the team environment in particular impacts the adoption and effectiveness of new technology. Team cohesion is a measure of the constructive work relationships among primary care team members.9-12 How well teams work together may be an important factor in helping practices maximize the potential benefits of EHRs.13

The results of previous studies of the effects of health information technology on the clinical outcomes of patients with diabetes have been mixed, with some showing improvements in low-density lipoprotein cholesterol (LDL-C) and glycated hemoglobin (A1C) values,14 and others reporting null or negative results.15-20 Differences in work environments, such as team cohesion, may help explain these conflicting findings. Work relationships are crucial for providing safe and reliable patient care and establishing the collective capacity for change, such as adopting new technologies, which demands considerable changes to the clinical work flow.21-28 Team cohesion may promote an atmosphere of more informal learning, in which members are more comfortable experimenting with the EHR and sharing best practices with each other. Consequently, cohesion among team members likely influences the success of teams at adopting EHRs and achieving desired improvements in patient care.

In prior work, we reported that the use of a commercially available federally certified outpatient EHR, within a large integrated delivery system, resulted in modest improvements in patient physiologic outcomes, measured by lipid and glycemic levels, and in fewer unfavorable clinical events such as hospitalizations.29,30 In this study, we explored the heterogeneity of these physiologic effects by examining how primary care team cohesion changes this EHR-associated improvement in clinical outcomes for patients with diabetes. We hypothesized that patients cared for by primary care teams with higher team cohesion would achieve greater improvements from EHR use compared with patients cared for by teams with lower team cohesion.


Study Setting

Table 1

This study was conducted at Kaiser Permanente Northern California (KPNC), a large, prepaid Integrated Delivery System (IDS) providing comprehensive medical care for more than 3 million members. The system receives bundled prospective payments for all medical care. Primary care clinicians worked in 110 primary care teams, across 18 medical centers. Primary care teams were created in the 1990s in an effort to redesign primary care using multidisciplinary teams. The size and composition of teams varied, but each generally included 1 or more physicians, nurse practitioners, registered nurses, health educators, pharmacists, behavioral medicine specialists, physical therapists, etc. Teams ranged in size from 5 to 37 members ().

An individual with diabetes will often receive care from multiple members of the care team. For example, a primary care physician or nurse practitioner provides routine care (“check-ups”) and acute care, a health educator teaches diabetes self-care, a dietitian assists with dietary and nutritional needs, a pharmacist provides information regarding medications, and a behavioral medicine specialist can help address stress, depression, and other mental health issues.

Team Cohesion Measure

In 2005, before the staggered implementation of an EHR, we mailed a letter introducing the study, a survey, and a pre-paid return envelope to all primary care team members working in the IDS. Respondents who completed the survey received a $5 gift card. Nonrespondents were re-sent up to 3 follow-up surveys.

The team cohesion measure was designed to describe the quality of working relationships and communication between primary care team members and, was developed using published validated instruments.9-12 Although other instruments capture similar aspects of the team environment—such as team participation, team dynamics, and relational coordination—we chose to use this one specifically because it was the only validated instrument available at the time of the study that was created specifically for use among primary care teams.9 We asked primary care team members whether they agreed or disagreed with the following 4 items: “when there is conflict on this team, the people involved usually talk it out and resolve the problem successfully”; “team members have constructive work relationships”; “there is often tension among people on this team” (reverse scored); and “the team members operate as a real team.”

Response options included a 5-point Likert-like agreement scale (1-5) that were averaged over the 4 team cohesion items for each respondent, and then averaged across members from the same team. The overall measure demonstrated high internal consistency with a Cronbach alpha coefficient of reliability of 0.83. For ease of interpretability, we categorized team cohesion scores into quartiles and created a binary indicator variable classifying each team as having lower or higher cohesion, with the lowest quartile of scores representing lower cohesion teams. We chose to categorize lower cohesion teams as those in the lowest quartile of cohesion because the median cohesion score in the study setting was higher than those previously reported; our 25th percentile scores were similar to the median score previously published.9

Patient Population

The study population included all patients who were in the health plan’s diabetes clinical registry at the start of 2004. The registry used the following 4 health plan data sources to identify patients: pharmacy data, lab values, and inpatient and outpatient diagnosis. Patients with 1 inpatient principal diabetes diagnosis; 2 outpatient diabetes diagnoses within 5 years; 2 or more abnormal lab results within 2 years (A1C >6.5%, fasting glucose >162 mg/dL, random glucose >200 mg/dL); or 1 diabetes medication prescription were entered into the registry. We used delivery-system administrative data to link patients with their primary care provider and team based on patients’ assigned primary care provider at the beginning of the study (2005). Members left the study cohort when they first disenrolled from the IDS, died, or changed their primary care team.

Outcome Measures: A1C or LDL-C Value

Using the health plan’s automated lab data, we collected all A1C and LDL-C values for the patients in our study cohort during the study period between January 1, 2005, and December 31, 2009. We chose these specific measures of disease control (A1C and LDL-C) since they are reliably captured, have been previously shown to improve with EHR implementation, and are associated with risk for adverse clinical events.29,30

Health Information Technology

Between 2005 and 2008, KPNC implemented an outpatient EHR in a staggered schedule across medical centers and teams. Although the implementation schedule was not randomly ordered, we confirmed that it was not associated with baseline level of diabetes care quality and did not coincide with other organizational changes.30 This implementation schedule created a quasi-experimental setting to examine the effects of team cohesion and EHR use with concurrent controls to adjust for secular trends in diabetes care practices unrelated to the EHR.30 The outpatient EHR replaced the paper-based medical record and a patchwork of non-integrated health information technology tools that were previously available.

The EHR is a commercially available EpicCare-based system that has been certified as a complete EHR, thereby qualifying its users for federal incentive payments. The system provides clinicians with complete outpatient information at the point of care, as well as lab and drug order entry and management, clinical decision support, and secure messaging with patients and across providers. Once implemented, the system was used by both clinical and support staff.

To determine the EHR status for each patient lab value, we linked patients in the study population to the medical facility where they sought care and defined each patient’s lab value according to whether the EHR was in use at their facility at the time of the test. We defined a facility as using the EHR once it was used for at least 80% of outpatient visits in a given calendar month. For each patient, we separately classified the first lab value after EHR implementation as having been done during the transition to the EHR, since it likely captured effects of treatment decisions based on the previous test value obtained pre-EHR.30 We defined each patient’s second and subsequent values after EHR implementation as being post EHR follow-up values. This allowed for the patient to be fully exposed to the EHR and its potential effect on treatment and follow-up care.

Data Analysis

We examined follow-up A1C and LDL-C values using linear regression models with fixed effects at the patient level,31 adjusting for calendar quarter and year, with an interaction term between outpatient EHR use and low team cohesion. To calculate the estimated EHR association for patients cared for by clinicians working in teams with lower cohesion, we added the coefficient for the EHR association and the interaction term for EHR and the lower team-cohesion indicator. The interaction term represents the difference in the EHR association with clinical outcomes for patients cared for by teams with higher versus lower cohesion scores. In addition, we also used logistic regression models with fixed effects at the patient level, adjusting for the same covariates to examine the interaction effect of team cohesion and EHR use on follow-up binary measures of good clinical A1C and LDL-C control (eg, A1C ≤7% and LDL-C ≤100 mg/dL).

As a sensitivity analysis, we also ran all models using random effects at the patient level, while controlling for patient characteristics (ie, gender, age, race, and chronic diseases) and obtained comparable results to the fixed effects models. We also conducted sensitivity analyses using a continuous cohesion score, other cohesion threshold levels, and separate cohesion quartile indicators: all models yielded comparable findings. We chose to present findings using the binary lower cohesion indicator for ease of interpretation of interaction term results. All analyses were implemented using Stata 10 (StataCorp LP, College Station, Texas).

The Kaiser Foundation Research Institute Institutional Review Board reviewed and approved the study protocol.


Table 1 provides a description of the primary care teams (N = 104, 95% of teams) included in the study; we excluded 6 teams that had fewer than 3 respondents. The mean team cohesion score was 3.71 (a score of 5 represents the highest possible level of cohesion), with a range from 2.84 to 4.42 (SD = 0.29).

Table 2

describes the individual characteristics of respondent and nonrespondent primary care team members (N = 780, 49% individual response rate). Respondents and nonrespondents were comparable in age and race (P >.05), but team members who were male and/or physicians were less likely to have completed our survey (P <.05).

Table 3

Our study included 80,611 patients in the health plan’s clinical diabetes registry at the end of 2003. shows patient characteristics at baseline. During the study period (2005-2009), these patients had a total of 598,924 A1C and 549,619 LDL-C tests; 60.1% of A1C and 58.4% of LDL-C tests were done after the implementation of the certified, outpatient EHR. On average, patients had 4 A1C and 4 LDL-C tests prior to the EHR, and 3 of each test after the EHR implementation. At baseline (last test in 2003), patients treated by teams with higher and lower team cohesion had similar mean A1C values (7.2% and 7.1% respectively; P = .11), but modestly different LDL-C values (96.8 mg/dL for lower cohesion vs 97.5 mg/dL for higher cohesion teams; P = .01).

Table 4


shows the adjusted association between EHR use and patient A1C and LDL values for primary care teams with lower and higher team cohesion. For patients cared for by clinicians working in primary care teams with higher cohesion, use of an EHR was associated with significantly greater improvements in A1C levels (0.11 percentage point decrease in A1C) compared with patients whose provider worked in a lower cohesion team (0.08 percentage point decrease); the difference in the EHR-associated reduction in A1C for patients cared for by teams with higher versus lower team cohesion was 0.02 percentage points (95% CI, 0.00%-0.03%; P <.01). Similarly, for patients whose provider worked in a team with higher cohesion, use of the EHR was associated with a greater decrease in LDL-C (2.15 mg/dL decrease) than for patients whose provider worked in a lower cohesion team (1.42 mg/dL decrease); the difference in the EHR-associated reduction in LDL-C for patients cared for by teams with higher versus lower team cohesion was 0.73 mg/dL (95% CI, 0.41-1.11 mg/dL; P <.001). Similarly, in the logistic models, we found that the association of EHR use and good physiologic control among patients with diabetes (A1C ≤7% and LDL-C ≤100 mg/dL) was significantly higher for patients treated by primary care teams with high cohesion compared with those treated by teams with low cohesion (P <.01) (, available at in PDF).


We found that the association between primary care teams’ use of an outpatient EHR and improvements in glycemic and lipid control in their patients with diabetes varied significantly by provider-reported team cohesion. For patients cared for by more cohesive primary care teams, using an EHR was associated with statistically significantly greater reductions in their glycemic and lipid levels than for patients cared for by less cohesive teams. These findings highlight the importance of attributes of the organizational environment, such as team cohesion, in the successful adoption of new technologies and practices.

Since the Institute of Medicine called for the redesign of our healthcare system centered around the use of multidisciplinary teams more than a decade ago, their use continues to grow.32 New care models, such as patient-centered medical homes and accountable care organizations, emphasize the importance of multidisciplinary teams to ensure high-quality, coordinated care.6-8 Expansions in insurance coverage through the Affordable Care Act will increase demand for primary care services, which, combined with our nation’s shortage of primary care physicians, may result in a greater reliance on the use of teams. Our findings are particularly timely given the ongoing federal incentive payments for meaningful use of EHRs and concurrent efforts to promote team-based primary care with patient-centered medical homes.

One way in which team cohesion may enhance the EHR-associated improvements in care could be through promoting informal learning. Members in more cohesive primary care teams may be more open to sharing best practices and minimizing unintended consequences than members of less cohesive teams.33 For example, use of an EHR may increase the amount of information included in the patient’s record, creating new processing challenges. It is possible that the members working in less cohesive teams had fewer agreed-upon approaches to document and retrieve critical patient health information.13

Evidence suggests that team member relationships are important for managing the care of chronically ill patients and for successfully adopting new practices and technologies.21,23,26,34 Our previous study found that cohesion among primary care teams significantly changed the association between EHR use and a number of clinician-reported coordination outcomes.13 Nonetheless, while team cohesion enhanced the EHR adoption process and its short-term effect on clinical outcomes, it is possible that, in the longer term, all clinicians will achieve comparable improvements in care. Future studies should examine whether differences in the EHR-associated changes in care by team cohesion persist over time.

Although the magnitude of the changes in LDL-C and A1C in this study are modest, our analyses were designed to measure the incremental within-patient changes associated with outpatient EHR use and team cohesion, excluding any secular trends. Whereas a small reduction in LDL and A1C control may have little effect on an individual, small changes in a large population, accompanied by parallel unmeasured improvements across other care pathways, could have clinically relevant favorable effects on downstream events. In addition, the results from the logistic regression models showed that patients cared for by more cohesive primary care teams were more likely to achieve guideline-recommended glycemic and lipid targets with EHR use, compared with patients cared for by less cohesive teams. In a previous paper, we showed that outpatient EHR use was associated with decreases in the rates of emergency department visits and hospitalizations for patients with diabetes.29


There are limitations to the generalizability of our findings. This study was conducted in a single delivery system and EHR system; in other settings, the team structure may differ, with some physicians practicing without teams. Still, use of multidisciplinary teams in primary care continues to grow across the country. Prior to the EHR, the IDS did use a limited set of health information technology applications and used disease management programs to target patient intermediate outcomes, so there may have been somewhat limited room for improvement in the study outcomes. In contrast, the benefit of the EHR on new quality targets, or in settings that do not have access to these tools at baseline, could be potentially greater in magnitude. In addition, our measure of team cohesion was based on survey responses, which are subject to response bias. We compared characteristics of respondents and nonrespondents and found that they were comparable in age and race, but not in gender or job title (see Table 2). We were not able to include patients who changed or left their primary care team during the study period. It is possible that team members or cohesion levels changed during this time.

Patients are the central figures in their own care and are the focus of their care team; yet, in this study, we collected surveys only from healthcare providers and not from patients. Future studies should examine patient perception of their healthcare team’s cohesion. While we used a quasi-experimental study design with concurrent controls, this was still an observational study, and therefore, we cannot rule out unmeasured confounding. It is possible that an unobserved factor, such as leadership or resources, drove both team cohesion and outcomes, or that the differences in EHR-associated improvements in these outcomes will converge over time.


The implementation of an EHR system is inherently complex and not always successful.35 There is also great variation in how healthcare providers are organized, and surprisingly little is known about how the organization of clinicians might influence the effect of EHR use on clinical care.5 We found that patients tended to by primary care teams with higher team cohesion experienced greater EHR-related improvements in A1C and LDL-C levels than patients cared for by lower cohesion teams. The organizational context—in particular, team cohesion&mdash;may play an important role in fully realizing potential gains in care quality from EHR use. Understanding the conditions necessary to maximize any potential benefits of EHR use is a critical policy area in need of more evidence. Future studies should explore which factors promote greater team cohesion, such as the development of shared goals and knowledge,36 the use of team development coaches, and the roles played by organizational culture and leadership. Identifying opportunities to improve team relationships in the work environment may enhance the effect of EHRs on care quality.Author Affiliations: Department of Preventive Medicine, University of Tennessee Health Science Center (IG), Memphis; Division of Research, Kaiser Permanente Northern California (IG, MER), Oakland; Department of Epidemiology and Biostatistics, University of California at San Francisco (RB); School of Public Health, University of California at Berkeley (SMS, TGR); Care Management Institute, Kaiser Permanente (JB), Oakland, CA; Mongan Institute for Health Policy, Massachusetts General Hospital (JH), Boston; Department of Health Care Policy, Harvard Medical School (JH), Boston, MA; Endocrinology and Internal Medicine, Kaiser South San Francisco Medical Center (MJ), CA.

Source of Funding: This research was supported by grants from the Agency for Healthcare Research and Quality (1R36HS021082-01 and R01HS015280) and the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK085070). The content, findings, and conclusions of this paper are the sole responsibility of the authors; the sponsors were not involved in any way in the determination of the content of this paper.

Author Disclosures: Dr Bellows is employed by Kaiser Permanente, which was the organization studied. Dr Shortell is a member of the Kaiser Permanente advisory board. The remaining 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 (IG, TGR, MER, JB, JH); acquisition of data (IG, JH, MER, JB, JH); analysis and interpretation of data (IG, SMS, JH, MJ, MER, RB); drafting of the manuscript (IG, SMS, TGR, MJ, MER); critical revision of the manuscript for important intellectual content (IG, SMS, TGR, MER, MJ, JH); statistical analysis (IG, RB, JH); provision of patients or study materials (IG, JB); obtaining funding (IG, MER, RB, JH); administrative, technical, or logistic support (MER, RB); and supervision (JH).

Address correspondence to: Ilana Graetz, PhD, Department of Preventive Medicine, University of Tennessee Health Science Center, 66 N Pauline St, Ste 633, Memphis, TN 38163. E-mail: igraetz@uthsc.edu.REFERENCES

1. MGMA Government Affairs Department. The federal EHR incentive program: achieving ‘meaningful use.’ MGMA Connex. 2010;10(8):14-16.

2. MGMA Government Affairs Department. CMS defines ‘meaningful use’. proposed rule outlines requirements for EHR incentive payments. MGMA Connex. 2010;10(3):10-13.

3. Office of the National Coordinator for Health Information Technology (ONC), HHS. Health information technology: initial set of standards, implementation specifications, and certification criteria for electronic health record technology. final rule. Fed Regist. 2010;75(144):44589-44654.

4. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010;363(6):501-504.

5. Nembhard IM, Singer SJ, Shortell SM, Rittenhouse D, Casalino LP. The cultural complexity of medical groups. Health Care Manage Rev. 2012;37(3):200-213.

6. Barr MS. The need to test the patient-centered medical home. JAMA. 2008;300(7):834-835.

7. Rittenhouse DR, Shortell SM. The patient-centered medical home: will it stand the test of health reform? JAMA. 2009;301(19):2038-2040.

8. Sidorov JE. The patient-centered medical home for chronic illness: is it ready for prime time? Health Aff (Millwood). 2008;27(5):1231-1234.

9. Ohman-Strickland PA, John Orzano A, Nutting PA, et al. Measuring organizational attributes of primary care practices: development of a new instrument. Health Serv Res. 2007;42(3, pt 1):1257-1273.

10. Shortell SM. High-performing healthcare organizations: guidelines for the pursuit of excellence. Hosp Health Serv Adm. 1985;30(4):7-35.

11. Shortell SM, Jones RH, Rademaker AW, et al. Assessing the impact of total quality management and organizational culture on multiple outcomes of care for coronary artery bypass graft surgery patients. Med Care. 2000;38(2):207-217.

12. Shortell SM, Rousseau DM, Gillies RR, Devers KJ, Simons TL. Organizational assessment in intensive care units (ICUs): construct development, reliability, and validity of the ICU nurse-physician questionnaire. Med Care. 1991;29(8):709-726.

13. Graetz I, Reed M, Shortell SM, Rundall TG, Bellows J, Hsu J. The association between EHRs and care coordination varies by team cohesion. Health Serv Res. 2014;49(1, pt 2):438-452.

14. Cebul RD, Love TE, Jain AK, Hebert CJ. Electronic health records and quality of diabetes care. N Engl J Med. 2011;365(9):825-833.

15. Crosson JC, Ohman-Strickland PA, Cohen DJ, Clark EC, Crabtree BF. Typical electronic health record use in primary care practices and the quality of diabetes care. Ann Fam Med. 2012;10(3):221-227.

16. Herrin J, Nicewander DA, Hollander PA, et al. Effectiveness of diabetes resource nurse case management and physician profiling in a fee-for-service setting: a cluster randomized trial. Proc (Bayl Univ Med Cent). 2006;19(2):95-102.

17. Mangione CM, Gerzoff RB, Williamson DF, et al; TRIAD Study Group. The association between quality of care and the intensity of diabetes disease management programs. Ann Intern Med. 2006;145(2):107-116.

18. O’Connor PJ, Crain AL, Rush WA, Sperl-Hillen JM, Gutenkauf JJ, Duncan JE. Impact of an electronic medical record on diabetes quality of care. Ann Fam Med. 2005;3(4):300-306.

19. Shojania KG, Ranji SR, McDonald KM, et al. Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. JAMA. 2006;296(4):427-440.

20. Zhou L, Soran CS, Jenter CA, et al. The relationship between electronic health record use and quality of care over time. J Am Med Inform Assoc. 2009;16(4):457-464.

21. Bower P, Campbell S, Bojke C, Sibbald B. Team structure, team climate and the quality of care in primary care: an observational study. Qual Saf Health Care. 2003;12(4):273-279.

22. Hung DY, Rundall TG, Cohen DJ, Tallia AF, Crabtree BF. Productivity and turnover in PCPs: the role of staff participation in decision-making. Med Care. 2006;44(10):946-951.

23. Hung DY, Rundall TG, Crabtree BF, Tallia AF, Cohen DJ, Halpin HA. Influence of primary care practice and provider attributes on preventive service delivery. Am J Prev Med. 2006;30(5):413-422.

24. Rodriguez HP, Rogers WH, Marshall RE, Safran DG. Multidisciplinary primary care teams: effects on the quality of clinicianpatient interactions and organizational features of care. Med Care. 2007;45(1):19-27.

25. Shortell SM, Marsteller JA, Lin M, et al. The role of perceived team effectiveness in improving chronic illness care. Med Care. 2004;42(11):1040-1048.

26. Wagner EH. The role of patient care teams in chronic disease management. BMJ. 2000;320(7234):569-572.

27. Weick KE, Roberts KH. Collective mind in organizations: Heedful interrelating on flight decks. Admin Sci Q. 1993;38(3):357-381.

28. Weick KE, Sutcliffe KM. Managing the Unexpected: Resilient Performance in an Age of Uncertainty. 2nd ed. John Wiley & Sons; 2007.

29. Reed M, Huang J, Brand R, et al. Implementation of an outpatient electronic health record and emergency department visits, hospitalizations, and office visits among patients with diabetes. JAMA. 2013;310(10):1060-1065.

30. Reed M, Huang J, Graetz I, et al. Outpatient electronic health records and the clinical care and outcomes of patients with diabetes mellitus. Ann Intern Med. 2012;157(7):482-489.

31. Wooldridge JM. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press; 2010.

32. Institute of Medicine (US) Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academies Press (US); 2001.

33. Burns LR, Bradley EH, Weiner BJ. Shortell and Kaluzny’s Healthcare Management: Organization Design and Behavior. 6th ed. Clifton Park, NY: Cengage Learning; 2011.

34. Strasser DC, Falconer JA, Herrin JS, Bowen SE, Stevens AB, Uomoto J. Team functioning and patient outcomes in stroke rehabilitation. Arch Phys Med Rehabil. 2005;86(3):403-409.

35. Poon EG, Jha AK, Christino M, et al. Assessing the level of healthcare information technology adoption in the United States: a snapshot. BMC Med Inform Decis Mak. 2006;6:1.

36. Hoffer Gittell J. Coordinating mechanisms in care provider groups: relational coordination as a mediator and input uncertainty as a moderator of performance effects. Management Science. 2002;48(11):1408-1426.

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