Attitudes and Perceptions of Behavioral Health Clinicians on Electronic Health Record Adoption: Overcoming Obstacles to Improve Acceptance and Utilization

by Stephen Odom, PhD, and Kristen Willeumier, PhD

Abstract

The dynamics and progress of the integration of the electronic health record (EHR) into healthcare disciplines have been described and examined using theories related to technology adoption. Previous studies have examined healthcare clinician resistance to the EHR in primary care, hospital, and urgent care medical settings, but few studies have been completed that pertain specifically to behavioral healthcare clinicians. The purpose of this study was to examine the relationships that may exist between behavioral healthcare clinicians’ perceptions of usefulness and ease of use of EHRs and demographic variables regarding adoption of EHRs. The Physician’s Survey Questionnaire (PSQ) was utilized as the survey instrument, which was administered to 95 licensed marriage and family therapists in California. Regression analyses were performed to test the relationship between the therapists’ personal characteristics, their perceived ease of use and usefulness of EHRs, and their attitudes toward adoption of EHRs. The findings of the study suggest that (1) older clinicians are less likely to perceive EHRs as useful in their professional practice and that (2) perceived ease of use and usefulness of EHRs are positively associated with attitudes toward adoption of EHRs. These findings indicate that to improve the adoption of EHRs among behavioral health clinicians, the EHR needs to be viewed as useful. Interpretation of the results and suggestions for future research are offered.

Keywords: electronic health record; health information technology; adoption; resistance; behavioral health

Introduction

Over the past 25 years, the use of electronic health records (EHRs) has been emphasized in the medical community, with federal regulations strongly recommending or mandating their use. Most recently, the Obama administration introduced the Health Information Technology for Economic and Clinical Health legislation, with the federal government committing $27 billion in resources and support for the adoption and meaningful use of EHRs.1, 2 While government agencies have made significant efforts to provide the support required to integrate EHRs as standard practice within the medical community, the rate of adoption of EHRs in the behavioral healthcare community has been lower. This has occurred because behavioral health providers are not eligible for the meaningful use incentives, making these providers less inclined to adopt this technology in their practice.

Numerous studies have demonstrated the benefits of EHR adoption, including decreased mortality rates among the chronically ill, reduced costs, and safer and more efficient processes, as well as improvements in clinical and organizational outcomes, data mining, outcome reporting, and treatment planning.3, 4 Access to electronic data has been demonstrated to empower physicians with more actionable knowledge, which serves to improve both patient care and satisfaction while controlling costs.5 Nonetheless, the adoption of EHRs has been slow and not without challenges. Numerous barriers to adoption have been noted in the literature, including time investment in making the switch, cost, technical support, keeping up with new technology, clinicians’ attitudes toward technology, disruption in workflow, concerns about patient security and privacy, communication among users, a disruption in communication between patients and physicians, reliability, speed, and issues with data integrity and appropriate data exchange.6–9

Despite the advances in and wide availability of health information technology, many behavioral healthcare clinicians have not adopted EHRs.10–12 In 2012, a survey of behavioral health organizations’ adoption of EHRs and readiness for meaningful use found that only 21 percent used EHRs.13 Beliefs about both the efficacy of EHRs and the extra layers of privacy rights for behavioral health records may be partly to blame for the slower adoption.14–16 Beliefs about the importance of the patient-to-therapist relationship may also make it difficult to accept EHR technology in the psychotherapy space.17, 18 Given this significant limitation in the use of EHRs, important questions remain regarding behavioral clinicians’ perceptions of EHR technology and how behavioral clinicians can be encouraged to use EHRs and to share data with other medical providers as the exchange of electronic health information becomes standardized across healthcare platforms.

Despite the growing body of literature regarding EHR adoption, few studies have investigated it specifically in the context of the behavioral healthcare clinician.19, 20 This gap is concerning because EHRs can provide a platform from which to standardize and measure the effects of interventions that are utilized to treat mental disorders.21 Therefore, the purpose of this study is to take a quantitative approach in investigating the barriers to EHR adoption in the behavioral health community with regard to perceptions of this technology among a cohort of licensed marriage and family therapists. The associations between perception of ease of use, perception of usefulness, and demographic variables on EHR adoption are measured. Furthermore, the correlations between variables that include perceptions of ease of use and usefulness of the EHR with regard to clinicians’ gender, age, ethnicity, length of professional practice, theoretical orientation, employment setting, and employment type are examined. The intent of this work is to advance the knowledge of how to more effectively integrate EHR technology within the behavioral healthcare setting and to present solutions to overcome the obstacles in order to improve patient care.

Methods

Population Sample

This study sample consisted of licensed marriage and family therapists in California selected through profiles found on a state-level professional organization website with a mailing address within a 50-mile radius of a large metropolitan area. Recruitment of participants was through email and standard mail. Participants were directed to a survey website to take the study, and all were also offered a paper survey.

A common formula was used to determine the minimum sample size needed for the study. The formula was based on the choice of the quasi-experimental, correlational design used: 50 + 8(k), where k equals the number of independent variables.22 Five independent variables were utilized in the study; therefore, the minimum sample size was determined to be 90. Of the 428 individuals recruited for participation, 105 answered surveys with 10 excluded for incompletion. The result was 95 completed surveys available for analysis, which exceeded the minimum number necessary to adequately power this study.

More than 95 percent of the remaining cases had no missing data regarding the main variables used in the analysis. In the remaining 5 percent of the cases, 1.1 percent to 4.2 percent of the values were missing. Table 1 presents an overview of the descriptive characteristics of the participants in the study.

Instrumentation/Measures

The Physician’s Survey Questionnaire (PSQ), developed by Aldosari23 and adapted by Morton,24 was utilized to measure perceived ease of use, perceived usefulness, and an attitude score. The PSQ included 10 sections:

  1. General background information
  2. Management support
  3. Physician involvement
  4. Adequate training
  5. Clinician autonomy
  6. Doctor-patient relationship
  7. Perceived ease of use
  8. Perceived usefulness
  9. Attitude about EHR usage
  10. Comments

The current instrument adapted each question from physicians to psychotherapists and eliminated the subscales not examined in the research questions in this study, which were those addressing management support, physician support, and adequate training. Two questions were added to the general background information addressing the clinician’s theoretical orientation and the clinician’s practice setting. All questions, except those in the general information and comments sections, utilized a five-point Likert scale with responses ranging from 1, indicating strongly disagree, to 5, indicating strongly agree. The survey also included questions about attitude about EHR usage, perceived ease of use, and perceived usefulness, as well as the perceived usefulness and ease of use of electronic treatment planning (as categorical variables, where 1 = easy to use, 2 = moderately easy to use, 3 = some effort to use, 4 = moderately difficult to use, and 5 = difficult to use).

Data Collection and Analysis

The research employed a quantitative, correlational design utilizing data collected from licensed marriage and family therapists’ responses to the adapted PSQ instrument. Institutional Review Board approval for this study was obtained through Capella University. All participants were fully informed and gave their written consent for participation in the study.

The statistical calculations of the descriptive and inferential statistics were completed with IBM SPSS v. 22.0 (Armonk, NY; IBM Corp.). Inferential tests were conducted to determine the relationships between the behavioral healthcare clinicians’ attitude scores and the perceived ease of using and usefulness of EHRs. Raw data consisted of results collected from the adapted PSQ via an online survey program (SurveyMonkey). The data were coded and grouped according to the specific categories of clinician characteristics described above.

After the data collection was complete, the data from the online survey were downloaded and entered into the SPSS database. Little’s chi-square statistic was calculated to determine which missing value imputation method was the most appropriate. The statistical analysis was conducted with the final sample of 95 respondents, with no missing data remaining in the dataset. Data were then examined for outliers. Descriptive statistics yielded means, percentages, standard deviation, and correlation coefficients among the key scale variables. Because all the variables were originally ordinal, Spearman’s correlation index—typically used to test the correlation between interval, ratio, or ordinal data—was utilized. Ordinary least squares (OLS) regression analysis was performed to test the relationship between the behavioral healthcare clinicians’ attitude scores and the perceived ease of use of EHRs and usefulness of EHRs. A set of regression models was performed separately for each dependent variable. After a baseline model (Model 0) was established, three subsequent regression models were run to address the three main research questions of this study. A statistical significance level of p < .01 was used to determine differences among the variables at the different data collection points.

Results

To measure the respondents’ attitudes toward their perceived autonomy when using EHR technology, they were asked for their opinions on a series of statements across five measures. All internal reliability scores across the five measures denoted a strong correlation between items. Table 2 provides reliability scores (Cronbach’s alpha) for all scales used in the analysis.

To continue with the analysis, the scale items were first summed and then averaged to create a mean score for each respondent. Table 3 provides the sample size, means, standard deviation, and correlation coefficients among the key scale variables. All correlation coefficients are statistically significant at the .01 level. Two predictor variables, “Perceived ease of using EHR” and “Perceived usefulness of EHR,” are also positively correlated (r = .47, p < .01), suggesting a potential issue of multicollinearity. An additional analysis of the assumption of multicollinearity was performed.

A set of OLS regression analyses were performed to test the relationship between behavioral healthcare clinician personal characteristics, their perceived ease of using and usefulness of EHRs, and their attitudes toward adoption of EHRs. To address how well the behavioral health clinicians’ characteristics (age, years in practice) predicted their perceived ease of use of EHRs, perceived usefulness of EHRs, and attitude toward adoption of EHRs, three separate regression models were performed.

Table 4 shows the results of Model 1a, which examined whether behavioral health clinician characteristics predicted the perceived ease of using EHRs. The results of this model show that age and years in practice explained 3.5 percent of the total variance, R2 = .035, F(2, 94) = 1.69, p > .05. The Durbin-Watson statistic was calculated to determine error terms, with the value between 0 and 4. A value closer to 2 is considered acceptable.25 The Durbin-Watson statistic was 2.17, which indicates that the assumption of independent errors was met. As shown in Table 4, there is no statistically significant relationship connecting the behavioral health clinicians’ age and perceived ease of using EHRs (β = −.21, p > .05). The association between experience in the healthcare field and perceived ease of using EHRs was also statistically insignificant (β = .05, p > .05).

Table 5 shows the results of Model 1b, which examined whether behavioral health clinician characteristics (age and years in practice) predicted perceived usefulness of EHRs. The results of Model 1b suggest that two predictors explained 7.4 percent of the total variance, R2 = .074, F(2, 94) = 3.68, p < 0.05. The Durbin-Watson statistic was 1.94, indicating that the assumption of independent errors was met.

Regression coefficients presented in Table 5 suggest a negative yet statistically significant relationship between a clinician’s age and perceived usefulness of EHRs (β = −.28, p < .05). This finding suggests that older clinicians are less likely to find EHRs useful in their healthcare practice. The perceived usefulness of EHRs is not statistically related to the respondent’s years of experience (β = .02, p > .05).

Table 6 shows the results of Model 1c, which examined whether personal characteristics of behavioral health clinicians predicted their attitude toward adoption of EHRs. The overall explained variance in this dependent variable is 2.5 percent of the total variance, R2 = .025, F(2, 94) = 1.18, p > 0.05. The Durbin-Watson statistic was 1.91, indicating that the assumption of independent errors was met. As in Model 1a, none of the clinician personal characteristics were statistically related to attitude toward adoption of EHRs (i.e., β = −.16, p > .05, for age, and β = −.001, p > .05, for years of practice).

Overall, the results of Models 1a to 1c demonstrate that behavioral health clinician characteristics are not significantly related to the perceived ease of using EHRs, perceived usefulness of EHRs, and attitude toward adoption of EHRs. However, the association between the age of the clinician and perceived usefulness of EHRs was an exception and suggests that older respondents in this study are less likely to view EHR technology as useful in their healthcare practice, compared with their younger colleagues.

Table 7 presents the results of Model 2, which addresses how well the behavioral health clinicians’ perceived ease of use of the EHR predicts their attitude toward adoption of the EHR. The results of this model show that the perceived ease of using EHRs accounts for 24.8 percent of the total variance for attitude toward adoption of EHRs, R2 = .248, F(1, 93) = 30.70, p < 0.01. The Durbin-Watson statistic was 1.92, indicating that the assumption of independent errors was met. As presented in Table 5, perceived ease of using EHRs is significantly associated with attitudes toward adopting the EHR technology (β = .50, p < .01).

Table 8 presents the results of Model 3, which addresses how well the behavioral health clinicians’ perceived usefulness of EHRs predicts their attitude toward adoption of EHRs. The results of this model reveal that perceived usefulness of EHRs accounts for 71.2 percent of the total variance, R2 = .712, F(1, 93) = 229.85, p < 0.01. The Durbin-Watson statistic was 2.3, indicating that the assumption of independent errors was met. Regression coefficients presented in Table 6 show that perceived usefulness of EHRs is significantly associated with the attitude toward adopting EHRs (β = .84, p < .01). These results suggest that behavioral health clinicians’ perceived usefulness of EHRs is positively associated with their attitude toward adoption of EHRs.

In Model 4, all variables used in the previous models (i.e., age, years in practice, perceived ease of using EHRs, and perceived usefulness of EHRs) were entered into regression analysis to examine how well they predict clinician attitudes toward adoption of EHRs. The results of Model 4 are presented in Table 9. The overall explained variance increases to 72.3 percent of the total variance, R2 = .723, F(4, 90) = 58.79, p < 0.01. The Durbin-Watson statistic was 2.3. The assumption of independent errors is also reached (variance inflation factor [VIF] = 1.43), which suggests that the two predicting scale variables are moderately associated. If the VIF is equal to 1, the assumption is that there is no multicollinearity among predictors, but if the VIF is greater than 1, the predictors may be moderately correlated. The results of regression analysis above show that the VIF for the perceived ease of using EHRs and perceived usefulness of EHRs are about 1.4, which suggests only a moderate correlation. A VIF between 5 and 10 indicates high correlation, which in most cases is problematic. If the VIF is greater than 10, it can be assumed that the regression coefficients are weakly estimated due to multicollinearity.26 This correlation between the scale predictors, however, might explain the significant effect of only one of them (i.e., perceived usefulness of EHRs, β = .82, p < .01) after all variables are input into the same model. The results of Model 4 indicate that perceived usefulness of EHRs is the only significant predictor of clinician attitudes toward adoption of the EHR technology.

Taken together, the results of the inferential analysis suggest that of all the predictors, perceived proficiency in computer use and the frequency of using EHR technology are significantly related to some behavioral healthcare clinician attitudes. Perceived ease of use is associated positively with clinicians’ attitudes toward adoption of EHRs as well. With respect to the scale measures, the perceived usefulness of EHRs was found to be significantly related to behavioral healthcare clinician attitudes. Additionally, a meaningful relationship was found between clinician age and perceived usefulness of EHRs.

Discussion

The data presented in this study reveal important information for both individuals and institutions in behavioral health to more thoroughly understand the factors that influence adoption of EHRs. Although the generalizability of the conclusions found in the data may have limitations, the study was able to determine the following:

  1. Perceived ease of use and usefulness of the EHR positively influence adoption, and
  2. Older clinicians do not find the EHR as useful as younger clinicians do, despite reporting its ease of use similarly to younger clinicians.

The results of this study demonstrate that behavioral health clinicians’ perceptions of the ease of use and usefulness of EHRs are positively associated with attitudes toward adoption of EHRs. This finding provides support for a concept that may seem intuitively correct but for which no empirical evidence has been reported for behavioral health clinicians.

Furthermore, a statistically significant relationship was found between the age of the behavioral health clinician and the perceived usefulness of EHRs. Older clinicians were less likely to find EHRs useful in their professional practice, compared with younger clinicians. The years in practice were not statistically related to the perceived usefulness of EHRs. Also, no statistically significant relationship was discovered between either age or years in practice and the attitude toward use of EHRs. It appears that older clinicians believe that the EHR is easy enough to use but do not find it useful to them. What is not addressed in the finding is why older clinicians do not think the EHR is useful.

The results of this study indicate that clinician age or years in practice did not significantly affect perceived ease of use or attitude toward adoption of the EHR. Interestingly, while older clinicians did not seem to have any significant differences in ease of use, they did not find the EHR useful to them. It may be interesting to explore the self-reporting of ease of use by operationalizing the variable in a more objective manner. For example, it is possible that older clinicians may actually have more difficulty mastering EHRs than they report, and the resulting finding that the EHR is not useful may instead be explained, at least in part, by a lack of ability or task mastery that the clinician either is not aware of or is hiding.27

These results of this work can be examined within the context of other medical specialties including hospitals and physicians in small office settings. Wilkins et al.28 performed a study in small hospitals throughout rural Arkansas in which a survey was administered to 100 health information managers measuring the perceived usefulness and perceived ease of use of EHRs. Respondents who adopted EHR technology found it to be beneficial in their work and perceived that it would be easy to navigate. Consistent with the findings from this study, the authors concluded that perceived ease of use and perceived usefulness are driving factors toward adoption of technology and that attitude and receptiveness to change are essential to facilitate adoption. Gagnon et al.29 performed a systematic review of 101 studies, mostly in hospitals, focused on factors that increase adoption and limit the implementation of information and communication technologies in healthcare settings. Their findings demonstrate that the perception of benefits (system usefulness) of the innovation was the most common facilitating factor, followed by ease of use. To this point, in a large national survey study of a representative sample of US physicians (n = 2,578), EHR adoption in small office settings was reported to be as low as 17 percent,30 with 20 percent of respondents expressing reservations about the ease of use and reliability of the system. Taken together, the findings from this study complement the literature from other healthcare settings acknowledging the importance of attitudes and perceptions of technology and its usefulness as essential aspects of facilitating acceptance and adoption of EHRs.

Limitations

Although the current study provides significant and informative findings, several limitations must be noted. The study participants were all licensed marriage and family therapists located within a 50-mile radius of a large, metropolitan area. This group may not be representative of marriage and family therapists located in other geographic areas across the United States, and they may not be representative of other licensed behavioral health clinicians, including social workers, professional counselors, psychologists, or psychiatrists. Additional studies examining other behavioral health disciplines across wider geographic areas are recommended and would be helpful to improve the context in which this research is examined.

Another potential limitation of the current study pertains to the study respondents’ self-selection to participate.31, 32 There may be unknown factors that contributed to a decision to participate, such as a bias toward or away from ease of use or usefulness of EHRs.

A third limitation that needs to be acknowledged is the sample size. While the sample was adequately powered for the purpose of this study, the limited number of respondents may not be representative or generalizable to the behavioral healthcare clinician population; therefore, the conclusions from this work must be viewed within the scope of this limitation.

Conclusion

The current study extends what is known about EHR adoption in the medical community and extends exploration into the behavioral healthcare space. Not only has behavioral health been a late adopter of EHRs institutionally speaking,33 some authors have suggested that these clinicians may be resistant to adoption as well.34 This work suggests that to improve the adoption of EHRs among behavioral health clinicians, the EHR must be viewed as useful. Behavioral health organizations and professional associations should work collaboratively to mitigate concerns about workflow burden and effects on the physician-patient relationship and to demonstrate the value of EHRs to improve professional practice, efficiency, safety, effectiveness, and patient outcomes.

In this study, the gap in the EHR adoption literature as it applies to behavioral health clinicians has been addressed in a small but meaningful way. This information may encourage organizations to be mindful of the presence of older clinicians as a potential late adopter group to improve the transition to EHR implementation with positive attitudes. Future research could focus on factors that may influence behavioral health clinicians’ perceptions and attitudes, including theoretical orientation, current employment, and institutional setting, among others. Ultimately, the EHR is here to stay, and finding ways to improve adoption rates and attitudes around this technology will help clinicians, healthcare organizations, and, most importantly, the consumers of behavioral healthcare services.

 

Competing Interests/Support

No author reports a conflict of interest or financial disclosure.

 

Authors’ Access to Data

The corresponding author (SO) had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

 

Stephen Odom, PhD, is the CEO and Chief Clinical Officer of New Vista Behavioral Health in Orange County, CA.

Kristen Willeumier, PhD, is a Senior Fellow at the Society for Brain Mapping and Therapeutics in West Hollywood, CA.

 

Notes

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  2. Blumenthal, D., and M. Tavenner. “The ‘Meaningful Use’ Regulation for Electronic Health Records.” New England Journal of Medicine 363, no. 6 (2010): 501–4.
  3. Morton, M. E., and S. Wiedenbeck. “A Framework for Predicting EHR Adoption Attitudes: A Physician Survey.” Perspectives in Health Information Management 6 (2009): 1a.
  4. Menachemi, N., and T. H. Collum. “Benefits and Drawbacks of Electronic Health Record Systems.” Risk Management and Healthcare Policy 4 (2011): 47–55.
  5. Manca, D. P. “Do Electronic Medical Records Improve Quality of Care? Yes.” Canadian Family Physician 61, no. 10 (2015): 846–47, 850–51.
  6. Ajami, S., and T. Bagheri-Tadi. “Barriers for Adopting Electronic Health Records (EHRs) by Physicians.” Acta Informatica Medica 21, no. 2 (2013): 129–34.
  7. Magnus, M., J. Herwehe, L. Andrews, L. Gibson, N. Daigrepont, J. M. De Leon, N. E. Hyslop Jr., S. Styron, R. Wilcox, M. Kaiser, and M. K. Butler. “Evaluating Health Information Technology: Provider Satisfaction with an HIV-Specific, Electronic Clinical Management and Reporting System.” AIDS Patient Care and STDS 23, no. 2 (2009): 85–91.
  8. Matthews, E. B. “Integrating the Electronic Health Record into Behavioral Health Encounters: Strategies, Barriers, and Implications for Practice.” Administration and Policy in Mental Health and Mental Health Services Research 44, no. 4 (2017): 512–23.
  9. Greene, J. “Behavioral Health Data in the Electronic Health Record: Privacy Concerns Slow Sharing.” Annals of Emergency Medicine 62, no. 4 (2013): 19A–21A.
  10. Matthews, E. B. “Integrating the Electronic Health Record into Behavioral Health Encounters: Strategies, Barriers, and Implications for Practice.”
  11. Gagnon, M. P., M. Desmartis, M. Labrecque, J. Car, C. Pagliari, P. Pluye, P. Fremont, J. Gagnon, N. Tremblay, and F. Legare. “Systematic Review of Factors Influencing the Adoption of Information and Communication Technologies by Healthcare Professionals.” Journal of Medical Systems 36, no. 1 (2012): 241–77.
  12. Shank, N., E. Willborn, L. Pytlikzillig, and H. Noel. “Electronic Health Records: Eliciting Behavioral Health Providers’ Beliefs.” Community Mental Health Journal 48, no. 2 (2012): 249–54.
  13. Greene, J. “Behavioral Health Data in the Electronic Health Record: Privacy Concerns Slow Sharing.”
  14. Shank, N., E. Willborn, L. Pytlikzillig, and H. Noel. “Electronic Health Records: Eliciting Behavioral Health Providers’ Beliefs.”
  15. Lardiere, M. R. “Unlocking and Sharing Behavioral Health Records.” Journal of AHIMA 84, no. 4 (2013): 36–40.
  16. Matthews, E. B. “Integrating the Electronic Health Record into Behavioral Health Encounters: Strategies, Barriers, and Implications for Practice.”
  17. Martin, D. J., J. P. Garske, and M. K. Davis. “Relation of the Therapeutic Alliance with Outcome and Other Variables: A Meta-analytic Review.” Journal of Consulting and Clinical Psychology 68, no. 3 (2000): 438–50.
  18. Murray, C. E. “Diffusion of Innovation Theory: A Bridge for the Research-Practice Gap in Counseling.” Journal of Counseling and Development 87, no. 1 (2009): 108.
  19. Shank, N., E. Willborn, L. Pytlikzillig, and H. Noel. “Electronic Health Records: Eliciting Behavioral Health Providers’ Beliefs.”
  20. Matthews, E. B. “Integrating the Electronic Health Record into Behavioral Health Encounters: Strategies, Barriers, and Implications for Practice.”
  21. Ibid.
  22. Tabachnick, B. G., and L. S. Fidell. Using Multivariate Statistics. 6th ed. Boston: Pearson, 2013.
  23. Aldosari, B. “Factors Affecting Physicians’ Attitudes about the Medical Information System Usage and Acceptance Through the Mandated Implementation of Integrated Medical Information System at the Saudi Arabia National Guard Health System: A Modified Technology Acceptance Model.” Dissertation Abstract International: Section B. Sciences and Engineering. 64 (7): 3169B.
  24. Morton, M. E. “Use and Acceptance of an Electronic Health Record: Factors Affecting Physician Attitudes.” PhD dissertation, Drexel University, 2008.
  25. Field, A. Discovering Statistics Using SPSS. 3rd ed. London: Sage, 2009.
  26. Ibid.
  27. Decker, S. L., E. W. Jamoom, and J. E. Sisk. “Physicians in Nonprimary Care and Small Practices and Those Age 55 and Older Lag in Adopting Electronic Health Record Systems.” Health Affairs 31, no. 5 (2012):1108–14.
  28. Wilkins, M. A. “Factors Influencing Acceptance of Electronic Health Records in Hospitals.” Perspectives in Health Information Management 6 (2009): 1f.
  29. Gagnon, M. P., M. Desmartis, M. Labrecque, J. Car, C. Pagliari, P. Pluye, P. Fremont, J. Gagnon, N. Tremblay, and F. Legare. “Systematic Review of Factors Influencing the Adoption of Information and Communication Technologies by Healthcare Professionals.”
  30. DesRoches, C. M., E. G. Campbell, S. R. Rao, K. Donelan, T. G. Ferris, A. Jha, R. Kaushal, D. E. Levy, S. Rosenbaum, A. E. Shields, and D. Blumenthal. “Electronic Health Records in Ambulatory Care—A National Survey of Physicians.” New England Journal of Medicine 359, no. 1 (2008): 50–60.
  31. Daniel, J. Sampling Essentials: Practical Guidelines for Making Sampling Choices. Thousand Oaks, CA: Sage, 2012.
  32. Remler, D., and G. Van Ryzin. Research Methods in Practice: Strategies for Description and Causation. Thousand Oaks, CA: Sage, 2010.
  33. Holden, R. J., and B. T. Karsh. “The Technology Acceptance Model: Its Past and Its Future in Health Care.” Journal of Biomedical Informatics 43, no. 1 (2010): 159–72.
  34. Craig, S. L., and M. V. Calleja Lorenzo. “Can Information and Communication Technologies Support Patient Engagement? A Review of Opportunities and Challenges in Health Social Work.” Social Work in Health Care 53, no. 9 (2014): 845–64.

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Stephen Odom, PhD, and Kristen Willeumier, PhD. “Attitudes and Perceptions of Behavioral Health Clinicians on Electronic Health Record Adoption: Overcoming Obstacles to Improve Acceptance and Utilization.” Perspectives in Health Information Management (Spring 2018): 1-19.

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