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It has been suggested that providing patients with access to their medical records and secure messaging with health care professionals improves health outcomes in chronic care by encouraging and activating patients to manage their own condition.
The aim was to evaluate the effect of access to a patient portal on patient activation among chronically ill patients. Further, the relationship between temporal proximity of a severe diagnosis and patient activation were assessed.
A total of 876 chronically ill patients from public primary care were allocated to either an intervention group receiving immediate access to a patient portal that included their medical records, care plan, and secure messaging with a care team, or to a control group receiving usual care. Patient Activation Measure (PAM) at baseline and at 6-month follow-up was obtained from 80 patients in the intervention group and 57 patients in the control group; thus, a total of 137 patients were included in the final analysis.
No significant effect of access to patient portal on patient activation was detected in this study (
Time since last severe diagnosis and patient activation at baseline may affect changes in patient activation, suggesting that these should be considered in evaluation of activating chronic care interventions and in the specification of possible target groups for these interventions. This may be relevant in designing services for a heterogeneous group of patients with a distinct medical history and level of activation.
Approximately 40% of the population in Europe and the United States suffer from at least 1 chronic disease, and this number is expected to grow [
A growing body of research shows that sharing information regarding the state and goals of care and improving access to communication with a health care professional can strengthen a patient’s active role in the management of their own condition [
We address 2 essential factors that may promote or dilute the effect of self-management interventions; namely, the level of a patient’s activation when entering an intervention and the temporal proximity of a diagnosis. Patient activation may have an impact on self-management intervention outcomes, especially when the intervention requires some level of patient participation. Temporal proximity of a diagnosis is related to a patient’s perception of their health and the consequential interest in managing their health. The health belief model by Rosenstock and colleagues [
This paper describes the results of a controlled before-and-after study in which the effect on patient activation of a simple patient portal with access to personal clinical information and electronic messaging with clinicians was examined. In addition, we assessed the effects of patient activation at baseline and time since severe diagnosis on change in patient activation. The study was conducted among the chronically ill patients in public primary care in a medium-sized town in Finland (approximately 68,000 citizens). Because it has been suggested that the benefits of a patient portal apply to all regular primary care customers, we did not restrict study participation on the basis of specific diagnoses, but instead based it on a professional’s perception of the chronic, but treatable, nature of a patient’s condition.
This was a controlled before-and-after study conducted in Finnish public primary care. Patients visiting 1 of the 10 health centers in the town of Hämeenlinna during the recruitment phase from October 2011 to March 2012 were considered potential study participants. To study the impact of a patient portal among those most likely to become users of such a service in the future, the following eligibility criteria were applied: (1) age at least 18 years, (2) at least 2 treatable health conditions assessed by a health professional, (3) bank identifiers (electronic credentials for online authentication provided by their bank) and access to the Internet, (4) willing and able, both according to themselves and to a health care professional, to engage in using the portal.
The eligible patients were approached during their visits to primary health care facilities. The nurses and doctors were advised to consider each patient as a potential participant. Once a patient was found eligible, invited to participate, and showed interest in taking part in the study, they were allocated either to the intervention group or the control group on the basis of their date of birth. Patients born on odd dates were assigned to the intervention group and patients born on even dates were assigned to the control group. The intervention group received immediate access to the patient portal and participants in the control group were to receive delayed portal access after 6 months. Ethical approval was granted by the ethical board of the local authority (Pirkanmaa Hospital District). Patients who returned the informed consent to participate were included in the study, whereas patients who did not return the informed consent were considered to have declined to participate (
Once a patient enrolled in the study, they formed a care plan together with a health care professional. The plan was personally tailored for each patient to holistically care for their health and to involve them in the planning of their own health care. Although a care plan was created for all study participants, only the patients in the intervention group were given online access to their care plan through the portal. Patients in the control group received a printed copy of their plan. Other features of the patient portal were access to (1) customer’s own patient records provided and maintained by the health care provider with diagnoses of chronic illnesses and permanent medication prescriptions (
Patient flow.
Screenshot of the patient portal.
Patient activation was studied through the short form of Patient Activation Measure (PAM13) created by Hibbard and colleagues [
The PAM13 instrument consists of 13 statements, such as “When all is said and done, I am the person who is responsible for taking care of my health” (see
Because a Finnish translation of PAM13 has not been used in previous studies, the translation was conducted in collaboration with an expert panel of 3 researchers with expertise in health service research. An independent Finnish translator first translated the questionnaire to Finnish, after which each member of the expert panel made their translations of the instrument. Discrepancies were discussed and a single translation of the PAM13 was agreed upon.
Diagnoses of the participants from 5 years before the intervention were gathered from the electronic patient records to examine the temporal proximity of a diagnosis. Because the effect of diagnosis on patient activation is assumed to depend on diagnosis severity [
Independent sample
To examine the main effect of (1) patient activation level at baseline and (2) severe diagnosis proximity on the change in activation score, we used post hoc tests for group comparisons. In the post hoc tests, we employed the Tukey honestly significant difference (HSD) method to compare the change in patient activation between groups with different times since severe diagnosis (0-1 year, 1-2 years, over 2 years, severe diagnosis during the intervention, and no severe diagnoses), and between groups with different levels of patient activation at baseline (1-2, 3, and 4).
To test the moderating effect of (1) patient activation level at baseline and (2) severe diagnosis proximity on intervention outcome, we used linear regression modeling. Estimates (linear predictions) for changes in patient activation are presented for each category of the moderating variables.
To verify the reliability of the translated Finnish PAM13 instrument, we analyzed item response rate, internal consistency (Cronbach alpha), and item-rest correlations at both baseline and follow-up. All statistical analyses were performed using Stata version 13 (StataCorp LP, College Station, TX, USA). We used a CHARLSON Stata module by Stagg [
A total of 24,818 unique patients visited the health care facilities during the recruitment phase and could be assessed for eligibility. Of the assessed patients, 863 met the inclusion criteria and were allocated to intervention and control groups. In the end, informed consent and responses to baseline and follow-up questionnaires were obtained from 80 patients in the intervention group and 57 patients in the control group; thus, a total of 137 patients were included in the final analysis (
None of the differences in patients’ baseline characteristics were statistically significant. There were slightly fewer women in the control group (45.6%, 26/57) than in the intervention group (56.3%, 45/80). More patients in the intervention group had a CCI of zero (52.5%, 42/80) than in the control group (47.4%, 27/57); accordingly, a greater number of patients in the control group (21.1%, 12/57) had a CCI of 2 than patients in the intervention group (15.0%, 12/80). In addition, more patients in the control group had diagnosed hypertension (36.8%, 21/57) than patients in the intervention group (27.5%, 22/80). The mean age and the baseline score for mental health were similar in both groups as were the proportions of patients with diabetes and hypercholesterolemia.
Baseline characteristics of study participants (N=137).
Characteristic | Portal access (n=80) | Control (n=57) |
|
χ2( |
|
|
Age (years), mean (SD) | 61 (9) | 63 (10) | –0.8 |
|
.40 | |
Female, n (%) | 45 (56.2) | 26 (45.6) |
|
1.5 (1) | .22 | |
|
|
|
|
|
|
|
|
Type 1 or 2 diabetesa,b | 32 (40.0) | 22 (38.6) |
|
0.0 (1) | .87 |
|
Hypertensiona,c | 22 (27.5) | 21 (36.8) |
|
1.3 (1) | .25 |
|
Hypercholesterolemiaa,d | 37 (46.3) | 24 (42.1) |
|
0.2 (1) | .63 |
|
|
|
|
0.9 (2) | .64 | |
|
0 | 42 (52.5) | 27 (47.4) |
|
|
|
|
1 | 26 (32.5) | 18 (31.6) |
|
|
|
|
2 | 12 (15.0) | 12 (21.1) |
|
|
|
aFrom before the beginning of the intervention.
b ICD10 codes E10-E14 or ICPC codes T89-T90.
c ICD10 codes I10-I15 or ICPC codes K85-K87.
d ICD10 codes E78 or ICPC T93.
To verify the psychometric properties of the translated instrument, internal consistency and item-rest correlations were examined at both baseline and follow-up (
The item response was high, with at most 0.7% (1/137) missing values at baseline and 1.5% (2/137) at follow-up. Question 12 was scored as “not applicable” by 9.5% (13/137) of the participants at baseline and by 12.4% (17/137) at follow-up. The overall mean PAM score in this Finnish sample was 63.59 (SD 15.00) at baseline and 63.55 (SD 14.80) at follow-up, and these are similar to the Danish (64.2) [
Internal consistency was assessed as the Cronbach alpha for the sum scale, which was .87 at baseline and .86 for the follow-up sample. These are similar to the Danish (.89) [
Item-rest correlation per item to the sum scale was .32 to .73 at baseline and .33 to .70 at follow-up. For several items, these values were only moderate (≤.50), which indicates that they may not be absolutely true to 1 dimension.
The view to patient’s own health information containing diagnoses, medication prescriptions, and laboratory results was the starting page encountered by the patient once they logged in to the portal. On average, this information was viewed 10.8 times per patient during the 6-month study period. The second most popular feature of the portal, used 3.2 times on average, was viewing one’s personal care plan. Patients sent 1.5 messages to their care team and viewed their vaccination record 1.3 times on average. Only 0.3 prescription renewals, on average, were made through the portal during first year after access (
Mean use of patient portal functionalities per patient in the intervention group (n=80) during the 6-month study period.
Functionality | Mean (SD) | Range |
Viewing personal health record | 10.8 (9.8) | 1-43 |
Viewing personal care plan | 3.2 (3.2) | 0-16 |
Messages to the care team | 1.5 (2.0) | 0-9 |
Viewing vaccination record | 1.3 (1.5) | 0-7 |
Prescription renewal | 0.3 (0.6) | 0-3 |
In analysis of variance, no significant effect of access to patient portal on patient activation was detected (
The 1-way analysis of variance showed a significant difference in mean change in patient activation score across the 3 groups starting from different levels of patient activation (
Changes in patient activation scores within groups starting at different levels of patient activation (n=137).
No statistically significant interaction effect on change in patient activation was detected between portal access and baseline PAM level (
Intervention and control group estimates for mean change in patient activation (0-100 points) at different baseline levels of patient activation.
The 1-way ANOVA revealed a significant difference in mean change in patient activation scores across the 5 groups with different temporal proximity of a severe diagnosis (
Changes in patient activation scores by time since last severe diagnosis (n=137).
No statistically significant interaction effect on change in patient activation was detected between portal access and baseline activation level (
Group estimates for mean change in patient activation (0-100 points) in groups with different time since last severe diagnosis.
The psychometric assessment of the translated Finnish PAM13 instrument supported the reliability of the measure, and replicated to a great extent the findings from the previous Danish [
No significant effect of access to patient portal on patient activation was detected in this study unlike previous research [
As has been observed in previous studies, the change in patient activation was greater among patients starting at a lower level of activation [
To our knowledge, this is the first study to examine the effect of time since diagnosis on patient activation. In both the intervention and control groups, a greater positive change in patient activation was identified among patients diagnosed with a severe condition during the intervention than among patients whose last severe diagnosis was made more than 2 years ago. This suggests that a severe diagnosis may have an independent immediate effect on patient activation. The intervention, in turn, appears to have made the greatest impact on the group diagnosed with a severe condition up to 1 year before the intervention. Among the few studies addressing the effect of time since diagnosis on care outcomes other than patient activation are those by Karter and colleagues [
The main strengths of this study are the experimental setting with longitudinal design and the use of scientifically validated measures for assessment of patient activation (PAM13) as well as in defining time since diagnosis (CCI).
As in any study, there are also several limitations. Three main limitations are related to the natural experimental setting. First, because the recruitment of the patients was conducted by clinical professionals and the time period for recruitment was limited, the sample size remained modest, reducing the statistical significance of the effects. The second limitation concerns the allocation of the patients in the intervention and the control groups. Although birth date itself is not expected to affect the outcomes of the intervention, the allocation method is deterministic in the sense that the assigned intervention could be predicted before the allocation [
In this study, the participants formed a diagnostically heterogenic group. Because there may be differences in activation and its development in different diagnostic groups, further research is needed to assess the association of different diseases and patient activation. Furthermore, CCI, used in defining time since severe diagnosis, is restricted to a set of typical severe diagnoses; thus, some relevant diagnoses that might affect change in patient activation may possibly have been omitted. Broadening the set of diagnoses may further specify the relationship between time since diagnosis and patient activation.
In this study, we created a Finnish translation of the validated PAM13 to evaluate the benefits of giving patients access to their medical records and secure messaging with health care professionals. Patient activation serves as “an intermediate outcome of care that is measurable and linked with improved [health] outcomes” [
No significant effect of a patient portal on patient activation was detected in this study. This result concerning a simple form of a patient portal differs from previous studies in which more interactive functionalities were included in the portal studied.
In addition to the functionalities offered through a patient portal, the activating effect of the portal is dependent on the characteristics of the patient who uses the portal. In this study, 2 patient-related factors, namely patient activation level at baseline and time since last severe diagnosis, were considered. Both variables were shown to be associated with changes in patient activation. Thus, it is suggested that these are considered in any evaluation of activating chronic care interventions. Further studies on the effect of time since diagnosis may identify sensitivity periods during which patients can benefit the most from specific chronic care self-management interventions. Findings on the factors affecting patient activation may aid in designing effective services for a heterogeneous group of patients with a distinct medical history and level of activation.
Patient portals are complex interventions in the way that their outcomes depend on multiple patient-related factors, such as recontacts with their health care provider during the intervention period, but also on the characteristics of the portal itself, such as the set of functionalities offered through the portal. We encourage further conceptual and empirical research on the mechanisms ignited by different patient portal functionalities and on the contextual factors that may moderate the effect of these mechanisms on patient outcomes.
Patient Activation Measure.
Charlson Comorbidity Index conditions.
Data quality and item-rest correlations of the Finnish 13-item PAM (N = 137) at baseline and 6 months’ follow-up.
Charlson Comorbidity Index
honestly significant difference
Patient Activation Measure
The authors wish to thank Tieto Corporation for collection of the data, and doctors Tuomo Lehtovuori and Osmo Saarelma for their valuable comments on the study setting and on the practical implications of the results. The following professionals working at Hämeenlinnan Terveyspalvelut Public Utility deserve special recognition for their aid in realizing the research setting and the data collection: CEO Risto Mäkinen (MD), Senior Physician and Head of Department Virpi Kröger (MD), Nursing Director Kirsti Helkiö (RN), Mia Haapanen (RN), and Elina Pohja (RN). Finally, the authors are obliged to the nurses and doctors working in Hämeenlinnan Terveyspalvelut who participated in the recruitment of the patients and made this study possible.
The study was partly supported by the SalWe Research Program for IMO (Tekes-the Finnish Funding Agency for Technology and Innovation grant 648/10).
Riippa was responsible for the design of the study, participated to the collection of the data, analyzed and interpreted the data, and drafted the paper. Linna participated in the design of the study and the interpretation of the results, and continuously revised the paper during drafting. Rönkkö participated in the collection of the data and provided insight into the implementation of the intervention and the collection of the data in the target organization.
None declared.