Search all of the Society for Participatory Medicine website:Search
The Journal for Participatory Medicine's website has moved. Please check out the new website for the latest articles.


Introduction: We examine the extent to which patients and providers discuss health goals, patient self-management, and factors associated with increased health risk, particularly among patients with chronic disease, using a large, statewide survey of patients.

Methods: We analyzed data from the Connecticut Health Care Survey (CTHCS), a statewide telephone survey involving 4,608 adult residents conducted in 2012-2013. The dependent variables consisted of five patient-reported measures of patient-provider communication related to health goals, patient self-management, and factors associated with increased health risk.

Results: Limited discussion of health goals, patient self-management, and factors associated with increased health risk, such as stress and depression, was observed. The condition that had the greatest impact on health care providers’ communication with and engagement of patients was hypertension. Hypertension was significantly associated with discussion of patients’ difficulties in taking care of their health, things that worried or stressed them, and things they could do to change their habits or lifestyle. Patients’ chronic disease status and obesity did not substantially increase patient-provider discussion of health goals and patient self-management, particularly related to patients’ stress and depression.

Conclusion: This is the first study assessing patient-provider communication and engagement across all payers and health care settings using data from a representative statewide survey. Findings highlight gaps in patient-provider discussions regarding modifiable behaviors that contribute to poor health and identify opportunities for health care providers to be more proactive in engaging patients in pursuing their health goals.

Keywords: Patient engagement, chronic disease, obesity, hypertension, diabetes.
Citation: Aseltine RH Jr, Sabina A, Barclay G, Rappoport D, Graham G. Engaging patients in managing chronic disease: an analysis of data from the connecticut health care survey. J Participat Med. 2016 Jun 6; 8:e7.
Published: June 6, 2016.
Funding Sources: Support for developing this manuscript was provided by the Aetna Foundation. Funding for the Connecticut Health Care Survey was provided by the Aetna Foundation, Children’s Fund of Connecticut, Connecticut Health Foundation, Foundation for Community Health, The Patrick and Catherine Weldon Donaghue Medical Research Foundation, and Universal Health Care Foundation of Connecticut. The survey was developed and conducted by the University of Massachusetts Medical School Center for Health Policy and Research. The views expressed in this paper are those of the authors and do not necessarily reflect those of the funding partners.
Competing Interests: The authors have declared that no competing interests exist.


Chronic diseases are the leading causes of death and disability in the United States. Cardiovascular disease, cancer, and stroke constitute the three most common causes of death in the US, collectively accounting for almost two-thirds of all deaths among adults. National data indicate that half of all Americans have chronic conditions, and almost half of those have multiple chronic conditions. [3] Concerns over the burden of chronic disease may be tempered somewhat by the fact that these conditions have a significant component that is behaviorally driven, or at minimum have behavioral correlates, and thus are potentially modifiable. The Centers for Disease Control and Prevention (CDC) identify four modifiable health risk behaviors-lack of physical activity, poor nutrition, tobacco use, and excessive alcohol consumption-that are responsible for much of the undesirable health outcomes related to chronic diseases. [4][5][6][7][8]

Recognition of the growing threat of chronic disease to population health has prompted changes in health care delivery to promote and support better patient engagement and self-management. Use of the Chronic Care Model (CCM) in ambulatory settings, a practice transformation facilitating the delivery of patient-centered, evidence-based care, has been associated with improved health outcomes and lower health care costs among patients with chronic disease. [9][10] Similarly, the Health Resources and Services Administration’s (HRSA) Patient-Centered Medical/Health Home Initiative (PCMHH) encourages health centers to have a goal of transforming primary care to provide care that is comprehensive, coordinated, and patient-centered using a team-based approach. Among the practice standards expected for National Committee for Quality Assurance (NCQA) recognition are the use of comprehensive health assessments that include behaviors affecting health and other correlates of chronic disease, such as depression, and activities to support patients in self-management, including goal-setting and counseling patients to adopt healthy behaviors. [11] Hallmarks of both the CCM and PCMH initiatives are shared decision-making and patient engagement in their health and healthcare. [12][10]

Patient engagement and patient activation are increasingly recognized as important factors in improving health outcomes, improving health care experiences, and reducing costs. [13][14][15] Although the term can be used loosely and at times inconsistently in the literature, engagement among patients with chronic disease refers to an active partnership with healthcare providers that fosters behaviors such as: greater understanding of the condition(s); collaborative evaluation of treatment options; promotion of patients’ efforts to change behavior to improve health outcomes; and self-monitoring of symptoms and condition(s). [16][17][18][19] There is substantial evidence of the critical role that physicians can play in influencing levels of patient engagement among those with chronic diseases. Findings of improved outcomes among more engaged patients and among patients of physicians who employ a more participatory model of patient decision-making have been reported across a range of chronic diseases. [20][21][22]

The purpose of this study is to examine the extent to which patients report that providers discuss health goals, patient self-management, and factors associated with increased health risk and improved health outcomes using data from the Connecticut Health Care Survey (CTHCS). Recent surveillance data indicate that problematic health behaviors put many Connecticut residents at risk of chronic disease: only 54% of residents get the recommended levels of physical activity, only 28% have adequate intake of fruits and vegetables, and as a consequence, 23% of residents are obese. [23] Chronic disease prevalence largely mirrors national rates, with Connecticut reporting slightly higher age adjusted rates of cancer and slightly lower rates of cardiovascular disease and stroke. [2] The CTHCS provides insight into the ways in which physicians are interacting with their patients in managing their health, particularly for patients with chronic illnesses.


We analyzed data from the CTHCS, a statewide telephone survey conducted between June 2012 and February 2013. The overarching goal of this project was to gather information from Connecticut residents relating to their experiences and perspective on their health and the health care system. A random-digit-dial (RDD) telephone interview strategy was employed using a dual frame, probability-based random sample of Connecticut residents. [24] The survey collected information by telephone using both landlines and cell phones from a sample of households across the state. Both cell phone and landline numbers were stratified by county; landline numbers were additionally stratified by whether the telephone number was in a listed vs unlisted block, and whether the city or town of the phone number was classified as urban, manufacturing or other health reference group. [25] Adult residents of all ages were included in the survey. In all, 4,608 adult surveys were completed resulting in a cooperation rate of 66.5% and a response rate of 29.3%. The analysis presented below is restricted to the 3,765 participants who had seen a clinician in the past 12 months.

The Center for Health Policy and Research at the University of Massachusetts Medical School designed the survey and collected the data. The study was approved by the Institutional Review Board of the University of Massachusetts Medical School.


The primary outcome measures in this analysis were five patient-reported measures of patient-provider communication related to health goals, patient self-management, and factors associated with increased health risk derived from the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) Patient Centered Medical Home Survey (PCMH). The CAHPS® surveys were developed to elicit reports from consumers about their health care experiences, with the PCMH version of this survey featuring additional questions relevant to providers’ ability to provide person-centered care, comprehensive and coordinated care, and other characteristics of the PCMH. [26][27] Patients were asked to report whether (yes or no) in the past 12 months anyone at their usual source of medical care had talked with them about: specific goals for your health; if there are things that make it hard for you to take care of your health; if there are things in your life that worry you or cause you stress; whether there was a period of time when you felt sad, empty or depressed; and specific things you could do to change your habits or lifestyle. The primary independent variables consisted of whether patients had been told by a healthcare provider that they had one of the 5 following chronic conditions: diabetes or sugar diabetes; high blood pressure or hypertension; asthma; heart disease, heart failure or heart attack; cancer; or whether they were obese as measured by self-reported body mass index (BMI > 30). [28][29] We included control variables for patients’ race and ethnicity (Hispanic, Black, Other Race vs White); patients’ gender; patients’ age group (18-24, 25-34, 35-44, 45-54, 65-74, 75+). Missing values on patient’s age and level of education were assigned to the median values for these variables. Controls for whether patients’ primary source of care was a doctor’s office vs a clinic or a hospital; whether they tended to see the same provider each time vs a different provider; and whether their provider was a physician or other health professional were also included in initial analyses.

Statistical Analysis

Due to the binary outcome variables and the complex sampling design, weighted logistic regression models were estimated. Data were weighted using a two-step process: in the first step design weights were calculated to account for the complex survey design, with these weights subsequently adjusted to balance the sample according to the Connecticut population distributions based on the 2010 U.S. Census and 2011 American Community Survey and adjusted for survey nonresponse. Data were analyzed using the Complex Samples module for SPSS 22.0.


The demographic characteristics of participants are presented in Table 1. Consistent with Census data for the Connecticut, CTHCS participants were largely White/Non-Hispanic (79%), married (55%), and having a high school degree or higher (89%). The modal age of survey participants was 55 to 64 (22%), and 60% of respondents were women.

Table 1. Demographic characteristics of participants in the Connecticut Health Care Survey (N = 3,765).
Aseltine et al Table 1
Note: Cell counts and percentages are unweighted.

The prevalence of chronic illness and obesity reported by CTHCS participants is presented in Table 2. Hypertension is the most prevalent chronic condition in the state, affecting over 26% of patients. Rates of diabetes, heart disease, cancer, and asthma ranged between 7 – 13% of the Connecticut population. Fifteen percent of residents had multiple chronic conditions. These self-reported prevalence rates are consistent with Connecticut data from the CDC’s Behavioral Risk Factor Surveillance System. [23]

Table 2. Self-reported rates of chronic conditions and provider-patient communication and engagement in the CTCHS (N = 3,765).
Aseltine et al Table 2

Patient-Provider Discussion of Lifestyle and Health Issues

Participants in the CTCHS reported modest to low levels of provider discussion of health goals, patient self-management, and factors associated with increased health risk, such as stress and depression (Table 2). One half to two thirds of patients reported that someone in their provider’s office had talked with them about specific goals for their health (57%) or things they could do to change their habits or lifestyle (66%) during the past year. Fewer patients reported having discussed things in their lives that worry them or cause them stress (48%) or feelings of sadness or depression (39%). Finally, less than one third of respondents reported having been asked if there were things that make it hard for them to take care of their health (29%).

Patient-Provider Discussion of Lifestyle and Health Issues among Patients with Chronic Conditions

While all patients can benefit from guidance related to lifestyle, health goals, and strategies for managing their health, patients with chronic illnesses are in particular need of such guidance. Table 3 presents odds ratios and confidence intervals from logistic regression analysis in which different aspects of provider engagement were regressed on dummy variables capturing patients’ obesity, chronic disease history, and controls for gender, age, and education level. Preliminary analyses additionally controlled for patients’ race and ethnicity, patients’ primary site of care, patients’ primary care provider type, and consistency in provider. None of these additional control variables were statistically significant at the .05 level for any of the measures of provider communication and engagement and were trimmed from the final model to simplify the interpretation of results.

Data from the CTHCS indicate that patients with chronic conditions were somewhat more likely to have discussed these issues with their providers in the past year. The condition that had the greatest impact on providers’ communication with and engagement of patients in their health was a diagnosis of hypertension. Those with hypertension were significantly more likely to report that a provider had asked whether aspects of life were difficult to manage, about things that worried or stressed them, and about things they could do to change their habits or lifestyle in the past 12 months. Obese patients reported a significantly greater likelihood of discussing health goals and things they could do to change their habits or lifestyle in the past 12 months. However, obese patients were no more likely than non-obese patients to have talked about things that worry or stress them, whether they had feelings of depression and sadness, or whether it was difficult for them take care of their health. Patients with diabetes were significantly more likely than those without diabetes to report that they had discussed health goals with their providers in the past 12 months. Conversely, patients with heart disease were significantly less likely than those without heart disease to report that their providers had discussed things that stressed or worried them. Patients with asthma or cancer did not report any increased likelihood of provider communication and engagement in these areas than those without these conditions.

Table 3. Results from weighted logistic regression equations predicting patients and providers discussion of health goals, patient self-management, and factors associated with increased health risk, using data from the CTHCS.
Aseltine et al Table 3
Note: Asterisk (*) indicates coefficients significant at .05 level.

To facilitate interpretation of these effects, we present in Table 4 the percent of patients reporting different aspects of provider-patient communication and engagement by chronic disease status. Differences based on chronic disease status were modest in magnitude: for example, 70% of diabetic patients had discussed health goals with their providers, compared to 57% of non-diabetic patients. Hypertension, which was significantly associated with three of the measures of provider engagement and communication, also had a small to modest impact, with differences between those with and without hypertension in provider-patient communication and engagement ranging between 4-11 percentage points. The greatest percentage difference between patients with and without hypertension involved talking about specific things they could do to change their habits or lifestyle: 73% of those with hypertension reported discussing this, compared to 61% without hypertension. Surprisingly, 50% of those without heart disease reported discussing things that caused them stress and worry in the past year, in contrast to only 37% of those with heart disease.

Table 4. Rates (in percentages) of patient-provider discussion of health goals, patient self-management, and factors associated with increased health risk among CTHCS participants with and without chronic conditions.
Aseltine et al Table 4
Note: Cells present percent of survey participants saying they had talked about each item in the past 12 months. Cells in bold italic type reflect statistically significant differences between patients with and without a specific condition.

Discussion and Conclusion


Findings from this study highlight gaps in provider-patient discussions related to modifiable behaviors that contribute to poor health, or conversely, can promote improved health. Of particular concern were the significantly lower rates of provider engagement around stress reduction among patients with heart disease given the strong evidence of stress as a risk factor for this disease. [30][31][32] The absence of higher rates of engagement related to stress and depression among obese patients was similarly troubling given the well-documented association between stress, depression, and obesity. [33] Data from the CTHCS indicate that a third of obese patients had not discussed setting health goals with their healthcare provider in the past year and a quarter had not discussed altering their health habits and lifestyle, highlighting a missed opportunity to intervene with a substantial number of high risk patients.

It is important to acknowledge the limitations of our study. First, the data for this analysis consist entirely of patients’ self-reports and may be of limited accuracy and reliability. However, recent studies suggest reasonable correspondence between self- reported measures of chronic disease and administrative health records. [34][35] In addition, findings from Project CHAT (Communicating Health: Analyzing Talk) have demonstrated a high level of congruence between patient and provider assessments of the content of their communications and interactions related to obesity and chronic disease management. [36] Also, the 30% response rate achieved for the CTHCS may limit the generalizability of results. It is important to note, however, that this is typical of RDD surveys conducted over the past decade.
[37] A recent study of 114 national, statewide, or regional RDD surveys found little impact of this level of nonresponse on the demographic representativeness of the resulting survey samples. [38]

Despite these limitations, the data presented in this manuscript highlight opportunities to foster improved health, particularly among those with chronic disease, by heightening providers’ whole-person orientation and support for patient self-management. The findings from the CTHCS were consistent with recent ratings of Connecticut’s performance on a number of criteria related to patient-centered care by the Agency for Healthcare Research and Quality (AHRQ). AHRQ’s state level ratings on factors such as the quality of patient-provider communication, counseling related to healthy behaviors, or hospitalizations for chronic conditions that could be managed in ambulatory setting, show the state to be performing below or far below national benchmarks. [39] However, Connecticut, like many states, is currently pursing major initiatives that would foster a more patient-centered care experience. Connecticut’s State Innovation Model (SIM) Plan, a roadmap for transforming the healthcare delivery system into a more effective, efficient, and patient-centered enterprise, promotes the adoption of the medical home as a key objective. [40] The patient-centered medical home, which urges healthcare providers to systematically assess risk factors and behaviors that affect patient health, equip patients to manage chronic conditions, and counsel patients to adopt healthy behaviors, is the nation’s fastest growing practice transformation innovation. Over 35,000 clinicians in almost 7000 sites across the country have achieved NCQA recognition. [41] The six current SIM awardees have included medical home initiatives and improvements related to patient-centered care as elements of their healthcare transformation strategy. [42]

Caring for patients suffering from or at risk for chronic disease can be a daunting task for clinicians, particularly for those treating medically and socially complex populations (e.g., the underserved, seniors, children). Advances in health information technology hold great potential for assisting providers in identifying risk factors for chronic disease, improving its management, and personalizing patient care. Rates of electronic health record adoption in the US have increased dramatically over the past decade, yet implementation among primary care physicians nationally remains at just over half (53%). [43] A 2013 survey of primary care physicians in CT found a similar proportion (57%) using electronic medical records, [44] many of which feature modules facilitating patient risk assessment, medication monitoring, support for standard care plans, guidelines, and protocols. [45] Newer technologies, particularly those accessible to patients on mobile devices such as tablets and smartphones, offer much promise for facilitating clinical assessment and enabling patient self-management. The mobile health marketplace is burgeoning: there are more than 97,000 mobile health apps currently available for download on 62 app stores. [46] While most of these apps target patients and consumers, it is estimated that 15% are exclusively for use by medical providers. When deployed in clinical settings such technologies can reduce the burden on clinicians by automating mechanisms to screen and refer patients for needed services, such as nutrition counseling or behavioral health. Findings from a recent study employing mobile devices to assist in identifying at risk patients indicates that such technologies can dramatically improve the detection of problems such as depression and other mental health problems, alcohol misuse, food insecurity, and oral health problems. [47]


The CTHCS is, to our knowledge, the first representative statewide survey assessing patient-provider communication and engagement across all payers. The limited levels of patient engagement observed in the CTHCS highlight a relatively accessible means for improving the management of chronic disease and overall population health. Although more than half of Connecticut patients have talked with their medical providers about setting goals and altering their habits and lifestyle to improve their health, patient-provider discussions related to stress and feelings of depression were not as commonplace. This study has identified opportunities for healthcare providers to be more proactive in discussing the health goals of their patients as well as providing counseling to patients on self-management of their illness and the risks associated with their illness. Our results suggest that particular attention needs to be paid to awareness and counseling around depression and stress in patients with heart disease and obesity given the lower rates of addressing those comorbidities in those patients.


  1. Kung HC, Hoyert DL, Xu JQ, Murphy SL. Deaths: final data for 2005. National Vital Statistics Reports 2008;56:1-120.
  2. National Center for Chronic Disease Prevention and Health Promotion. Chronic Disease Indicators. Available at: Accessed October 22, 2014.
  3. Anderson G. Chronic Care: Making the Case for Ongoing Care. 2010. Robert Wood Johnson Foundation, Princeton, NJ. Available at: Accessed June 28, 2014.
  4. Centers for Disease Control and Prevention. Chronic Disease Prevention and Health Promotion. Available at: Accessed October 22, 2014.
  5. Telford RD. Low Physical Activity and Obesity: Causes of Chronic Disease or Simply Predictors? Med Sci Sports Exerc, 2007;39:1233-40.
  6. Harris JR, Wallace RB. The Institute of Medicine’s new report on living well with chronic illness. Prev Chronic Dis 2012;9:120-6.
  7. US Department of Health and Human Services. The Health Consequences of Smoking-50 Years of Progress: A Report of the Surgeon General. Atlanta, GA: US Dept of Health and Human Services, Centers for Disease Control and Prevention; 2014. Available at: Accessed June 28, 2014.
  8. Centers for Disease Control and Prevention (CDC). Alcohol use and health. Atlanta, GA: CDC. Available at: Accessed June 28, 2014.
  9. Coleman K, Austin BT, Brach C, Wagner EH. Evidence on the Chronic Care Model in the new millennium. Health Aff 2009;28:75-85.
  10. Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness: The Chronic Care Model, Part 2. J Amer Med Assoc 2002;288:1909-14.
  11. National Committee for Quality Assurance. NCQA PCMH 2011 Standards, elements and factors: Documentation guideline/data sources. July 8, 2011. Available at: Accessed June 28, 2014.
  12. National Committee for Quality Assurance. NCQA: The Future of Patient-Centered Medical Homes Foundation for a Better Health Care System. Available at: Accessed June 28, 2014.
  13. Hibbard JH, Greene J. What the evidence shows about patient activation: better health outcomes and care experiences; fewer data on costs. Health Aff 2013; 32:207-14.
  14. Hibbard JH, Greene J, Overton V. Patients with lower activation associated with higher costs; delivery systems should know their patients’ ‘scores’. Health Aff 2013; 32:216-22.
  15. Greene J, Hibbard JH. Why does patient activation matter? An examination of the relationships between patient activation and health-related outcomes. J Gen Intern Med 2012;27:520-6.
  16. Gruman J, Rovner MH, French ME, Jeffress D, Sofaer S, Shaller D, Prager DJ. From patient education to patient engagement: implications for the field of patient education. Patient Educ Couns 2010; 78:350-6.
  17. Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res 2004;39:1005-26.
  18. Carman KL, Dardess P, Maurer M, Sofaer S, Adams K, Bechtel C, Sweeney J. Patient and family engagement: a framework for understanding the elements and developing interventions and policies. Health Aff 2013; 32(2):223-31.
  19. Barello S, Graffigna G, Vegni E, Bosio AC. The challenges of conceptualizing patient engagement in healthcare: a lexicographic literature review. J Participatory Med 2014; 11;6:e9.
  20. Alexander JA, Hearld LR, Mittler JN, Harvey J. Patient–physician role relationships and patient activation among individuals with chronic illness. Health Serv Res 2012;47:1201-23.
  21. Becker ER, Roblin DW. Translating primary care practice climate into patient activation: the role of patient trust in physician. Med Care 2008;46(8):795-805.
  22. Parchman ML, Zeber JE, Palmer RF. Participatory decision making, patient activation, medication adherence, and intermediate clinical outcomes in type 2 diabetes: a STARNet study. Annals Fam Med 2010;8(5):410-7.
  23. State of Connecticut Department of Public Health. Health Risk Behaviors in Connecticut: Results of the 2012 Connecticut Behavioral Risk Factor Surveillance Survey. April, 2014. Available at: Accessed October 13, 2014.
  24. Office of Survey Research, Center for Health Policy and Research, University of Massachusetts Medical School. Connecticut Health Care Survey Field Report, November 2013. Unpublished report.
  25. Finison LJ. Community Health Data Scan for Connecticut: A Report Commissioned by the Connecticut Health Foundation. Available at: Accessed October 22, 2014.
  26. Scholle SH, Vuong O, Ding L, Fry S, GallagherP, Brown J, Hays RD, Cleary PD. Development of and field test results for the CAHPS PCMH Survey. Med Care 2012;50:S2-S10.
  27. Dyer N, Sorra JS, Smith SA, Cleary P, Hays R. Psychometric Properties of the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) Clinician and Group Adult Visit Survey. Med Care.2012;50(Suppl):S28-S3. DOI:10.1097/MLR.0b013e31826cbc0d.
  28. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Survey Questionnaire. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2012.
  29. Pew Research Center. The diagnosis difference. November 2013. Available at: . Accessed October 12, 2014.
  30. Ornish D, Brown SE, Billings JH, Scherwitz LW, Armstrong WT, Ports TA, McLanahan SM, Kirkeeide RL, Gould KL, Brand RJ. Can lifestyle changes reverse coronary heart disease? Lancet 1990;336:129-33.
  31. Bunker SJ, Colquhoun DM, Esler MD, Hickie IB, Hunt D, Jelinek VM, Oldenburg BF, Peach HG, Ruth D, Tennant CC, Tonkin AM. “Stress” and coronary heart disease: psychosocial risk factors. Med J Aust 2003;178:272-6.
  32. Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav; 35(Extra Issue):80-94.
  33. Björntorp P. Do stress reactions cause abdominal obesity and comorbidities? Obes Revs 2001;2:73-86.
  34. Martin LM, Leff M, Calonge N, et al. Validation of self-reported chronic conditions and health services in a managed care population. Am J Prev Med 2000;18:215-18.
  35. Muggah E, Graves E, Bennett C, Manuel DG. Ascertainment of chronic diseases using population health data: a comparison of health administrative data and patient self-report. BMC Public Health 2013;13:16. Doi:10.1186/1471-2458-13-16.
  36. Bodner ME, Dolor RJ, Óstbye T, Lyna P, Alexander SC, Tulsky JA, Pollak KI. Accuracy and congruence of patient and physician weight-related discussions: from Project CHAT (Communicating Health: Analyzing Talk). J Am Board Fam Med 2014;27: 70-77.
  37. Kohut A, Keeter S, Doherty C, Dimock M, Christian L. Assessing the representativeness of public opinion surveys. May 15, 2012. Pew Research Center, Washington, DC. Available at: Accessed October 12, 2014.
  38. Holbrook AL, Krosnick JA, Pfent A. The causes and consequences of response rates in surveys by the news media and government contractor survey research firms. In Lepkowski JM, Tucker C, Brick JM et al., eds. Advances in telephone survey methodology. New York: Wiley; 2008: 499-528.
  39. Agency for Healthcare Research and Quality. National healthcare quality and disparities reports. Available at: Accessed June 29, 2014.
  40. State of Connecticut. Connecticut Healthcare Innovation Plan. Available at: Accessed October 12, 2014.
  41. National Center for Quality Assurance. Sneak Preview: 2014 Patient-Centered Medical Home Recognition Presentation. Available at: Accessed June 24, 2014.
  42. Centers for Medicare & Medicaid Services. State Innovation Models Initiative: Model Testing Awards Round One. Available at: . Accessed October 18, 2014.
  43. Hsiao C-J, Hing E. Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001-2013. NCHS data brief, no 143. Hyattsville, MD: National Center for Health Statistics. 2014. Available at: Accessed October 18, 2014.
  44. Tikoo M, Costello D. Evaluating Connecticut’s Health Information Technology Exchange: Physician Survey Report. Farmington, CT: University of Connecticut Health Center; 2014.
  45. Health Level Seven International. HL7 EHR-System Functional Model, R2. Available at: Accessed October 18, 2014.
  46. Jahns, R. The market for mHealth app services will reach $26 billion by 2017. March 7, 2013. research2guidance. Available at: Accessed June 28, 2014.
  47. Staeheli MR. mHealth Risk Screening in Urban Primary Care. DigitalCommons@UConn. December 17, 2015. University of Connecticut — Storrs.
  48. Copyright: © 2016 Robert H. Aseltine, Jr., Alyse Sabina, Gillian Barclay, Daniel Rappoport, and Garth Graham. Published here under license by The Journal of Participatory Medicine. Copyright for this article is retained by the authors, with first publication rights granted to the Journal of Participatory Medicine. All journal content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License. By virtue of their appearance in this open-access journal, articles are free to use, with proper attribution, in educational and other non-commercial settings.