Casey, I. (July, 2016). The effect of education on portal personal health record use. Online Journal of Nursing Informatics (OJNI), 20(2), Available at http://www.himss.org/ojni
The purpose of this study was to examine the rate of portal personal health record (PHR) use among chronically ill older adults to evaluate the effectiveness of an educational intervention in improving PHR adoption, and to identify patients’ thoughts about the PHR. The quasi-experimental study was performed at a primary care group practice in central Florida.
Participants were recruited during provider appointments. Fifty participants completed the Background and Computer Questionnaire (DBQ) and received the educational intervention followed by a four-week follow-upphone survey. To evaluate the effectiveness of the educational intervention, the study pair matched participants, to form a non-participant control group.
Participants’ computer use comfort level increased significantly four weeks after the PHR educational intervention (Z = -1.668, p < 0.005). In addition, the amount of PHR use by the participant group (M = 1.08) was significantly higher, compared to the pair-matched control group (M = 0.16), U = 735.5, p = 0.001. Analysis of the qualitative component indicated that patients are willing to use the PHR if their laboratory results are up-to-date and available for review.
Hands-on computer instructions are an effective method to increase PHR use among chronically ill adult primary care practice patients. Computer training and education promote and improve people’s overall computer use comfort levels. Patients feel that the PHR is a valuable tool if their data are current and accessible.
A personal health record portal is an online tool that allows people to access, view and manage their personal health information and facilitate self-management and care coordination via the Internet (Ricciardi, Mostashari, Murphy, Daniel, & Siminerio, 2013; Shade, Steward, Koester, Chakravarty, & Myers, 2015). The portal may be a stand-alone technology or a component of a healthcare provider’s electronic health record (EHR) system (Archer, Fevrier-Thomas, Lokker, McKibbon, & Straus, 2011). In this study, the term portal personal health record (PHR) refers to an Internet-accessed patient health record linked to a provider EHR.
Integrating EHR data into PHRs allows patients to gain access to their health information. By having their patients use a PHR, providers meet a key requirement for patient engagement in the Meaningful Use Stage 2 criteria for EHR technology (Griskewicz, 2014). Meaningful Use is a Centers for Medicare and Medicaid Services (CMS) incentive program that rewards eligible providers when specific EHR objectives are met (Office of the National Coordinator for Health Information Technology, n.d.)
Giving patients access to their health information and encouraging the use of PHRs can better position patients to self-manage their conditions, facilitate patient engagement, and improve patient-provider communication (Ricciardi, et al., 2013). Self-management is a unique approach wherein patients assume greater responsibility for their own health care (Baumann & Dang, 2012). The PHR represents an emerging opportunity to improve patients’ access to health information and is viewed as an important step toward shared medical decision making (Daniel, Deering, & Murphy, 2014).
PHRs may be of particular value to patients with chronic conditions (Tenforde, Jain, & Hickner, 2011). Adding medical resources, such as the PHR, may enhance chronic disease patient self-management and ultimately improve health outcomes. The World Health Organization (WHO) defines chronic disease as one which involves ongoing management over a period of years (World Health Organization, 2011). Treating a chronic disease requires care coordination among a wide range of providers and access to medical records and monitoring systems (Nolte & Osborne, 2013).
Since evidence indicates that self-management may enhance quality of life (Centers for Disease Control and Prevention (CDC), 2014), the Centers for Medicare & Medicaid Services (CMS) recommends quality metrics that require the provider to enhance self-management abilities for patients with chronic diseases (CMS, 2013). This study explored the current trend of PHR adoption by the chronically ill, including PHR benefits, and hypothesizes that educational interventions may improve PHR adoption within the selected patient population.
Prevalence and Impact of Chronic Disease
The prevalence of chronic disease has increased dramatically over the past 20 years, making it the number one cause of death in the United States (CDC, 2014). The WHO (2011) also confirms that chronic diseases are the leading cause of death and disability globally. In the United States, almost half of all adults are living with chronic disease; 84% of all health spending is allocated to people with chronic conditions (CDC, 2014). To reduce this dramatic development and impact to society, effective chronic disease management and healthcare consumer engagement is essential (Sands & Wald, 2014). Research supports that individuals with chronic conditions have better health outcomes when able to self-manage and collaborate with their providers (Melchior et al., 2014; Nolte & Osborne, 2013).
The Role of Self-Management in Lessening Chronic Disease Burden
In an attempt to lessen the chronic disease burden on society and lower healthcare spending allocated to people with chronic conditions, researchers have focused on prevention and better management (Bauer, Briss, Goodman, & Bowman, 2014). One such focus is to empower patients to better manage their own care and become engaged and active participants when making healthcare decisions.
Research shows that patients exposed to paternalistic care, where the provider makes decisions for patients, often require more health care and incur higher healthcare costs (Robert Wood Johnson Foundation, 2014). In a paternalistic system, healthcare decision making is left to the provider and it is assumed that the clinician is the expert who knows best. Krist and Woolf (2011) found that the effect of paternalistic care on patients is detrimental; it creates a dependency that is incongruent with modern healthcare tenets.
Most healthcare consumers prefer a more patient-centered care model (Tenforde et al., 2011). Patients want to be partners with their providers and want to make decisions about their health in an informed and collaborative manner (Krist & Woolf, 2011). Consequently, the patient-provider relationship should be based on mutual respect and shared decision making. In a collaborative effort, the healthcare team should empower patients with self-management tools to encourage decisions that improve health-related behaviors and clinical outcomes.
PHR as a Self-Management Tool for Patients with Chronic Disease
The PHR provides a secure online website that gives patients convenient 24-hour access to their health information from any location with an Internet connection (Office of the National Coordinator for Health Information Technology, 2015a). More importantly, patients have access to their health information during emergencies, while traveling, and on a continued basis to track their health over time (Office of the National Coordinator for Health Information Technology, 2015b). The PHR, when integrated into the delivery of care, allows patients to review their health information and enables patients and providers to directly communicate with each other using a secure messaging system. Recent studies indicate that health technology innovations, such as the PHR, empower patients to better manage their health results (Tenforde et al., 2011).
As the healthcare industry shifts into the digital age, patients now have the ability to more efficiently collaborate with their providers and actively engage in their own care with self-management tools such as the PHR. The PHR is an additional care delivery tool that helps individuals to reflect on their health and choose healthy behaviors (Higgins, Murphy, Worcester, & Daffey, 2012). The Registered Nurses' Association of Ontario (2010) recognized the importance of the PHR as a self-management tool and developed evidence-based guidelines that recommend the promotion of PHR adoption as a strategy to support chronic disease self-management.
Barriers to PHR Use
While PHRs have been available for more than 10 years, they are used only by a fraction of United States healthcare consumers (Markle Foundation, 2011). Healthcare provider practices struggle to promote patient adoption; the reasons are unclear. Krist et al. (2014) found that even large scale advertising campaigns fail to increase the number of patients using the PHRs of healthcare organizations. It appears that just making a PHR available will not ensure successful use by patients.
Patients are more likely to use PHRs if their providers recommend PHR adoption and staff is available to explain PHR features (Kerns, Krist, Longo, Kuzel, & Woolf, 2013). It is recommended that primary care providers integrate PHR use into the plan of care to increase usage rates (Krist et al., 2014). Although taking action to actively promote and facilitate PHR adoption seems logical, many providers do not have a structured program that improves PHR adoption (Butler et al., 2013).
Facilitators of PHR Use
Chronic disease self-management including PHR adoption The PHR allows patients to verify and reorder medication, access and print medical records, review lab reports, send secure messages, and examine visit summaries.
The PHR can also be used for interactive monitoring and coaching. Krist et al. (2014) found that the PHR may engage patients to actively participate in their treatment plan and use information in the PHR to better self-manage their chronic condition. While recent trends indicate a growing interest in providing self-management tools such as the PHR (Tenforde et al., 2011) to people with chronic diseases, the rate of adoption of these tools remains stagnant (Markle Foundation, 2011).
Innovative technologies such as the PHR allow patients to communicate more efficiently with their providers and actively engage and self-manage their own care; it is evident that the use of the PHR is an improvement over traditional patient care involvement. Accordingly, it seems only logical to inform and educate patients about PHR benefits and implement procedures to facilitate PHR use.
This study examined the effect of an educational intervention on the adoption of PHR among chronically ill adult primary care practice patients. This project used evidence-based research and clinical practice guidelines to evaluate a systematic process to actively promote and facilitate PHR use. Despite widespread interest in making patients’ medical records available, little PHR research has been conducted. Additional PHR research may lead to knowledge that may reduce healthcare costs and improve the quality of health care.
Review of Literature
Little was known about factors that influence PHR adoption until Logue and Effken (2012) developed the Personal Health Record Adoption Model (PHRAM), a theoretical framework that explains the interaction between personal, technological, environmental, and chronic disease factors and their influence on a person’s behavior. The complex interaction between these factors causes individuals to accept or decline the use of technology to improve their health.
Based on PHRAM’s factors associated with PHR adoption, a systematic literature review was performed. CINAHL Complete and PubMed were searched with the keywords PHR, patient portal, and chronic disease self-management. Of the 245 articles identified by the search, 50 were excluded based on title and abstract. One hundred ninety-five studies were screened and further reviewed based on the final inclusion criteria. A total of 49 articles were eligible for an in-depth appraisal (see Figure 1).
The identified studies were grouped into five major topics based on the research aims and related measures. The topics identified included chronic disease and self-management, factors affecting PHR use to manage health, patient engagement, barriers of PHR adoption, and facilitators of PHR adoption.
This evidence-based translational research project was conducted in two phases from August 3, 2015 until September 5, 2015. The Demographic and Background Questionnaire (DBQ) (Czaja et al. 2006a) described in detail in the data collection section below, was administered before the PHR educational intervention, and followed by a four-week follow-up survey.
The quasi-experimental approach was used to (a) assess PHR use among chronically ill adult primary care patients, (b) administer a PHR educational intervention, (c) observe factors associated with computer use, and (d) evaluate the effectiveness of the educational intervention among the participants compared to the pair-matched control group. Additionally, a qualitative component assessed the participants overall thoughts about the PHR. Harris et al. (2006) indicate that the quasi-experimental design is appropriate for nonrandomized intervention studies and commonly used in medical informatics research when randomized control studies are not feasible. A quasi-experimental methodology is capable of measuring change after an intervention (Polit & Beck, 2012) and is deemed practical and useful in the nursing and health informatics arena (Harris et al., 2006; Moran, Burson, & Conrad, 2014). Harris et al. (2006) found that the use of both a pretest and a comparison group enhance the validity and quality of the measurement method.
As the primary investigator, I completed the online Protecting Human Research Participants ethics training modules developed by the Collaborative Institutional Training Initiative (CITI). The course material and certification ensured that the wellbeing, safety, and privacy of research participants were protected. As an additional safeguard, the Institutional Review Board of Georgia College and State University reviewed this proposal and approved the study. A memorandum of understanding was signed between the medical director of the clinical research site, and Georgia College and State University on November 11, 2014.
Issues related to potential loss of privacy for participants were addressed in preparation for conducting this study. Participants selected a three-digit number in lieu of a name for matching the pre-intervention data with the follow-up phone survey results and all results were kept completely confidential. A secure webserver was used to deliver and analyze the survey information. All records were de-identified and stored in a locked area throughout the duration of the study and will be completely destroyed after three years.
This study took place at a primary care group practice (PCGP) in Lake County, Florida. The practice provides integrated healthcare services including health promotion, disease prevention, health maintenance, nutritional counseling, patient education, and diagnosis and treatment of acute and chronic conditions. The practice also has an internal medical laboratory providing clinical specimen testing services to their patients.
The PCGP clinic staff consisted of eight full- or part-time primary care providers (five physicians and three physician assistants), four licensed practical nurses, seven certified medical assistants, and a dietician. Non-clinical staff included six medical office assistants staffing the front desk, six medical billing and coding specialists, and three medical laboratory technicians. The PCGP accepted most commercial insurance plans, as well as Medicare.
The PCGP patient population totaled approximately 6,500 individuals with 75% White, 5% Hispanic, and 10% African American patients. The practice averaged about 120 patient visits per day. The PCGP did not provide services for pediatric patients. The PCGP patient population age ranged from 18 to 102 years, with an average age of 65 years.
In 2011, the practice transitioned from paper-based medical records to using eClinicalWorks, an electronic health record (EHR). The practice partners also decided to participate in the Medicare EHR Incentive Program that provides governmental reimbursement when EHR technology is used in ways that can positively impact patient care. For clinicians to participate in this program, they must be: (a) eligible, (b) registered, (c) use a certified EHR, (d) demonstrate and prove Meaningful Use, and (e) receive reimbursement (CMS, 2010).
Meaningful Use has to be demonstrated in multiple stages. For stage 1, CMS established objectives that all providers have to meet. Some objectives require a minimum percentage reporting to show that providers use their EHR in ways that can positively affect their patients’ health. Others specify an action that must be taken to prove Meaningful Use (CMS, 2010).
The PCGP registered for reimbursement in 2012 and successfully reported and met Meaningful Use Stage 1 criteria in 2013. To demonstrate Meaningful Use Stage 2 criteria, the providers must meet 17 core objectives and three menu objectives (CMS, 2012). One of the Meaningful Use Stage 2 core objectives included to “provide patients the ability to view, download, and transmit their health information online” (CMS, 2012). eClinicalWorks delivers this requirement with an integrated PHR application called Healow (eClinicalWorks, 2015). As of July 2015, the PCGP met Meaningful Use Stage 2 by enrolling 5% of their patient population for PHR use. Every newly enrolled patient receives a Healow PHR sign in with a temporary password. After signing into the PHR, the patient is prompted to choose a personal password. Patients who use the PHR are then able to view their medical records and use a secure messaging system to communicate with their PCGP health team electronically.
The PCGP patients scheduled from August 3 to August 18, 2015, were recruited for this study. A power analysis was conducted to determine an adequate sample size. Given an anticipated effect size (Cohen’s d) of 0.8, a desired statistical power level of 0.8, and a probability level of 0.05, the calculated minimum required total sample size was 42. Accordingly, the goal was to recruit up to eight participants per day with an anticipated total enrollment of 50-80 individuals within a 10 business-day period.
During the 10-day study implementation period, 580 individuals were scheduled for clinic appointments (see Figure 2). All medical records were screened; 300 individuals met the study eligibility criteria. A total of 52 individuals agreed to participate, 45 declined, and 203 were not approached during checkout while I was providing the educational intervention to individual study participants in a private office. Two individuals scoring greater than 28 on the Center for Epidemiologic Studies Depression Scale (CES-D) were excluded from participating in the study.
Participants had to speak English fluently, be 40 to 85 years old, and be diagnosed with a chronic condition. For the purpose of this study, the chronic disease operational definition by the World Health Organization was adopted: a chronic disease involves ongoing management over a period of years and includes, but is not limited to, heart disease, stroke, cancer, chronic respiratory diseases, and diabetes (World Health Organization, 2011).
Exclusion criteria were any mental, depressive, behavioral, or physical conditions that would preclude participants from completing a 20-minute questionnaire and a 10-minute educational intervention, as determined by the treating PCP. Depression was measured using the CES-D Scale; potential subjects with a score of 28 or greater were excluded from the study and a follow-up appointment with the subject’s PCP was arranged the same day. The cognitive symptoms of depression, such as loss of interest and fatigue would inhibit the participant's ability to engage fully in the PHR educational activity (Czaja et al., 2013; Sharit, Hernandez, Czaja, & Pirolli, 2008).
This study used two quantitative and one qualitative source for outcomes: the pre-intervention DBQ (Czaja et al., 2006b), EHR data, and a four week post-intervention follow-up phone interview.
Taha, Czaja, Sharit, and Morrow (2013) found that PHR use is influenced by education, age, and socio-economic background, as well as computer use attitudes and experience. Taha et al. gained the information primarily through the administration of the DBQ survey instrument (see Appendix A; Czaja et al., 2006b). The DBQ was developed in 2006 by a team of researchers from the Center for Research and Education on Aging and Technology (CREATE) at the University of Miami and published as Technical Report CREATE 2006-02 (Czaja et al., 2006b). The survey is a validated tool with five sections: (1) demographics, (2) health information, (3) Center for Epidemiologic Studies Depression Scale (CES-D), (4) Computer Questionnaire 1, and (5) Computer Questionnaire 2.
According to Czaja et al. (2006a), one purpose of the DBQ survey is to examine issues related to the successful use of technology by older adults. The questionnaire gathers information related to the use and perceptions of technical systems and can be used to establish a relationship between demographics, abilities, and the use and adoption of technology. The DBQ consists of questions in multiple-choice or a five-point Likert Scale, format; it is in large print to facilitate readability and requires about 25 minutes to complete. Permission to use the DBQ for this study was obtained from Dr. Sara Czaja, the instrument developer and director of CREATE. The DBQ was administered in its entirety before the educational intervention. The following paragraphs describe the components of the DBQ and the data collection for this study in detail.
An EHR audit was performed to evaluate the effectiveness of the educational intervention. The frequency of PHR messages sent to providers and office staff by participants was counted over a four-week period following the educational intervention. The total number was then compared to a pair-matched (non-participant) control group.
Post-intervention Follow-up Phone Interview
A post-intervention follow-up phone survey was conducted four weeks after the educational intervention. During the call, I asked the participant to respond to four questions. The first question was, “How often have you used the patient portal over the past four weeks?” Data generated from this question examined the rate of PHR use. The second question was, “From 1 = strongly disagree to 5 = strongly agree, rate the following statement: I feel comfortable using the patient portal.” Data generated from this question were compared to the pre-intervention answers to identify computer use comfort level differences among participants. The third question was, “From 1 = strongly disagree to 5 = strongly agree, rate the following statement: I will continue to use the patient portal in the future.” Data generated from this question were used to identify participants’ intentions for future PHR use. The final question was the qualitative component of the study; participants were asked, “What are your overall thoughts about the patient portal?” Participant answers were organized by their pattern to identify specific themes.
The results of this quasi-experimental study assessing PHR use and the effectiveness of the educational intervention using an educational intervention group and a control group are discussed below. Reported findings include descriptive information about the participants, participants’ perceptions of their health ratings, and pre- and post-test results for attitudes about computer use related to PHRs. Statistical data addressing each research question are also presented.
Sample description. As shown in Table 1, the sample included 50 adults (17 male and 33 female) ranging in the age from 47 years to 81 years (M = 64.82, SD = 7.78). For analysis purpose, the participants were divided into a middle-aged (40-62 years) adult group and an older (63-85 years) adult group. There were 15 participants (3 male and 12 female) in the middle-aged adult group and 35 participants (14 male and 21 female) in the older adult group.
The sample had a homogeneous ethnic background: there were 46 (92%) white participants, two (4%) Hispanics, one (2%) African American, and one (2%) Asian participant. Among the participants, 30% (n = 15) had a high school education or less, 32% (n = 16) had some college or an associate’s degree, 24% (n = 12) held a bachelor’s degree, and 14% (n = 7) had a graduate or postgraduate degree. The participants in this sample were fairly well educated; there were no significant differences between the two age groups in regards to the level of education. Of the sample population, 42 % (n = 17) reported working full- or part-time, 2% (n = 1) were actively seeking employment, 8% (n = 4) were disabled, and 48% (n = 24) were retired.
There was a significant difference among the age groups with respect to occupational status, χ2 (5, N = 50) = 11.001, p = 0.05. As expected, the middle-aged adults were employed and the older adults were retired. Regarding annual income, seven participants (16%) reported an annual income of less than $30,000; 22 participants (49%) had an income range from $30,000 to $69,999, and 16 participants (36%) had an income greater than $70,000. Five participants did not provide their annual income data.
Health information. Participants were asked to rate their general health and health for their age (poor to excellent) and their satisfaction with their health (not at all satisfied to extremely satisfied) on a 5-point Likert scale. They were also asked to rate the extent to which health conditions got in the way of performing routine activities. In addition, they were asked to rate the extent to which they experienced functional limitations (e.g. lifting, running) and to indicate current chronic conditions.
There were age-related differences for general ratings of health, χ2 (3, N = 50) = 8.58; p = 0.05. Participants of the middle-aged adult group were more likely than the older group participants to rate their health as poor or fair and reported lesser satisfaction with their health. There were also age differences with respect to the type of chronic conditions reported χ2 (2, N = 50) = 7.407; p = 0.05. The older group participants more frequently reported diabetes as a current condition than the middle-aged people. There were no differences with respect to the extent to which health problems affected performance of routine activities or health-related limitations.
Center for Epidemiologic Studies Depression scale (CES-D). The 20-item CES-D scale (Radloff, 1977) has response categories that indicate the frequency of occurrence of each item, and is scored on a 4-point scale ranging from 0 (rarely or none of the time) to 3 (most of the time). The scores of each participant was totaled; the total scores may range from 0 to 60. Data were examined using one way ANOVA. No significant gender differences or age group differences were present. However, when the Spearman’s rank-order correlation was used to determine the relationship of participants’ depression scores and their self-reported PHR use, a small, negative correlation was found. The correlation was statistically significant, rs(50) = -.286, p < 0.05 indicating that participants with higher CES-D scores used the PHR less and participants with a lower CES-D score used the PHR more often.
Attitudes toward computers. All participants completed the Computer Questionnaire 1 (CQ1), a 15-item multidimensional scale assessing five dimensions of attitudes toward computers: comfort (feelings of comfort with computers and their use), efficacy (feelings of competence with computers), interest (the extent to which one is interested in learning about using computers), and utility (the belief that computers are useful). Participants were required to indicate the degree to which they agreed with the 15 statements (e.g. “I feel comfortable with computers”) with a 5-point Likert scale from strongly agree to strongly disagree. A composite score was obtained by summing responses (0-17). Overall, it was found that 60% (n = 30) of the participants had an overall positive attitude toward computers (composite score of 56-75), with low anxiety levels and high levels of confidence, interest, efficacy and utility. None of the respondents reported negative attitudes (composite score 15-35) and 40% (n = 20) had a moderate attitude toward computers.
Age-group and gender differences in computer attitudes were examined with univariate two (gender) by two (age group) ANOVAs. No significant age by gender interactions were found for the computer attitude composite score. Neither the difference between the middle-aged women and the middle-aged men, nor the difference between the older-aged women and older-aged men, were significant for these variables.
Computer experience. Participants who reported computer experience in Computer Questionnaire 1 were asked to respond to Computer Questionnaire 2 (CQ2) that pertained to training, perceptions of experiences with computers (e.g. frustration), and technical support. Participants were required to indicate the degree to which they agreed with the 31 statements (e.g. “I am usually curious to use the latest version computer software”) with a 5-point Likert scale from strongly agree to strongly disagree. A breath-of-computer-experience variable was computed by summing responses to all 31 items. Overall, there was a wide variety of computer experience within the sample population. Men reported being more comfortable using new applications and software as well as taking advantage of computer training.
Study aim I. The purpose of study aim I was to assess the computer-use attitudes among adult primary care patients (participants) who have a chronic condition in Lake County, Florida, before and after a PHR educational intervention. Overall, the majority (60%) of the participants reported low levels of anxiety, and high level of confidence, efficacy, utility and interest. Some (40%) had moderate levels of anxiety, confidence, efficacy, and interest. No one reported high levels of anxiety, low confidence, efficacy, and interest. Univariate ANOVA testing indicated no difference between the middle-aged women and the middle-aged men and no difference between the older-aged women and older-aged men.
The Wilcoxon Signed Ranks Test was used to identify differences when subjects have been monitored on two different occasions (Kim & Mallory, 2013). This nonparametric alternative to the paired sample t-test identified whether the educational intervention had an effect on the participants’ computer use comfort level. The Wilcoxon Signed Ranks Test indicated that the computer use comfort level was statistically significantly higher four weeks after the PHR educational intervention Z = -1.668, p < 0.005 (one-tailed).
Study aim II. The purpose of study aim II was to examine the rate of PHR use by participants within a four-week time period of the educational intervention. Of the overall participant group (N = 50), fewer than half of the participants 48% (N = 24; 14 women, 10 men; M = 1.08; SD = 1.95) chose to use the PHR as a communication tool after the educational intervention and sent a total of 54 PHR messages to their providers (see Table 2). There was no significant difference between gender and middle-aged and older adults.
Forty participants answered the follow-up survey (80% response rate). Of those 18, (45%) used the PHR one or two times within four weeks after the educational intervention, 15 (37.5%) used the PHR three or four times, and seven (17.5%) used the PHR five to seven times. There was no statistically significant difference between the middle-aged adult and older adult age groups. The follow-up survey indicated that 80% (n = 32) confirmed intentions of future PHR use, 12.5% (n = 5) were unsure, and 7.5% (n = 3) declined future PHR use for reasons such as privacy and security concerns, content not being current, and not being a good match with their lifestyle.
Study aim III. The purpose of study aim III was to evaluate the effectiveness of the educational intervention in improving PHR adoption among the study participants in comparison with a matched set. Participants who attended the educational intervention sent a total of 54 PHR messages to their provider compared with 12 by the non-participant group (see Table 3).
The Mann-Whitney U test was used to compare two independent samples and answered the question: was the PHR use, measured by the number of messages sent, higher for the intervention group than for the matched control group? The test results indicated that the amount of PHR use differed significantly in the participant group that received the educational intervention (M = 1.08) compared to the matched control group (M = 0.16), U = 735.5, p = 0.001.
Study aim IV. The purpose of study aim IV was to identify individual participants’ thoughts about the PHR. A word-based technique was used for a qualitative analysis of the follow-up survey question: “What are your overall thoughts about the patient portal?” Word repetitions indicated that certain ideas were important and thus indicated recurring themes. The following major themes were identified: information availability, training, PHR application usability, provider connectivity, privacy, and security concerns (see Table 4).
The purpose of this study was to assess the computer-use attitudes among adult primary care patients with a chronic condition. The goal was to examine the rate of portal PHR use of middle-aged and older adults, to evaluate the effectiveness of an educational intervention in improving the rate of PHR use, and to identify patients’ thoughts about the PHR.
Despite the availability of PHRs for more than 10 years, less than 10% of US healthcare consumers actually use the PHR to monitor their health information and to communicate with their primary healthcare providers (PCPs) (Markle Foundation, 2011). PCPs make PHRs available to their patients; however, offering the PHR does not ensure successful use by patients (Krist et al., 2014). Studies indicate that PHR use and potential adoption is more likely if the healthcare provider offers a structured program with hands-on training as well as assistance with interpretation of medical information (Noblin, Wan, & Fottler, 2012).
This study examined the effect of an educational intervention on PHR use by adult patients with chronic disease and explored participants’ opinions about PHRs in a follow-up survey. I found that patients were more likely to use the PHR following the educational intervention, compared to patients in the non-participant control group. Moreover, participants’ computer use comfort level increased significantly four weeks after the PHR educational intervention. The qualitative component of the study indicated that patients are willing to use the PHR if their laboratory results are up-to-date and available for review.
As in virtually any empirical research, this study has several limitations including small sample size, single geographic region, and a lack of sample diversity. First, the study was relatively small, with only 50 participants. Second, the setting was restricted to a single geographic region. This researcher recommends replication in other settings to broaden generalizability. Third, the sample lacked a diverse ethnic background. The participant group was rather homogenous and dominated by mostly white, educated participants. Nevertheless, the demographic sample combination mirrors the overall PCGP patient demographic. The PCGP is a well-established practice within the local community; it is likely that the exclusion of Medicaid and the predominant use of commercial insurance plans and Medicare may have contributed to a lack of a more diverse and potentially underserved patient population.
Hands-on PHR Training: Increased PHR Use and Elevated Comfort Level
Earlier studies mostly used methodologies that observed and explored individual attributes related to older patient PHR task performance (Czaja et al., 2006a; Taha et al., 2013) and identified factors that influence PHR adoption (Logue & Effken, 2012; Tenforde et al., 2011). This study was designed as a nonrandomized quasi-experiment, using a pre-post intervention study design with a pair matched control group in an attempt to understand cause and effect of education on PHR use.
There was a clear-cut effect as a result of the educational intervention: the participant group learned to use the PHR as a communication tool and felt overall more comfortable using the computer. In short, the significant difference between the educational intervention group and non-participants confirms the positive effect of the educational intervention on using the PHR and overall comfort level using computers. These findings correspond to results of most studies that used, to some extent, similar interventions (Cody, Dunn, Hoppin, & Wendt, 1999; Czaja et al., 2013; Mori & Harada, 2010; Shapira, Barak, & Gal, 2007; Wolfson, Cavanagh, & Kraiger, 2013).
This quasi-experimental research is different from other studies for the unique selection of outcome measures. To the best of my knowledge, this is the first study to utilize Logue and Effken’s (2012) PHRAM as an explanatory model to test an educational intervention to maximize PHR adoption.
This study was designed to measure “computer use comfort level” (PHRAM personal factor) and “PHR use” (PHRAM technology factor) after a hands-on educational intervention with chronic disease patients (PHRAM chronic disease factor). The results indicate that these unique factors contribute to patients’ acceptance of technology use to improve their health. These findings support the need for additional studies that develop and test interventions associated with factors identified in PHRAM to maximize facilitators and minimize barriers to PHR adoption.
PHR Use: Of No Value if Information is Not Current
The current study also focused on a qualitative component that evaluated the participants’ response to their overall thoughts about the PHR. The responses indicated that patients are willing to use the PHR if their laboratory (lab) values are current and updated. The participants of this study very much valued their lab results being available for review and actually stated that they “won’t continue to use [the PHR] if the information is not updated” and that “the system is […] of no value if the information is not current.”
It is more than evident that patients want their data. This request is in line with a final rule published by CMS on February 6, 2014, that gives patients a means of direct access to their individual and complete lab reports. The patients’ access to lab test reports relates to an ongoing effort to engage patients in their own care and to be an informed partner with one’s health care providers (U.S. Department of Health and Human Services, n.d.).
Despite the growing emphasis of patient data sharing, making patient lab results available in the PHR has been challenging for PCPs for three reasons. First, most physicians feel an obligation to interpret the data for the patient in person during a patient visit before making the data available in the PHR (Frellick, 2014). Second, some providers do not know how to transform the data within the EHR application from the provider view into the PHR view. For example, the EHR eClinicalWorks requires a three-step process to change the lab results to enable PHR viewing. Based on the study result, the research site Primary Care Group Practice realized the need to train their providers how to make lab values available to the patient in HEALOW, the eClinical Works' PHR, and brought each provider up-to-date on the CMS ruling. Third, providers would like the ability to annotate the lab results with notes to allow them to interpret the report for their patients (Frellick, 2014). However, this feature is not yet available in most PHRs, including HEALOW, the PHR used at this study’s clinical site.
While providers are adjusting to the requirement to release results within four days to meet Meaningful Use requirements (CMS, 2012), one concern that will have to be addressed in the future is patients finding abnormal or sensitive test results. Direct notification of abnormal results through the PHR may lead to patient confusion and anxiety (Giardina, Modi, Parrish, & Singh, 2015). Research to develop standardized clinical best practices and evidence-based strategies are desirable to help patients understand and manage the information they receive in the PHR.
No Age-Related Differences
This study also examined age-related differences by comparing middle-aged and older adults’ use of computers. A large number of human factor studies indicate that older adults have more difficulties than their younger counterparts do in learning computer applications (Charness, 2008; Czaja et al., 2006a; Taha et al., 2013). One of these studies conducted by Taha et al. (2013) found significantly lower levels of overall task performance among older participants compared with the middle-aged participants. Human factor researchers suggest that the difficulties older adults experience are due to diminished perceptual and cognitive abilities related to aging.
Nevertheless, this study failed to show age-related differences among reported factors associated with computer use by the participants. The implications of this finding may be two-fold. First, the hands-on educational intervention format met the unique needs of both age groups of this study, the middle-aged and older adults. The finding is in line with recent studies that suggest that training tailored to the individual learner’s needs may close the computer technology age gap and satisfy the needs of older learners (Barnard, 2013; Czaja, Sharit, Nair, & Lee, 2009). Second, all participants were diagnosed with a chronic condition. Studies indicate that patients with chronic conditions usually have more office visits, laboratory tests, and self-management needs (Agarwal, Anderson, Zarate, & Ward, 2013; Longo, 2005). Krist et al. (2014) found that a chronic condition is a predictor and key factor influencing PHR use. Chronically ill patients seem to be highly motivated to engage with their providers; this unique attribute may lead to overcoming age-related learning barriers as reported by human factor researchers (Charness, 2008; Czaja et al., 2006a; Taha et al., 2013).
An interesting finding that does not involve the effect of the educational intervention was the negative correlation of the CES-D scores related to PHR use. The outcome endorses prior studies (Jones, Siegle, Muelly, Haggerty, & Ghinassi, 2010; Kizilbash, Vanderploeg, & Curtiss, 2002) that indicate that depressive symptoms may interfere with learning new tasks such as using the PHR. This finding also reiterates the need to exclude participants with cognitive symptoms of depression when measuring the effect of an educational intervention on PHR use. Loss of interest and fatigue would inhibit the participant’s ability to engage fully in an educational activity.
This study demonstrates that an educational intervention will improve PHR use among older chronically ill adult primary care patients. The characteristics of PHR users, as well as the educational intervention format, may represent an important context for further research. PHRs support self-management and represent a way to engage patients. However, the PHR will continue to be underused if data are not current, not made available, or withheld from the patient. Efforts to promote PHR use and adoption should include provider training, vender collaboration, and patient education.
Advameg. (2012). Clermont, Florida. Retrieved from http://www.city-data.com/city/Clermont-Florida.html
Agarwal, R., Anderson, C., Zarate, J., & Ward, C. (2013). If we offer it, will they accept? Factors affecting patient use intentions of personal health records and secure messaging. Journal of Medical Internet Research: JMIR, 15(2), e43. doi: 10.2196/jmir.2243
Archer, N., Fevrier-Thomas, U., Lokker, C., McKibbon, K. A., & Straus, S. E. (2011). Personal health records: A scoping review. Journal of the American Medical Informatics Association, 18(4), 515-522. doi: 10.1136/amiajnl-2011-000105
Barnard, Y., Bradley, M. D., Hodgson, F., & Lloyd, A. D. (2013). Learning to use new technologies by older adults: Perceived difficulties, experimentation behaviour and usability. Computers in Human Behaviour, 29(4), 1715-1724. doi: 10.1016/j.chb.2013.02.006
Bauer, U. E., Briss, P. A., Goodman, R. A., & Bowman, B. A. (2014). Prevention of chronic disease in the 21st century: Elimination of the leading preventable causes of premature death and disability in the USA. Lancet, 384(9937), 45-52. doi: 10.1016/S0140-6736(14)60648-6
Baumann, L. C., & Dang, T. T. (2012). Helping patients with chronic conditions overcome barriers to self-care. Nurse Practitioner, 37(3), 32-39. doi: 10.1097/01.NPR.0000411104.12617.64
Butler, J. M., Carter, M., Hayden, C., Gibson, B., Weir, C., Snow, L., . . . Samore, M. (2013). Understanding adoption of a personal health record in rural health care clinics: Revealing barriers and facilitators of adoption including attributions about potential patient portal users and self-reported characteristics of early adopting users. AMIA Annual Symposium Proceedings, 2013, 152-161.
Centers for Disease Control and Prevention. (2014). Chronic disease prevention and health promotion. Retrieved from http://www.cdc.gov/chronicdisease/overview/index.htm
Centers for Medicare & Medicaid Services. (2010). Medicare & Medicaid EHR incentive program. Meaningful use Stage 1 requirements overview 2010. Retrieved from https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/downloads/MU_Stage1_ReqOverview.pdf
Centers for Medicare & Medicaid Services. (2012). EHR incentive Programs: Stage 2. Retrieved from https://www.cms.gov/regulations-and-guidance/legislation/ehrincentiveprograms/downloads/stage2_meaningfulusespecsheet_tablecontents_eps.pdf
Centers for Medicare & Medicaid Services. (2013). CMS quality strategy 2013-beyond. Retrieved from http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf
Charness, N. (2008). Aging and human performance. Human Factors, 50(3), 548-555. doi: 10.1518/001872008X312161
Cody, M. J., Dunn, D., Hoppin, S., & Wendt, P. (1999). Silver surfer: Training and evaluating Internet use among older adult learners. Communication Education, 48, 269-286. doi: 10.1080/03634529909379178
Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006a). Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychology of Aging, 21(2), 333-352. doi: 10.1037/0882-79188.8.131.523
Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006b). Demographic and background questionnaire (CREATE-2006-2). Center for Research and Education on Aging and Technology Enhancement. Retrieved from http://create-center.gatech.edu/publications_db/report%202%20ver1.3.pdf
Czaja, S. J., Sharit, J., Lee, C. C., Nair, S. N., Hernandez, M. A., Arana, N., & Fu, S. H. (2013). Factors influencing use of an e-health website in a community sample of older adults. Journal of the American Medical Informatics Association, 20(2), 277-284. doi: 10.1136/amiajnl-2012-000876
Czaja, S. J., Sharit, J., Nair, S. N., & Lee, C. C. (2009). Older adults and Internet health information seeking. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 53(2), 126-130. doi: 10.1177/154193120905300204
Daniel, J., Deering, M. J., & Murray, M. S. (2014). Issue brief: Using health IT to put the person at the center of their health and care by 2020. Robert Wood Johnson Foundation. Retrieved from http://www.healthit.gov/sites/default/files/person_at_thecenterissuebrief.pdf
eClinicalWorks. (2015). Patient engagement: Healow. Retrieved from https://www.eclinicalworks.com/products-services/patient-engagement/
Frellick, M. (2014). New ruling means patients can access their own lab results. Medscape Multispecialty. Retrieved from http://www.medscape.com/viewarticle/820183
Giardina, T., Modi, V., Parrish, D., & Singh, H. (2015). The patient portal and abnormal test results: An exploratory study of patient experiences. Patient Experience Journal., 2(1), article 20. Retrieved from http://pxjournal.org/journal/vol2/iss1/20/
Griskewicz, M. (2014). Technology is improving patients' access to their health information. Agency for Healthcare Research and Quality. Innovations Exchange. Retrieved from https://innovations.ahrq.gov/perspectives/technology-improving-patients-access-their-health-information
Harris, A. D., McGregor, J. C., Perencevich, E. N., Furuno, J. P., Zhu, J., Peterson, D. E., & Finkelstein, J. (2006). The use and interpretation of quasi-experimental studies in medical informatics. Journal of the American Medical Informatics Association, 13(1), 16-23. doi: 10.1197/jamia.M1749
Higgins, R., Murphy, B., Worcester, M., & Daffey, A. (2012). Supporting chronic disease self-management: translating policies and principles into clinical practice. Australian Journal of Primary Health, 18(1), 80-87. doi: 10.1071/PY11006
Jones, N. P., Siegle, G. J., Muelly, E. R., Haggerty, A., & Ghinassi, F. (2010). Poor performance on cognitive tasks in depression: Doing too much or not enough? Cognitive, Affective, & Behavioral Neuroscience, 10(1), 129-140. doi: 10.3758/CABN.10.1.129
Kerns, J. W., Krist, A. H., Longo, D. R., Kuzel, A. J., & Woolf, S. H. (2013). How patients want to engage with their personal health record: A qualitative study. BMJ Open, 3(7). doi: 10.1136/bmjopen-2013-002931
Kim, M. & Mallory, C. (2013). Statistics for Evidence-Based Practice in Nursing. Burlington, Mass.: Jones & Bartlett Learning
Kizilbash, A. H., Vanderploeg, R. D., & Curtiss, G. (2002). The effects of depression and anxiety on memory performance. Archives of Clinical Neuropsychology: Oxford Journals, 17(1), 57-67. doi: 10.1093/arclin/17.1.57
Krist, A. H., & Woolf, S. H. (2011). A vision for patient-centered health information systems. Journal of the American Medical Association, 305(3), 300-301. doi: 10.1001/jama.2010.2011
Krist, A. H., Woolf, S. H., Bello, G. A., Sabo, R. T., Longo, D. R., Kashiri, P., . . . Cohn, J. (2014). Engaging primary care patients to use a patient-centered personal health record. Annals of Family Medicine, 12(5), 418-426. doi: 10.1370/afm.1691
Logue, M. D., & Effken, J. A. (2012). Modeling factors that influence personal health records adoption. Computers, Informatics, Nursing, 30(7), 354-362. doi: 10.1097/NXN.0b013e3182510717
Longo, D. R. (2005). Understanding health information, communication, and information seeking of patients and consumers: a comprehensive and integrated model. Health Expect, 8(3), 189-194. doi: 10.1111/j.1369-7625.2005.00339.x
Markle Foundation. (2011). Doctors and patients overwhelmingly agree on health IT priorities to improve patient care. Retrieved from http://www.markle.org/about-markle/media-release/doctors-and-patients-overwhelmingly-agree-health-it-priorities-improve
Melchior, M. A., Seff, L. R., Albatineh, A. N., McCoy, H. V., Page, T. F., & Palmer, R. C. (2014). Intermediate outcomes of chronic disease self-management program offered by members of the Healthy Aging Regional Collaborative in south Florida. Research on Aging, 36(4), 431-449. doi: 10.1177/0164027513500054
Moran, K. J., Burson, R., & Conrad, D. (2014). The doctor of nursing practice scholarly project: A framework for success. Burlington, Mass.: Jones & Bartlett Learning.
Mori, K., & Harada, E. T. (2010). Is learning a family matter? Experimental study of influence of social environmnet on learning by older adults in the use of mobile phones. Japanese Psychological Research, 52(3), 244-255. doi: 10.1111/j.1468-5884.2010.00434.x
Noblin, A. M., Wan, T. T., & Fottler, M. (2012). The impact of health literacy on a patient's decision to adopt a personal health record. Perspectives in Health Information Management, 9, 1-13. http://perspectives.ahima.org/the-impact-of-health-literacy-on-a-patients-decision-to-adopt-a-personal-health-record/#.VkP4xL-VktE
Nolte, S., & Osborne, R. H. (2013). A systematic review of outcomes of chronic disease self-management interventions. Quality of Life Research, 22(7), 1805-1816. doi: 10.1007/s11136-012-0302-8
Office of the National Coordinator for Health Information Technology. (2015a). What is a patient portal? The basics. Retrieved from https://www.healthit.gov/providers-professionals/faqs/what-patient-portal
Office of the National Coordinator for Health Information Technology. (2015b). What are benefits of personal health records? Retrieved from https://www.healthit.gov/providers-professionals/faqs/what-are-benefits-personal-health-records
Office of the National Coordinator for Health Information Technology. (n.d.). Health IT regulations: Meaningful use regulations. Retrieved from http://www.healthit.gov/policy-researchers-implementers/meaningful-use-regulations
Polit, D. F., & Beck, C. T. (2012). Nursing research: Generating and assessing evidence for nursing practice (9th. ed.). Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.
Radloff, L. S. (1977). The CES-D scale. A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385-401. doi: 10.1177/014662167700100306
Registered Nurses' Association of Ontario. (2010). Strategies to support self-management in chronic conditions: Collaboration with clients. Retrieved from http://rnao.ca/sites/rnao-ca/files/Strategies_to_Support_Self-Management_in_Chronic_Conditions_-_Collaboration_with_Clients.pdf
Ricciardi, L., Mostashari, F., Murphy, J., Daniel, J. G., & Siminerio, E. P. (2013). A national action plan to support consumer engagement via e-health. Health Affairs (Millwood), 32(2), 376-384. doi: 10.1377/hlthaff.2012.1216
Robert Wood Johnson Foundation. (2014). Empowering patients to better manage their health results in improved health and health care. Retrieved from http://www.rwjf.org/en/library/articles-and-news/2014/03/empowering-patients-to-better-manage-their-health-results-in-imp.htm
Sands, D. Z., & Wald, J. S. (2014). Transforming health care delivery through consumer engagement, health data transparency, and patient-generated health information. Yearb Med Inform,9, 170-176. doi: 10.15265/IY-2014-0017
Shade, S. B., Steward, W. T., Koester, K. A., Chakravarty, D., & Myers, J. J. (2015). Health information technology interventions enhance care completion, engagement in HIV care and treatment, and viral suppression among HIV-infected patients in publicly funded settings. Journal of the American Medical Association, 22(e1), e104-111. doi: 10.1136/amiajnl-2013-002623
Shapira, N., Barak, A., & Gal, I. (2007). Promoting older adults' well-being through Internet training and use. Aging & Mental Health, 11(5), 477-484. doi: 10.1080/13607860601086546
Sharit, J., Hernandez, M. A., Czaja, S. J., & Pirolli, P. (2008). Investigating the roles of knowledge and cognitive abilities in older adult information seeking on the web. Journal of Transactions on Computer-Human Interaction, 15(1), 3. doi: 10.1145/1352782.1352785
Taha, J., Czaja, S. J., Sharit, J., & Morrow, D. G. (2013). Factors affecting usage of a personal health record (PHR) to manage health. Psychology & Aging, 28(4), 1124-1139. doi: 10.1037/a0033911
Tenforde, M., Jain, A., & Hickner, J. (2011). The value of personal health records for chronic disease management: What do we know? Family Medicine, 43(5), 351-354.
U.S. Department of Health and Human Services. (n.d.). Health information privacy. HHS strengthens patients' right to access lab test reports. Retrieved from http://www.hhs.gov/ocr/privacy/hipaa/understanding/special/CLIA/index.html
Wolfson, N. E., Cavanagh, T. M., & Kraiger, K. (2013). Older adults and technology-based instruction: Optimizing learning outcomes and transfer. Academy of Management Learning & Education, 13(1), 26-44. doi: 10.5465/amle.2012.0056
World Health Organization. (2011). Global status report on noncommunicable diseases 2010. Retrieved from http://apps.who.int/iris/bitstream/10665/44579/1/9789240686458_eng.pdf
Imke Casey, DNP, CRNA, RHIT
Dr. Casey is a health information technology instructor at Lake-Sumter State College in Clermont, Florida. Dr. Casey obtained a Master of Nursing in the clinical specialty nurse anesthesia in 1999 and a Doctor of Nursing Practice with an emphasis in nursing informatics in 2015. She has been a clinical consultant mostly leading process improvement activities related to clinical workflow, clinical information systems, clinical software, and electronic medical records.