IJGG Journal

Background and Significance 

Effective interprofessional communication is critical for delivery of safe patientcare [1].Incomplete interprofessional communications can lead to medical errors and adverse events in healthcare settings [2]. Nursing homes (NH), as well as other healthcare settings such as operating rooms and intensive care units (ICU), have high vulnerability to adverse events from poor nurse 

physician communication, including preventable hospitalizations [2-5]. Interprofessional communication is the second safety goal after correct identification of the patient in the Joint Commission’s 2020 National Patient Safety Goals for Long-Term Care (LTC) [6]. Poor nurse-healthcare provider (HCP) communication can also lead to low work satisfaction among HCPs and nurses [1,2]. 

Communication in the nursing homes is complex due to the healthcare setup in NH – nurses work in shifts, HCP may have variable NH rounding schedules, and depending upon the time of the day when laboratory tests are reported to the HCP, nurse 

HCP communication may occur more frequently during specific hours of the day than at other times. Therefore, face-to- face interaction may be limited in NH. Although traditional methods of communication between nurses and HCPs in NHs have been verbal (face-to face or over the phone) and/or written (pages, e-mails, or fax) but most often non-face-to-face communication occurs during after-hours and on weekends when HCPs are not in the facility [5]. Synchronous communication via telephone is a common method used in NHs. It provides simultaneous exchange of information but can cause time burden, frustration, and frequent interruptions in work or personal activities [7,8]. Similarly, communication via pages leads to interruptions for HCP, with resultant distraction from the 

Primary task and cognitive overload [9,10]. Multitasking to tackle frequent interruptions can lead to medical errors and compromise patientcare [11]. 

Currently, nurses and HCP both perceive multiple barriers to effective communication. Nurses reports lack of timely HCP response, inadequate time to discuss clinical issues, lack of professionalism or trust, language barriers, and inadequate collaboration [5]. Barriers reported by HCPs include lack of nurses’ preparedness or competency to report patient-related issues while nurses reported lack of interest and professional respect [7]. On the other hand, better communication and information exchange among the NH staff leads to improved decision- making, which reduces the number of residents transferred to the hospital [12]. 

Advancement in the digital technology has introduced new channels of communication to improve efficiency and control cost [13]. It ranges from cell phone text messages, to advanced digital devices and smartphone applications, with 2- way camera, interactive video calls, wireless emails, web-cams, secure chat rooms, online forums and other smartphone applications [7,13,14]. 

Smartphones offer Asynchronous Communication (AC) tools to share information [14,15]. AC is separated by time, usually in the form of text messages and emails[15]. It is considered less interruptive and is a preferred method for non-urgent situations[15]. Hospitals have adopted smartphone applications to allow sensitive patient health information (PHI), in emergency rooms, medical units and ICUs to be shared in a timely manner, overcoming a shortcoming with the traditional modalities of phone/pager [2,10,16]. Such structured communication methods provide benefits of improved patient care, team work and job satisfaction [17]. Smartphone applications have emerged as potentially useful tool in patientcare and mobile clinical communication in diverse healthcare setting, [16] it is still relatively new phenomenon in NHs. To our knowledge, there are no prior studies that specifically investigated this mode of communication. 

Objective 

In this study, we examined the perceived impact of a smartphone app, eMediCallTM in three NHs by survey questionnaire. Established in 2012, eMediCall™ was specifically designed for LTC to provide communication that was Health Insurance Portability and Accountability Act (HIPAA) compliant.18 With eMediCallTM a nurse generates a patient-related message and labels it as emergent, urgent, or routine. HCP receives an alert and in response to the message HCP can give “orders”, ask clarifying questions, or call the nurse directly. 

Materials and Methods 

Research Design 

eMediCall™ was introduced to many NHs in Maryland in 2015. We recruited nurses and HCPs from three NH facilities where this app is used. HCPs included physicians, nurse practitioners and physician assistants. Nurses included licensed practical nurses and registered nurses. We created survey questions based on the common themes noted by the nurses and HCP of three NH through informal discussions. The questions that were included in the final version of the survey included respondent demographic details and questions to elicit perceptions of nurses and HCP on aspects of usability and clinical communication of the app (Table 3). We defined “Usability” of this application as capacity to allow its users to perform the intended tasks with ease. Usability may reflect effectiveness, efficiency, satisfaction, usefulness, aesthetic, learnability, simplicity, intuitiveness, understandable, and attractiveness [19]. We focused on usefulness, understandability, and simplicity to reflect on app usability. 

 We defined “Clinical Communication” as any exchange of clinical information related to patientcare. The aspects of clinical communication surveyed included; response time, prioritization of messages, data sharing, patientcare, and social interaction. Responses were rated on 5-point Likert scale, with 1 (strongly disagree), 3 (neutral) to 5 (strongly agree). Project was approved by our Institutional Review Board. Participation in survey was voluntary, and agreement to participate was considered as consent to this study.

Statistical Methods 

Demographic data was summarized by service role (HCP vs. nurse), followed by two- step statistical analysis of the survey questions. Licensed practitioner nurses and registered nurses were included in “nurse” category. 

First, principal component analysis (PCA) was used to assess the dimensionality (i.e., content domains) of survey scale based on patterns of the correlations between different survey items. Exploratory Factor Analysis (EFA) was used to assess the dimensionality and internal consistency of the eMediCall™ efficacy scale. The number of factors to extract was selected via principal component analysis and based on “eigenvalue-greater 

than-1” rule, percentage variance explained, and the parallel test. To accommodate the discrete scale of item responses, polychoric correlation matrix was used for the EFA. The EFA was conducted via a weighted least square mean and variance estimator and the GEOMIN-rotated loadings were presented to aid interpretation. 

We further assessed the internal construct validity of the scale by conducting confirmatory factor analysis (CFA) based on

the EFA results. Adequacy of model fit was assessed using the Chi-square test of model fit (>0.05), Comparative Fit Index (CFI, >0.90), the Tucker-Lewis Index (TLI, >0.90), and the root mean square error of approximation (RMSEA, <0.05), and Standardized Root Mean Square Residual (SRMR, <0.08) or Weighted Root Mean Square Residual (WRMR, <1). Modification indices were used to identify parameter constraints (e.g., constraining a loading of a particular factor indicator to zero in the CFA) that had the most influence on the fit statistics of the CFA model. The PCA was fit using STATA version 15.0 and the EFAs and CFAs were fit using MPLUS version [8]. 

In the second step, frequency distribution of item responses from the two groups (HCP and nurses) were compared using the Fisher’s exact test. 

Results 

Survey was given to 100 individuals and 18 HCPs, and 33 nurses responded to the survey with response rate of 51%. From the survey results noted that 49% of respondents were using the eMediCall™ app for more than a year (Table 1). 

Table 1: Demographic Characteristics of Survey Respondents.

Principal component analysis identified three principal components with eigenvalue greater than 1 that collectively explained 76% of the variance in the 19-item scale. The parallel test on the other hand identified one dominating principal component. Given that the second principal component accounted for almost 10% of the total variance, we conducted exploratory factor analysis with both one and two factors (Table 2). 

Table 2: Results from exploratory and confirmatory factor analysis (Factor 1 – clinical communication, Factor 2 – usability)

The one-factor and the two-factor model appeared to have largely comparable goodness- of-fit test statistics. The overall fit of the 2-factor model was good based on CFI, TLI, and SRMR. The two-factor model is composed of factors (domains) usability (indicated by items from survey questions [1, 2, 7, 17-19] and clinical communication (indicated by items from survey questions [3-6, 8-16]; and two factors were moderately correlated with a correlation coefficient of 0.67. The loading coefficients in (Table 2) represent the degree of influence of latent factors on their corresponding indicators (i.e., questionnaire items). The percentage of total variance in each item left unexplained by the two factors in the 2-factor model ranged from 13% to 39%, suggesting a nontrivial level of measurement error or other factors that might have influenced the item responses. The results of the CFA are presented in (Table 2) based on a priori mapping (informed by EFA) between items from survey questions [3-6, 8-16, 19] and factor 1 (i.e., clinical communication) and between items from survey questions [1, 2, 7, 17-19] and factor 2 (i.e., usability). Based on examination of the modification indices, the item [19] was allowed to be influenced by both factors; however, the direction of the influence seemed to be in the opposite direction, with a negative correlation with the usability factor and positive correlation with the clinical communication factor. The fit of the model appears to be good based on RMSEA, CFI, TLI, and WRMR (Table 3). Questions 20 and 21 were excluded from final analysis, as they did not correlate to the app’s usability and clinical communication.

Abbreviated questions, detailed questions are listed under the Appendix. *Based on Fisher’s exact test HCP= Healthcare Provider; n= Total number of respondents from that group

Usability 

Table 3: eMediCall™ Survey Questions and Summary survey item response frequencies by role Limitations 

Nurses were more likely than HCPs to agree that eMediCallTM messages were easily understood and barriers to language were removed (question #7, p-value 0.012). HCPs were more likely to agree that the app reduced frustration related to unclear communication (question #17, p-value 0.012). Both groups agreed that the app software was simple to operate, performed desired functions with minimal malfunction, and helped to create understanding and mutual respect in communication. 

Clinical Communication 

Nurses were more likely to agree that eMediCallTM app helped share adequate information to make clinical decisions, but the difference was not statistically significant (question 10, p=0.063). 

Nurses and HCPs agreed that the app enabled users to stay mobile, 2-way communication required less effort, was time efficient, caused less interruptions, helped create mutual respect in communication, improved HCP response time, and helped with prioritization of messages. They both expressed that app’s added features of sharing laboratory results, vital signs, clinical status, and radiology, helped deliver better patientcare in a timely manner. They liked features of storage and retrieval of information. Respondents agreed that it enabled them to work in new ways as healthcare team and increased nurses’ satisfaction. 

Discussion 

Our study shows that smartphone apps such as eMediCall™ can have a positive impact on perceptions of nurse-HCP communication in a NH setting in aspects of usability of the app and facilitation of clinical communication. Despite being a small study, our results add to the growing literature that supports the use of smartphone in healthcare. Smartphone apps like eMediCall in NH setting can improve communication by offering a platform for clear and accurate communication among healthcare providers, the option of prioritizing message can reduce unnecessary interruptions and sharing secured patient information can expedite clinical decisions resulting in timely patientcare. Smartphone apps in NH setting can also improve perceptions of nurses’ satisfaction and mutual respect with HCP. 

Based on the statistical analysis the highest correlation for this app was related to its usability, which we believe makes it an attractive tool to improve interprofessional communication in a NH setting. Nurses suggested embedding the PHI from electronic medical record (EMR) directly into the eMediCallTM messages which can further improve usability of such smartphone apps. 

This study has several limitations. It was limited to three nursing homes in Maryland. Reponses reflect subjective experiences and perceptions of the respondent’s rather than objective or measured outcomes. Additionally, the sample size was small, and the number of HCP respondents was disproportionately less as compared to the nurses, which may limit generalizability of the results. Lastly, this study was not designed to evaluate patientcare outcomes. 

Conclusion 

Effective and efficient nurse-HCP communication is a key to good patientcare. There is a paucity of research about role of smartphone communication apps in the NH setting[20].The NH environment is unique in terms of limited face-to face interaction between nurses and HCP, with most of the communication occurring distantly either over the phone or by email. Barriers to an effective communication differs in NH as comparted to an acute care setting. Our study shows that Asynchronous communication via eMedicalTM can improve perceptions of effective communication by secure sharing of PHI, enhancing clinical communication, expediting clinical decisions, and facilitating timeliness of patientcare. This mode of communication also increases nurses’ perceived satisfaction with HCP, which has been barrier for effective communication with traditional methods. 

Next steps include, study objective measures of communication, and patient outcomes associated with nurse-HCP communication in NH setting. Furthermore, research is needed to explore usability and clinical communication of other smartphone apps in NH. 

For wider application of this app, future studies may explore role of organizational culture and physical location of the NH (urban vs. rural). 

Funding sources: This research did not receive any funding from agencies in the public, commercial, or not-for-profit sectors.

References

1. Winn P, Cook JB, Bonnel W (2004) Improving Communication Among Attending Physicians , Long-Term Care Facilities , Residents, and Residents’ families. J Am Med Dir Assoc 5:114-22.
2. Renz SM, Carrington JM (2016) Nurse-physician communication in long-term care: Literature review. J Gerontol Nurs 42: 30-37.
3. Halverson AL, Casey JT, Andersson J, Anderson K, Park C, et al. (2011) Communication failure in the operating room. Surgery 149: 305-310.
4. Reader TW, Flin R, Cuthbertson BH (2007) Communication skills and error in the intensive care unit. Curr Opin Crit Care 13: 732-736.
5. Tjia J, Mazor KM, Field T, Meterko V, Spenard A, et al. (2009) Nurse-Physician Communication in the Long-Term Care Setting : Perceived Barriers and Impact on Patient Safety. J Patient Saf 5: 145-152.
6. Nursing Care Center (2020) National Patient Safety Goals.
7. Cadogan MP, Franzi C, Osterweil M, Hill T (1999) Barriers to Effective Communication in Skilled Nursing Facilities: Differences in Perception between Nurses and Physicians. J Am Geriatr Soc 47: 71-75.
8. Fowkes W, Christenson D, McKay D (1997) An Analysis of the Use of the Telephone in the Management of Patients in Skilled Nursing Facilities. J Am Geriatr Soc 45: 67-70.
9. Parker J, Coiera E (2000) Improving Clinical Communication: A View from Psychology. J Am Med Inform Assoc 7: 453.
10. Alvarez G, Coiera E (2006) Interdisciplinary communication: An uncharted source of medical error? J Crit Care 21: 236-242.
11. Laxmisan A, Hakimzada F, Sayan OR, Green RA, Zhang J, et al. (2007) The multitasking clinician: decision-making and cognitive demand during and after team handoffs in emergency care. Int J Med Inform 76: 801-811.
12. Powell KR, Popescu M, Alexander GL (2021) Examining Social Networks in Text Messages About Nursing Home Resident Health Status. J Gerontol Nurs 47: 16-22.
13. Fortney JC, Burgess JF, Bosworth HB, Booth BM, Kaboli PJ (2011) Review: A Reconceptualization of Access for 21^st Century Healthcare. J Gen Intern Med 26: 639-647.
14. Ventola CL (2014) Mobile devices and apps for health care professionals: uses and benefits. P T 39: 356-364.
15. Coiera E (2006) Communication systems in healthcare. Clin Biochem Rev 27: 89-98.
16. Mosa ASM, Yoo I, Sheets L (2012) A Systematic Review of Healthcare Applications for Smartphones. BMC Medical Informatics and Decision Making 12: 67.
17. Connor CO, Friedrich JO, Scales DC, Adhikari NKJ (2009) The Use of Wireless E-Mail to Improve Healthcare Team Communication. J Am Med Informatics Assoc16: 705-713.
18. EMediCall-Patented, HIPAA Compliant, Online Medical Answering Service-Better than Secure Text Messaging & Texting. 2020.
19. Baharuddin R, Singh D, Razali R (2013) Usability dimensions for mobile applications-a review. Research Journal of Applied Sciences, Engineering and Technology 5: 2225-2231.
20. Huang YH, Garrett SK (2012) Defining characteristics of communication quality in culturechanged long-term healthcare facilities. Journal of Communication in Healthcare 5: 227-238.