Consulting the Internet before a consultation
A mixed method study investigating the effects of searching the Internet before a consultation on satisfaction with the consultation, recall of information and medication
adherence
By
Remco Sanders BSc. 10277013 Master Thesis
Graduate School of Communication Communication Science (Research Master)
University of Amsterdam Supervisor:
Dr. A.J. Linn June 24, 2016
Many patients search for online health information prior to a consultation with their health care provider. However, how seeking for online health information influences communication during consultation and how this influences patient outcomes is heavily understudied. The current study aimed to gain a better understanding of what gratifications patients seek when looking for online health information (substudy 1), if and how the information that is found online is discussed during the consultation (substudy 2) and what the effects of discussing online health information are on patient outcomes (substudy 3). The study followed patients from their preparation for the consultation until three weeks after the consultation. A mixed method study was conducted by combining qualitative analysis of survey data (N = 160) and video recordings of actual consultations (N = 165) with structural equation modeling. Results showed that more than half of the patients (57.00%) searched for online health information prior to their consultation, mainly to fulfill content gratifications. In about half (46.81%) of the consultations of these Internet informed patients, the online health information was discussed. Providers were mainly reactive in how they engaged in online health information, by repairing the information or warning patients against incorrect information. When online health information was discussed, patients were more satisfied with the consultation. Satisfaction was positively related to recall, but only in patients with whom the information they found online was discussed during consultation. The results of this study show the importance of discussing online health information during consultations. Implications for research and practice are discussed.
Keywords: online health information, consultation, uses and gratification, medication adherence, recall
Nowadays, many patients google before they consult their doctor. In fact, every day more patients search for online health information than having a face to face
consultation with a health care provider (Fox & Rainie, 2002). Reasons for searching the Internet vary from preparing for a consultation to looking for information about a
prescribed treatment after the consultation. (Rice, 2006; Russ, Giveon, Catarivas, & Yaphe, 2011).
Percentages of patients searching for online health information in preparation to their consultation vary between 30% (Abdul-Muhsin, Tyson, Raghu, & Humphreys, 2015; Bastian, 2003) and 70% (Russ et al., 2011), with chronically ill patients showing higher percentages compared to patients with a temporary health complaint (Abdul-Muhsin et al., 2015; Dijkstra, Verbakel, & Mokkink, 2008). The impact of such online activities on patient outcomes remains unclear. Research suggest that the information retrieved online influences the patients’ actions during a consultation with their health care provider (Abdul-Muhsin et al., 2015; Wald, Dube, & Anthony, 2007). For example, health-related searches can empower patients to become more active in their own care, resulting in a more patient-centred consultation (Dowsett et al., 2000; Sillence, Briggs, Harris, & Fishwick, 2007). These active patients fit in nicely with a broader trend in which individuals take a more active role in all aspects of life (Giddens, 1992). Health care providers can take advantage of this by exploring patients’ use of online
information and assisting in the information seeking behaviour (Gerber & Eiser, 2001). This is expected to further improve patient outcomes such as satisfaction with the consultation and recall of information (Wald et al., 2007).
Currently, research has mainly focussed either on patient-provider communication (see path 1 of Figure 1; Ciechanowski, Katon, Russo, & Walker, 2001) or on patients’ online information seeking behaviour (path 2 in Figure 1; Dijkstra et al., 2008; Diviani, van den Putte, Giani, & van Weert, 2015; Diviani, van den Putte, Meppelink, & van Weert, 2016).
consultation and patient outcomes (solid lines, in Figure 1) remains, to our knowledge, heavily understudied (Murray et al., 2003; Russ et al., 2011; Sommerhalder et al., 2009). Therefore, the aim of this study is to advance our understanding of why and how online health information is used prior to the consultation, how this is discussed during consultation and what the impact is
on satisfaction with the consultation, recall of medical information and medication adherence. In achieving this aim, the following research questions will be answered in different substudies:
What types of gratifications do chronically ill patients seek when searching for online health information prior to a consultation? (substudy 1);
How many patients seek online health information prior to their consultation and how many patients and health care providers discuss online information during a consultation, who initiates this discussion and which communication strategy is used? (substudy 2);
What are the effects of discussing the online health information during consultation on satisfaction with the consultation, recall of information and medication adherence? (substudy 3).
The current study bridges the literature gap in the triangle as displayed in Figure 1. The current study will focus on online medication related information seeking
behaviour of chronically ill patients at the start of their treatment, when patients’ information needs are generally high (Abdul-Muhsin et al., 2015). This makes it a perfect time to survey patients’ online health information seeking behaviour in a natural setting, adding to the validity of the results. Specifically, the study will focus on patients diagnosed with Inflammatory Bowel Disease (IBD) at the beginning of severe treatment.
seeking information first by their gastroenterologist (38%) and second on the Internet (36%) (Bernstein et al., 2011). This makes this group highly relevant for the current study. The current study differentiates itself from previous research in this area by analysing patients’ and health care providers’ actual behaviour in addition to survey research. Previous studies are mainly focusing on self-reported, survey data among healthy, convenience samples (Russ et al., 2011). Self-reported measurements are affected by favourable bias and forgetfulness on the part of the patient, especially in health related issues (Brener, Billy, & Grady, 2003). By combining videotapes of actual
consultations and survey data collected over a longer period of time, the current study answers to the call of Chung (2013), who advocates to study the discussion of online information in a more holistic and accurate way. To the best of our knowledge, no previous study investigated the discussion of online information using videotapes of actual consultations.
The current study could be a first step to formulate recommendations on how to effectively handle patients’ online health information seeking during the consultations. Hereby adapting the consultation to the needs of the Internet informed patient.
Theoretical background
Online health information and information needs
The changing paradigm in which patients changed from passive to active
patients, occurred almost simultaneously with the rise of the Internet (Wald et al., 2007). The Internet has brought patients greater direct and easy access to health information, thereby increasing their autonomy in accessing information to satisfy their needs (Ciata-Zufferey & Schultz, 2012). This process is reflected in the percentages of patients using online health information. From a mere 8% to almost 70% a few years later (Budtz & Witt, 2002; Russ et al., 2011). Differences in percentages are not only due to the
highest percentages of online health information seeking behaviour. Between 31.82% and 54.84% searched for online health information prior to their consultation (Abdul-Muhsin et al., 2015; Diaz et al., 2002). When chronically ill patients are confronted with a new and threatening situation, such as the prescription of severe medication,
information needs increase (Bernstein et al., 2011). The Media System Dependency theory (MSD) (Ball-Rokeach & DeFleur, 1976) can explain why these information needs arise and helps us to understand why patients use the Internet as a source of online health information. The MSD assumes that patients actively use media to gratify their information needs (Ball-Rokeach, 1998). To fulfil these needs very detailed medical information is often required. The Internet might be the most appropriate medium to deliver this information since it has the availability to a vast amount of informationthat is readily available (Murray et al., 2003), at any time, against almost no costs (Dijkstra et al., 2008).
Substudy 1 - Gratifications of online health information seeking behaviour In line with the current paradigm of the active patient, the Uses and Gratification theory (UGT) can be used to further understand patients’ information needs, and how patients gratify these needs (Rubin, 2002). Stafford, Stafford, and Schkade (2004) revised the UGT to make it more appropriate for the online setting. They identified three types of gratifications that can be retrieved from online information seeking: content, process and social gratifications. Content gratifications are characterized by terms like gaining information, knowledge and learning. These gratifications involve the actual content carried by the medium. Thus, a primary gratification for patients would entail searching online for information about their treatment. Process gratifications are characterized by terms like resources, search engines, surfing and websites. It entails the “enjoyment” people gain from the actual use of the medium itself. For example, the lay-out and usability of a website are aspects that could increase the enjoyment of the user. Finally, social gratifications are characterized by terms like chatting, online friends
experiences with fellow patients.
To the best of our knowledge, no research is known in which this typology has been used to study gratifications within an online health communication context.
However, there is some research available that could provide support for this typology. As discussed before, many patients go online to seek health information (content gratification; Pautler et al., 2001). If a website is attractively designed a (e.g., containing illustrations) this has been found to increase satisfaction (Bol et al., 2014; Bol et al., 2015). Furthermore, many patients use the Internet for discussing and sharing
experience with fellow patients as well as with health care providers (social gratification; Fox, 2011; Hawn, 2009).
In substudy 1, the aim is to investigate which gratifications patients seek when searching online for medication-related information. In doing so, the following research question is proposed:
RQ1: What types of gratifications do chronically ill patients seek when searching for online health information prior to a consultation?
Substudy 2 - Discussing online health information during a consultation
The growing occurrence of the patient as an active partner in their own care is reflected by an increasing amount of patients seeking additional information beyond their health care provider. Patients who go online usually report a greater understanding and ability to manage their health (Akessson, Saveman, Nilsson, 2007). Despite these benefits, many health care providers are reluctant to discuss online health information with their patients (Abdul-Muhsin et al., 2015). Health care providers often feel
threatened by patients bringing online information to the consultation (McMullan, 2006). Little research has explored if online information is discussed during consultations (Diaz et al., 2002; Fox & Rainie, 2002; Imes, Bylund, Sabee, Routsong, & Sanford, 2008). The few studies on how many patients discuss online health information report mixed
online with their health care provider. The main reason for not discussing online health information is that patients are afraid of harming the patient-provider relationship (Fox & Rainie, 2002), by “stepping on the doctor’s turf’’ (Imes, 2008, p. 543).
Even more scarce are studies on how the discussion of online health information occurs during consultation. Some qualitative research has been conducted that might serve as a guideline for the current study. A typology has been developed on the different communication strategies that health care providers can apply when
discussing online health information (Caiata-Zufferey & Schulz, 2012). This typology consists out of four strategies. The first strategy is resistance. According to this communication strategy, the health care provider ignores, discredits or devalues the online information. When patients talk about online health information, the health care provider discredits the information by qualifying online information as “inaccurate” or simply ignores the remark of the patient (Henwood, Wyatt, Hart, & Smith, 2003). The second strategy is repairing. According to this communication strategy, the health care provider mainly focuses on correcting the online information. For example, the health care provider emphasizes the limits of online information such as not being relevant for the patients’ situation. The third strategy is co-construction. In this strategy, the health care provider evaluates the online information in a patient-centred way and encourages the patient to seek for further information online on their own. In this strategy, the health care provider can be considered as an interpreter of online health information.
Additional research showed that because of the medical training of the health care provider, he or she is able to help the patient to understand the information and adapt this information to the patient’ situation (Ahmad, Hudak, Bercovitz, Hollenberg, & Levinson, 2006). The last strategy is enhancement. In this strategy, the health care provider evaluates the online information, provide the patient with tools to evaluate online health information themselves, suggests new, high quality information sources and suggests the patient to come back with the new information.
seeking behaviour prior to the consultation and also how this information is incorporated into the consultations. To do so, the following research questions are proposed:
RQ2: How many patients seek online health information prior to their consultation and how many patients and health care providers discuss online information during a consultation, who initiates this discussion and which communication strategy is used (substudy 2)?
Substudy 3 - Towards a fully triangulated model
While it is important to gain more insight into patients’ motives to seek online, if and how online health information is discussed during a consultation, the effects of the discussion of online health information on patient outcomes is of upmost importance. Discussion of online health information is a way to identify misunderstandings or misinterpretation resulting from online health information. Leaving these
misinterpretations undiscussed can have negative effects on patients’ outcomes such as satisfaction with the consultation, recall of information, and medication adherence (Imes et al., 2008). Recall is defined as the ability to understand and reproduce medical information (Linn, van Dijk, Smit, Jansen & van Weert, 2003).
Therefore, the aim of substudy three is to test a conceptual model to gain insight into the effects of the use and discussion of online health information on important patient outcomes (satisfaction with the consultation, recall of information and adherence).
The relationship between discussion of online health information and satisfaction Research indicates that patients who search for online health information are more satisfied about the information they receive during consultation, compared to patients who do not search for online health information (Horne & Weinman, 1999; Fogel, Albert, Schnabel, Ditkoff, & Neugut, 2002). However, a recent study found no effect of patients’ online health information seeking behaviour on satisfaction
(Abdul-
the online health information is discussed during the consultation. When patients seek for online health information prior to the consultation, they often expect that this
information is discussed with their health care provider (Diaz, Sciamanna, Evangelou, Stamp, & Ferguson, 2005; Sommerhalder et al., 2009). Not feeling encouraged to discuss the information retrieved online, or not being able to do so, can lead to dissatisfaction because the needs for discussing is not (fully) met (Brandes, Linn, Butow, van Weert, 2015; Turner, Maher, Young, Young, & Hudson, 1996). Especially since information retrieved online is often difficult to understand (Diviani et al., 2016) patients might have the need to discuss this information with their health care provider. While not discussing online health information could lead to detrimental effects, the opposite, discussing online health information leading to positive effects, is also possible. Based on previous studies, the following hypothesis is proposed:
H1: Discussing online health information during consultation has a positive effect on satisfaction with the consultation as compared to not discussing online health
information.
The relationship between the discussion of online health information and recall Besides the effects on satisfaction, the discussion of online health information might also influence the recall of information. Based on McGuire's information
processing theory it is assumed that comprehension or understanding of information will lead to better processing and recall (McGuire, 1968). Indeed, research suggests that a better understanding of (online) health information, for example by discussing it with a health care provider, leads to a higher recall of information (Eysenbach, 2003; Houts, Doak, Doak, & Loscalzo, 2006; Ley, 1982; Ley, 1988).
A review concludes that the use of a preparatory tool can improve recall of information (Brandes et al., 2015).In this review, the preparation was a Question Prompt List (a structured list of questions that patients can use as a form of preparation for their consultation with their health care provider and encourages information
was proactive in addressing the list (Brown, Butow, Dunn, & Tattersall, 2001). This means that, if health care providers actively endorse patients’ preparations this results in higher recall rates compared to health care providers who do not actively endorse the preparatory work (Brandes et al., 2015). Since going online could be considered a form of preparation as well, the same results can be expected. In line with this assumption, research showed that if patients received a printed information leaflet prior a
consultation with a health care provider this resulted in improved recall of information as compared to patients who did not receive this leaflet (Kreuter, Chheda, & Bull, 2000). Patients who seek for online health information and with whom the information is discussed during consultation are exposed twice to the same information. First they encounter the information online and second during the consultation with their health care provider. When exposed to the information the second time, the knowledge gained from the first exposure is reactivated. This might result in a deeper processing of the information and consequently better recall (Calnan & Williams, 1996; Gerber & Eiser, 2001; Masson & Callius, 2001). Based on these previous studies, the following
hypothesis is therefore proposed:
H2: Discussing online health information during consultation has a positive effect on recall, compared to not discussing online health information.
The relationship and interaction between discussing online health information, satisfaction with the consultation and recall of information
Thus, it might be expected that the discussion of online health information results in higher levels of satisfaction and better recall of information. Research on patient-provider communication also suggests that patient satisfaction with a consultation is positively related to improved recall of information (Eysenbach, 2003; Ley, 1988; Ong et al., 2000). Moreover, it can be expected that the relation between satisfaction and recall is stronger when online health information is discussed with the patient, i.e., that the effect of satisfaction with the consultation on recall is moderated by discussing online
2015) did not test the satisfaction-recall relationship, however, satisfaction might have played a role in this effects. Based on the expectation that a higher satisfaction is related to a higher recall, it can be expected that this will be especially the case when the preparatory work of the patients is acknowledged, i.e., discussed during
consultation. As shown earlier, patients put in the effort to prepare themselves by searching for online health information, this effort can be acknowledged by discussing online health information, possibly resulting in an even higher satisfaction. When this is the case, a stronger link between satisfaction and recall might occur, because
discussing the online information and satisfaction might reinforce each other. The following hypothesis is proposed:
H3: a) Satisfaction with the consultation leads to improved level of recall, b) this effect is stronger when online health health information is discussed, i.e., discussing online information during consultation moderates the link between satisfaction with the consultation and recall of information.
The relation recall of information and medication adherence
Correct recall is an important factor in overall health care. A considerable part of the patients who start a medication treatment become non-adherent over time (Sabaté, 2003). Medication non-adherence can have multiple reasons. In all cases the understanding of the information about how to use the medication and recall of this information is an important first step in medication adherence. After all, if patients don’t remember this information, they won’t be able to use the medication according to instructions (Cameron et al., 2010; Kane & Robinson, 2010). However, due to the large amount of information provided during a
consultation, and the high emotional load patients are under, recall can become problematic (Kessels, 2003). Because of this, patients can misunderstand instructions or forget them, leading to poor medication adherence (Cameron et al., 2010; Kane & Robinson, 2010). The relationship between recall and adherence is often discussed (Ley, 1979), but only a few studies actually researched it. The few studies testing this relationship indeed suggest that
Eysenbach, 2003; Linn et al., 2013). Based on these results the following hypothesis is proposed:
H4: Recall has a positive effect on medication adherence.
This leads to the following conceptual model displayed in Figure 2.
Method Study design and procedure
The current study is part of a larger study which aimed to create a theoretical and evidence-based tailored multimedia intervention for IBD patients (Linn et al., 2013). As part of usual care, nurses inform patients about their newly prescribed, severe medication (in this case immunosuppressive and biological therapy). In total, eight IBD nurses from six different hospitals participated in the study. The current study uses the raw data, partly collected by the author himself.
Colitis, b) about to start with one of the following medication; Azathioprine, Methotrexaat, Adalimumab, Infliximab, 6-mercaptopurine, or 6-thioguanine, and finally c) being able to speak and write Dutch. The Medical Ethical Committee of the VU Medical Center,
Amsterdam, The Netherlands, granted permission for this study, which was supplemented with local feasibility statements (Trial No NTR2892). Participating patients were asked for written informed consent. First, prior to the consultation, patients completed a questionnaire containing questions about demographics and medical information including their age, gender, education, diagnosis, time diagnosed and online health information seeking behaviour and gratifications (T0). Second, the consultations were recorded on video (T1). Third, a follow-up survey was conducted (T2). This survey contained questions about satisfaction with the consultation, recall of information and medication adherence which were administered during a telephone interview 3 weeks after consultation.
Figure 3 visualises the design of this study.
Gratifications online health information. Patients were asked at T0 to list their most important gratifications (maximum of eight) when searching for medication-related
information online. The question was as follows: “What are important aspects for you when you chose to search for medication related health information?”. A total of 344 gratifications were written down.
Satisfaction with consultation. To assess the patients’ levels of satisfaction with the consultation, a 29 statement scale was used (Linn et al., 2013), measured at T2. This scale consisted out of three subscales. First, satisfaction with the general information about the disease and treatment (12 items, α = .87). Second, satisfaction with the support with regard to the medication (7 items, α = .67). Third, the level of affective communication (10 items, α = .82). Respondents were asked at T2 to rate statements on a four point scale, ranging for 0 (very good) to 3 (could be much better), M = 1.06; SD = 1.04. An example of a statement is “the nurse treated me with respect”.
Recall of medication information. Delayed recall was measured at T2 using an adapted version of The Netherlands Patient Information Recall Questionnaire (NPIRQ) containing questions that were based on the actual recording of the consultation (Jansen et al., 2008a). Patients were called, partly by the author himself, and asked what they recalled about the topic (for example, “can you describe the purpose of the treatment?”). These answers were coded, again partly by the author himself, with 1 (correct), .5 (half correct) or 0 (incorrect). 19 consultations (13%) were coded by a second coder to calculate intercoder reliability. Cohen’s kappa was used which corrects for chance. Intercoder reliability was high (Kappa = .91). In conformity with previous studies (e.g., Jansen et al., 2008b, van Weert et al., 2011) a percentage of accurate recall was calculated by dividing the sum of accurate items that were recalled by the total number of items questioned (M = .51; SD = .16).
Adherence. Adherence (T2) was measured using one item (Linn, 2003). The question was as follows “how precisely do you take your medication as is prescribed (right
adherence on a ten point scale, ranging from 0 (not at all adherent) till 10 (completely adherent), (M = 8.64; SD = 1.53).
Analysis Substudy 1
To answer the first research question: What types of gratifications do chronically ill patients have when searching for online health information prior to a consultation,
motivations were coded by one coder (RS), in line with the extended UGT (i.e., content gratification, process gratification and interaction) (Stafford et al., 2004). The outcomes of this step were discussed with another researcher (AL). First, patients’ gratifications were alphabetically ordered and given an in vivo code. In vivo codes are codes using literal words of the answers given by the respondent. After that, an iterative process was used. Which means that the in vivo codes were compared with other in vivo codes and merged into a first level code (slightly more abstract codes). The same process was repeated for first level codes to come to second level codes (most abstract, overarching codes). For example, gratification statements of patients indicating they valued “the discussion with other patients’ experience with certain medication” were first coded in vivo coded as “discussion with fellow patients” this was combined with similar in vivo codes in a first level code “discussion (patients)” and in the end as a second level code, using the UGT, as “interaction (other patients)’’ under social gratifications. Within this process the original arguments (N = 344) were reduced to eleven overarching, abstract categories. To prevent loss of detail, first level codes were added as sub categories of the second level codes (see previous example). In the end the three main gratifications consisted out of two to five sub-gratifications.
Substudy 2
To answer the second research question: how many patients seek online health information prior to their consultation and how many patients and health care providers discuss online information during a consultation, who initiates this discussion and
MAXQDA 12 to explore which patient searched for online health information prior to consultation. Consultations were listened to and transcribed. For all transcripts at least the following aspects were coded: 1) Initiator; patient or health care provider. 2) Introduction of online health information; direct (meaning discussing the actual use of online health information to prepare for current consultation) or indirect (discussing general use of online health information or online health information in general not particularly in preparation for this consultation). 3) Valence reaction by health care provider to introduction of online health information; negative, neutral/no reaction or positive. 4) Introduction ignored; yes / no. 5) follow up questions based on introduction asked by health care provider; yes / no. All sections relating the valance were double coded, once by the researcher (RS) and once by a research assistant (MB). The Kappa score was .80, showing a good reliability (Altman, 1991). Differences were discussed until mutual agreement was reached.
Based on the survey and the transcripts, four groups could be identified: 1) not searched / not discussed, 2) not searched / discussed, 3) searched / not discussed, 4) searched/ discussed. Differences between groups in terms of who initiated the
information and which strategy is used by the health care provider, are tested using Fischer exact two-sided test.
Additional analyses were done in group 3 and 4: Consultations of patients who used the Internet before their consultation. Sections were included in the analysis if (any combination of) the words Internet, Google(d), webpages, fora, online or any other Internet-related words were mentioned (N = 60). These sections were then coded with in vivo codes. The transcripts with these in vivo codes were coded using the same iterative process as discussed in substudy 1 but now focussed on identifying strategies as proposed earlier (Caiata-Zufferey & Schulz, 2012). See Appendix 1 for an example of used codes.
enhancement). Sections for resistance were coded when information was ignored (e.g., no reaction to the introduction of online health information), discredited or qualified as useless or harmful. The second strategy repairing was coded when health care providers discussed the limits of online health information, such as saying that the information was not tailored to the patient’ individual situation (e.g., used for different diseases). If the health care provider warned the patient in advance about the limits of online health
information, this was also considered repairing. If the health care provider was not (solely) arguing the limits of online health information but also discussed what the patient found and encouraged the health care patient to search for new online health information, this was considered co-construction. If the health care provider actively referred the patient to online health information sources, provided tools to evaluate online health information and were encouraged to come back with new information, this was considered enhancement. Again all sections relating the strategy were double coded, once by the researcher (RS) and once by a research assistant (MB). The inter-observer was assessed using
Cohen’s kappa which corrects for chance. The Kappa score was .86 showing a good reliability (Altman, 1991). Differences were discussed until mutual agreement was reached.
Substudy 3
Substudy 3 aimed to answer the following research question: What are the effects of discussing the online health information during consultation on satisfaction with the
consultation, recall of information and medication adherence.
First, independent t-test were used to gain insight into the differences between groups (i.e., 1) not searched / not discussed, 2) not searched / discussed, 3) searched / not discussed, 4) searched/ discussed) on the variables satisfaction with consultation, recall of medication information and adherence.
To test H1 till H4 the model was tested using Structural Equation Modelling (SEM; AMOS 23). This method allowed for testing the whole model at once, instead of
cleaned and prepared for analysis in SEM. Normal distribution, linearity, multicollinearity and individual and multivariate kurtosis were checked, in order to meet the criteria for using Maximum Likelihood Estimation (MLE) (Kline, 2011). No problems were found. Within AMOS multivariate outliers were checked but no values of Mahalanobis distance were significant.
Missing values were imputed using regression imputation, since only a maximum of 23 items per variable (13.93%) were missing, the lost in variance by using this
method instead of the stochastic regression is believed not significantly influencing the data. In total 165 observations were taking into the analysis. The model was tested using the two-step approach in which first the measurement and then the structural part were tested. Since the model would also be tested for the subsample of patients that searched for online health information prior to the consultation (n = 97), the model was tested using the derived latent factors from the measurement structure and these were insert as observed variables in the structural part (Kline, 2011). While it would be more in line with the two-step approach to use the measurement part as input for the
structural part, doing this would largely exceed the minimum ratio of 1:10 as discussed by Kline (2011). Using the latent variables directly would only leave eight values to be estimated, thus still satisfying the 1:10 ratio. The measurement model was specified and showed a good fit (χ2(2) = .419, p = .811; CFI = 1.00; RMSEA = 0.00, CI90% [.00, .09]). The constructs showed good discriminative validity, maximum absolute correlation between factors was r = .16.
The following variables were included in the model: discussion of online health information, satisfaction with consultation and the interaction between these two as independent variables and recall of medication information and adherence as dependent variables (see Appendix 2 for correlation and mean matrix):
Satisfaction with consultation. Within the structural equation model, satisfaction with the consultation was inserted in the model using the three sub-scales. While scale 1 loaded
showed lower standardized estimates compared to the standard of Kline (2011). Deleting this items resulted in significant lower model fit and loss of much data, therefore they were included in the sum scale that was created.
Recall of medication information. Within the structural equation model, this observed variable was inserted as a one item latent factor with an assumed error margin of 10% (variance of error term = .3). This margin was chosen by first taking the inter-coder reliability (.9) as starting point and then by exploring whether changing the error margin, and thus the error term variances changed the model fit and estimates significantly, which it did not.
Adherence. Within the structural equation model, this single item scale was inserted as a one item latent factor with an assumed error margin of 10% (variance of error term = 1.11 * .10 = .11). This margin was achieved by taking the assumed measurement error of the variable recall and then exploring whether changing the error margin, and thus the error term variances changed the model fit and estimates significantly which it did not.
Interaction satisfaction and discussion. To test the moderation of the variables satisfaction and discussion on recall, a moderation variable was made by making a new variable (called interaction) consisting of the calculation: satisfaction *discussion (M = .34; SD = .63).
The dataset was ill-scaled (Kline, 2011), recall was therefore transformed. While the factor of ill-scaled variance was reduced from 800 to 26, the ill-scaled variance remained, but this time minimization could be completed without problems. The minimization history showed no errors, with a steady decline across diameter, condition and F without too many tries or negative eigenvalues. No Haywood cases appear.
In total 165 patients participated in the survey. From five patients some
demographics were missing, therefore they were left out of the analysis when this data was relevant. The sample consisted out of 92 women (57.50%), the mean age was 43.10 (SD = 15.33; range = 18 - 84). The majority was diagnosed with Crohn’s disease (n = 101;
63.12%), 49 (30.62%) were diagnosed with Colitis Ulcerosa and 10 respondents (9.09%) were not yet officially diagnosed or diagnosed with both diseases. On average, the respondents were diagnosed for almost twelve years (M = 11.61; SD = 10.55), the range was between 1.5 and 47.1 years. Of the respondents, 22.50% finished a low education, 38.13% a moderate and 39.37% a high education. See Table 1 for all demographics.
Characteristic Patients N = 160 % Gender Female 92 57.50% Age M (SD) 43.10 (15.33) Type of Disease Crohn’s disease 101 63.12% Colitis Ulcerosa 49 30.62% Other 10 9.09% Diagnosed in years M (SD) 11.61(10.55) Range 1.5-47.1 Educational level Low 36 22.50% Moderate 61 38.13% High 63 39.37% Table 1 Demographic Characteristics
Substudy 1 – gratifications Content gratifications
The majority of gratifications for searching for medication related online information were content gratifications (53.78%). Patients reported to seek for information about the medication in general (28.65%) and side effects (33.51% e.g., “how does the medication work?” and “what kind of side-effects, from a combination of medications, can I expect?”). Fewer patients reported on information about treatment (“what does the treatment plan look like, and which steps”) (24.86%), general information about the disease (8.11%) and general information statements, e.g., "looking for new information" (4.84%). Process gratifications
Within the second category process gratifications (39.83%), website characteristics were the main reason for patients to value online health information. Especially tone of voice, e.g., avoiding medical jargon (45.99%) was stressed (e.g., “not too much difficult and technical terms”). The clarity of information, the objective nature of the text and easiness of the text were mentioned as an important gratification (i.e., “clear ABN” [civilized Dutch]). Furthermore, ease of use, e.g., navigation options, the layout and the presence of pictures or photos were stressed (31.39%) (“you should find the search button in one glace”). Fewer patients reported on information about the source and/or the presence of sources of the information (13.89%) and reliable websites/information (8.76%).
Social gratifications
Within the third category, the use of Internet to satisfy social gratifications (6.39%), reading about experiences from others / other patients (68.18%) and Interaction with other patients (31.82%) was most often mention. The interaction was mainly focused around talking about the experiences with medication (e.g., “personal experiences from fellow patients about the medication”). Figure 4 show the results.
Substudy 2
In total, 57.00% (n = 95) patients searched for medication-related online information prior to the consultation, as indicated by the survey at T0. In 46.81% (n = 44) of the consultations with these patients, the online health information was
discussed. In 56.82% (n = 25) of the consultations in which the online health information Figure 4. Online health information gratifications.
consultations, online health information was discussed. See Table 2 for frequencies of patients searching and/or discussing online health information.
Overall, the discussion of online health information was almost evenly initiated by patients (n = 33; 55.00%) and health care providers (n = 27; 45.00%). It was more often introduced directly (n = 46; 76.70%) compared to indirectly (n = 14; 23.30%). An example of a direct introduction is: “did you search on the Internet, can you tell me what you think of what you have found?”, (Nurse 5; Consultation 16). An example of an indirect introduction is: “If you search on the Internet you will often find information for cancer patients” (Nurse 3; Consultation 134). Health care providers were significantly more likely to introduce online health information indirectly compared to patients, and vice versa (p < .001). For an overview of these results see Table 3.
Furthermore, when the health care provider introduced online health information, it was significantly more of
t
en accompanied (44.44%) by a warning against online health information compared to when patients introduced the topic themselves (p = .044). These warnings were either about possible harmful effects of searching for health information (n = 6; 50.00%, e.g., "when you google about it (medication) you will find a lot of horror stories”, Nurse 6; Consultation 120) or because of information that is not applicable for the patients’ situation (n = 6; 50.00%, e.g., “this medicine is often used in cancerN = 165 Searched No Yes Discussed No 53 (32.10%) 51 (30.90%) Yes 17 (10.30%) 44 (26.70%) Table 2
disease”, Nurse 1; Consultation 17).
When looking at how health care providers react upon the introduction of online health information by patients, this was most often done by neutrally responding to the introduction (n = 21; 63.60%), followed by positive (n = 8; 24.20%) and negatively (n = 4; 12.10%)
Communication strategies
Co-construction was the most used strategy during the consultation (n = 22; 36.67%). Within this strategy the health care provider asked about what kind of online health information the patient found, and discussed this to make sure it was clear and did not lead to any miscommunications (“Did you have any questions based on what
N = 165 Discussed No Yes Searched No Group 1 2 How Direct: - - 12 70.59% Indirect: - - 5 29.41% By Patient: - - 8 47.06% Provider: - - 9 52.94% Ye s Group 3 4 How Direct: - - 34 77.27% Indirect: - - 10 22.73% By Patient: - - 25 56.82% Provider: - - 19 43.18% Table 3
most occurring strategy, almost half of the time (40.41%) the health care provider used this strategy to reassure the patient (“On the Internet and on the leaflet you will find every side effect that has happened, also if this happened only once”, Nurse 6, Consultation 74).
The second most used strategy was repairing (n = 19; 31.70%). Repairing mainly focused on countering the believes held after searching for online health
information or correcting the information (e.g., “What you might have seen when looking online is that this medication is also used for cancer treatment. Only in higher doses, so that explains the negative experiences”, Nurse 7, Consultation 46).
The third most used strategy was ignoring (n = 14; 23.30%). Within this strategy the health care providers discredited online health information as a whole, or simply ignored the remark of the patient where he or she indicated to have search online (“Oh the internet, there are only horror stories there”, Nurse3, Consultation 44).
The least used strategy was enhancement (n = 5; 8.33%). Here the health care provider refers the patient to specific (high quality) websites and ask them to come back to them whenever they have questions (“You can take a look at this website
(apotheker.nl), and here is the secretary’ e-mail address in case you have any
questions about what you read online or during a consultation”, Nurse 3, Consultation 47). Findings are summarized in Table 4.
N = 60 n
% Example statements by provider
St
ra
te
g
y
Ignore 14 23.30% Oh the internet, there are only horror stories there (#44)
Repair 19 31.70% What you might have seen when looking online is that this medication is also used for cancer treatment. Only in higher doses, so that explains the negative
experiences (#46).
Co-construction
22 36.67% Did you have any questions based on what you read on the Internet (#49)?
Reassurance 9 40.41%*
On the Internet and on the leaflet you will find every side effect that has happened, also if this happened only once.
(#74).
Enhancement 5 8.33% You can take a look at this website (apotheker.nl), and here is the secretary’ e-mail address in case you have any questions about what you read online or during a consultation (#47).
Total 60 100% * = proportion of co-construction
Substudy 3
Looking at differences between users of online health information and non-users, there were no significant differences between those two groups on satisfaction with consultation and adherence. However, there was a significant difference on recall of information Mnon-users
= .48 (SD = .15), Musers = .53 (SD = .17), Mdiff = .05, p = .007, d = .31, indicating a medium
effect size, meaning that users of online health information had a higher recall compared to non-users. Taking a closer look at the role of discussing online health information, results show that patients who discussed the online health information during consultation (group 4) were more satisfied with the consultation (M = .86; SD = .61) compared to patients who did search but did not discuss this during consultation (group 3; M = 1.30; SD = 1.13), Mdiff=
-.43, p = .041, d = .48 (medium effect). Table 5 gives an overview of these results.
Model testing
The model was only tested for patients who did search online health information on the Internet prior to the consultation (group 3 and 4). All paths were drawn to create a just identified model, next the non-hypothesized paths were tested whether deleting these would significantly decrease model fit using the χ2 difference test. This was not the case; χ2(4) =
1.40, p =.845. Non-significant paths were tested and explored whether deleting them would decrease model fit. This resulted in the following model for online health information seekers (N = 94) (see Figure 5). While χ2, CFI and RMSEA were good, the upper limit of RMSEA
Internet use Group
Overall No Yes 1 2 3 4
Satisfaction* 1.06 .99 1.10 .98 1.09 1.30b .86b
Recall .51 .48a .53a .47 .51 .53 .52
Adherence 9.15 9.14 9.16 9.21 8.86 9.21 9.15
a M
diff = .05, p = .007; b Mdiff(78.99)= .43, p = .029 * lower means higher satisfaction
Table 5
Means per condition on satisfaction with consultation, recall of information and adherence
Hypotheses 1, discussing online health information during consultation has a positive effect on satisfaction with the consultation as compared to not discussing online health information, was confirmed: there was a significant negative effect from discussion of online health information during consultation on consultation satisfaction (β = -.69, p < .001). This means that discussing online health information leads to a higher satisfaction (i.e., lower mean), compared to not discussing it.
Hypothesis 2, discussing online health information during consultation has a positive effect on recall, compared to not discussing online health information, was rejected: there is no significant effect between discussion of online health information and recall of information (β = .12, p = .310). This means that discussing online health information does not leads to higher recall.
Hypothesis 3a, satisfaction with the consultation leads to improved level of recall, was rejected, there was no significant direct effect of satisfaction on recall (β = -.13, p = .270). This means that higher satisfaction does not lead to higher recall of information.
Hypothese 3b, this effect is stronger when online health health information is discussed, i.e., discussing online information during consultation moderates the link between satisfaction with the consultation and recall of information, was confirmed with a marginally significance level: for patients discussing online health information, higher satisfaction with the consultation lead to higher recall, compared to not discussing it (β = .32, p = .054).
Hypothesis 4 was rejected, there was no significant effect of recall on adherence (β = .01, p = .922). This means that higher recall not leads to higher medication adherence.
Looking at the model tested in SEM, there are some (near) equivalent models to be made. There is a recursive block at the beginning in which all paths could be
reversed without changing the model fit. For example, another just as good fitting model could be made in which recall could lead to higher satisfaction in stead of satisfaction
consultation was earlier in time then the recall measurement, theoretically it does not make sense to do so. The outcomes are displayed in Figure 5.
Discussion General conclusion
This study aimed to gain more insight in why and how online health information is used prior to the consultation, how this is discussed during consultation and what the impact is on satisfaction with the consultation, recall of medical information and
medication adherence. Therefore, three substudies are conducted. The results of the first substudy showed that content gratifications (53.78%) and process gratifications (39.83%) underlie medication information seeking behaviour. The results of the second substudy showed that more than half of the patients (57.00%) searched for online health information prior to the consultation. In around half (46.32%) of the consultations of these Internet informed patients, online health information was discussed, initiated evenly by patients and health care providers. While initiation by patients was mainly done in a direct manner, health care providers tended to initiate online health
information in an indirect manner. This trend was also seen in the strategies that were used to deal with online health information. Health care providers responded mostly Figure 5. Hypotheses with significant standardized regression coefficients
introduction of online information. Co-construction is a more active approach to online health information than repairing. However, in this study co-construction was mostly limited to responding to online health information by providing reassurance.
Encouraging patients to seek for more online health information, the active component of co-construction, is scarce in the consultations included in this study. Overall, health care providers hardly used the strategy enhancement. Thus, discussing the information more in-dept, providing evaluation tools, or referring patients to high quality websites, are missing. This is a missed opportunity since substudy three shows that, if patients and health care providers discuss online health information, this results in higher levels of satisfaction with the consultation. Furthermore, satisfaction was positively related to recall, but only with patients who discussed the online information during the consultation. Gratifications of online health information seeking behaviour
As expected by the UGT (Rubin, 2002), a large amount of patients search for online health information prior to the consultation. Patients mostly seek content
gratifications, followed by process gratification. This is in line with the prediction of the MSD: The large amount of detailed medical information on the Internet was reflected in the gratifications patients seek online. While being one of the main innovative features of the Internet, surprisingly little social gratifications were mentioned by the patients. A possible explanation might be the early stage of the treatment these patients were in. It is possible that patients at this stage mostly have the need to “understand” their
disease. This is supported by the large amount of medication specific information that is searched. In later stages of the disease the gratification needs might change from the cognitieve need to ‘know and understand’ to the affective need to “feel understood” (Bensing et al., 2011; Germeni, Bianci, Valcaernghi, & Schultz, 2015). For example, when side effects occur, experiences from other patients might become more important. Thereby using online health information more to satisfy social gratification from patients. This assumption is in line with a literature study on patients’ needs in which a shift from
stages of patients’ treatment and disease was found (Rutten, Arora, Bakos, Aziz,
Rowland, 2005). Overall, the current study finds support for the applicability of the extended UGT typology in the context of online health communication (Stafford et al., 2004). The gratifications can easily be identified and provide a useful approach to systematically identify needs and gratifications when it comes to online health seeking behavior.
Discussing online health information during consultation
The proportion of patients searching for health information prior to the
consultation in this study is in line with previous research among chronically ill patients (Abdul-Muhsin et al., 2015). The results shows that health care providers initiate online health information often in an indirect manner, mainly using a repairing or a partly co-construction strategy. This is in line with previous research (Sommerhalder et al., 2009), were online health information was often discussed to correct wrongful information. However, this study also find some support for a more active approach, i.e.,
co-construction, although this co-construction was still mostly done in a superficial way. It’s definition entails both discussing the online information that was found by the patient as well as suggesting the patient to search more (Caiata-Zufferey & Schulz, 2012). The discussions in this study often entailed only reassurance of the patient, without encouraging the patient to seek for further information online. Enhancement as an active strategy was even less frequently used. A previous study also found that in total 92.7% of the patients expect that the health care provider will provide him/her with disease specific, high quality websites. However, only 3% of health care providers did so (Diaz et al., 2005). In the current study health care providers referred patients to online health information sources in five consultations (8.33%). Although 8,33% is still a small percentage, another way to look at this result is to see it as a three times
multiplication since the study of Diaz et al. (2005). This could be a signal of a (slow) transition from a reactive to a more active approach to online health information. In a recent study evidence for this transition can be found (Abdul-Muhsin et al., 2015).
during consultation, but they were afraid that this could take too much time. Insights of the current study show that paying a little attention (often only a minute) to online health information, especially when patients had searched for it, positively influenced
satisfaction with the consultation and when done right (resulting in a higher satisfaction with the consultation), also recall. Thus showing that it might not have to cost much of the providers’ time to generate positive results.
The current study adds to the literature by applying, for the first time, the communication typology to data from video recordings of consultations. The typology apeared to be a useful way to qualify responses to online health information. However, the strategies in the typology were not as mutually exclusive as suggested in the
original article (Caiata-Zufferey & Schulz, 2012). Especially the strategy co-construction showed overlap with the repairing strategy. According to the original definition, co-construction involves talking about the online health information that patients had found. However, discussing online health information was often done in the form of repairing the online information that was found, thus showing overlap with the repairing strategy. In the current study the subcategory “reassurance” was added to distinguish both strategies. It can be recommended to distinguish more than four strategies, and/or to define the strategies in more detail. For instance, the six strategies resistance, repairing, reassurance, co-construction (with a more limited definition, for instance discussing the content of the online information in a neutral way) and encouragement could be
distinguished. A widely accepted and easy to use typology is important n order to make research over time more consistent and to identify a possible transition into a more active approach.
The fully triangulated model
The importance of discussing online health information is shown in substudy three. This study gives more insight in the effect of online health information and the discussion of it on satisfaction with the consultation, recall of information and
such discussion increased satisfaction with the consultation. Satisfaction was also positively related with recall, but only in patients with whom the online information was discussed during the consultation. This is a first step in completing the triangulation of online health information. And thereby, partly, filling the literature gap. It suggests the assumed effect of double exposure to health information on recall. Active endorsement of a preparatory tool (Question Prompt Sheet) by the health care provider was
previously found to increase recall (Brown et al., 2001). Results of this study indicate that the same might occur when online health information that was found by the patient prior to the consultation is discussed during consultation.
However, the current research did not find the proposed effect of recall of information on medication adherence. One possible explanation could be that adherence was measured with a self-reported measurement, showing high reported adherence. This is limiting the variation that exists within this variable. While previous research show that self-reported intake behaviour is significantly related to automatic more “objective” measurements of medication adherence, such as refill data (Hugen et al., 2002), future research could include more variables in order to create more variation in this measure.
This study combines the discussion of online health information, satisfaction with the consultation, recall of information and medication adherence. The combination, and relations between these variables, can be a start of a conceptual framework, in which both the search for, as well as the discussion of, online health information during consultation is integrated. The proposed framework combines theories explaining why patients search for online health information (i.e., extended UGT), what communication strategies are used (i.e., communication typology) and what the effects are on patient outcomes (i.e., preparatory tools and double exposure). This framework, along with the suggestions made to adapt the included theories, could serve as a starting point for developing the framework. Factors like: what kind of online health information is
information strategy, can be added to the framework. By doing this a full triangulated framework, covering the complete process from preparation till treatment, can be build. Thereby answering to the call for a more holistic view of online health information as proposed by Chung (2013).
Limitations
This study also has some limitations. The introduction of online health
information was coded as “discussed” when online health information, or any synonym, was mentioned. Therefore, the discussion of online health information was treated as a dichotomy. However, the argument can be made that discussion is more like a
continuum, ranging from “no attention” to “a lot of attention” to online health information. To see online health information as a scale would also be a better proxy to the
strategies that can be used by a health care provider. The strategy resistance could be seen as the equivalent of “no attention” while enhancement could be seen as the equivalent of “a lot of attention”. By doing this, more variation will be taken into consideration, which could possibly lead to a more detailed picture. This continuum could be coded by, for example, how many minutes or words, are dedicated to discussing online health information. The effects of the different levels of attention to online health information on patient outcomes can be incorporated into the proposed framework. Within the current study this inclusion was, due to the limited sample size, not an option. Future research could focus on the effects of these strategies on patient outcomes.
This study adds greatly to previous research because of the longitudinal design. However, future research should add measurements at more different time points in order to make stronger causal claims. For example, satisfaction with the consultation could (also) be measured directly after the consultation, instead of after three weeks. Online health information is only a small part in the large and complex setting in which the overall health care is provided. Therefore, the effects of discussing online health
measuring satisfaction both right after the consultation and after three weeks, effects of the discussion of online health information can be measured.
Future research
Theoretically the current study has been among the first to bridge the literature gap between online health information and the health care provider. Up until now, most studies research either the online information seeking behaviour of patients or the patient-provider relationship. However, showing that the discussion of online health information can bring along positive effects to patient outcomes, is an important first step in raising attention to this area of research. Future research can benefit from these first findings and continue the current research. Both experimental as well as non-experimental studies should be conducted. For example, testing the effects of using different strategies when discussing online health information can be investigated in an experiment using role-played consultations. This setting can help isolate the effect of the way online health information is discussed. Thereby, adding to the complexity and explanatory power of the proposed framework.
Future research should also focus on the factors prior to the discussion of online health information (e.g., health information seeking behaviour. We now know that chronically ill patients look for online health information. However, how and what they seek remains largely unknown (Abdul-Muhsin et al., 2015). By identifying how patients search for online health information, and how patients perceive quality online, more insights can be gained. Research shows that if patients encounter higher perceived quality health information, they’re more likely they to discuss this information with their health care provider. Identifying the cues and heuristics patients use to seek and evaluate online health information, can help to design tools to empower patients and guide them to high quality information. In the current study it remains unknown which, and what kind of quality, online health information patients encountered prior to the consultation. Therefore, what role “quality of online health information” plays in the
developments in computer monitoring tools could serve as an objective measurement for patient’ online health information searching behaviour (Moreland, French, &
Cumming, 2015). This is an important aspect since a lot of low quality online health information is available (Eysenbach, Powel, Kuss, & Sa, 2002) and quality of online health information could have a moderating effect on patient outcomes. As high quality online health information is likely to correspond with the information communicated by the health care provider, low quality information could be contradictory. Therefore, low quality online health information can lead to doubts and conflicting information. This could lead to a boomerang effect instead of the positive effects that are expected based on high quality online health information.
Practical implications
The outcomes of this study can be used by both patients and health care
providers. For patients it is important to notice the overall neutral to positive reactions to the introduction of online health information by the health care provider. The results show that only in a few cases the health care provider reacted negatively. Previous research suggested that patients refrain from discussing online health information out of fear of the health care provider’ reaction (Abdul-Muhsin et al., 2015; Henwood et al., 2003). This fear seems not to be justified on grounds of the current findings. Even when health care providers remain hesitant to bring up the topic by themselves, patients should not feel reluctant to bring up online health information. The current study suggests positive effects of online health information seeking behaviour for patients. Searching for online health information and discussing the information during the
consultation leads to a more pleasant consultation (as indicated by satisfaction), as well as to a higher recall for patients who discussed the online health. For providers, this should also be an argument to adopt a more active approach. Being able to provide a better satisfactory consultation by discussing online health information is worth the investment looking at the positive outcomes. By guiding patients during consultation to
repairing the information and more time answering questions patients still might have. A training for health care providers in how to change a reactive approach towards online health information into an active one, is a good way to make a change. The
characteristics of the strategies identified in this study, together with the outcomes of the current study, could be a starting point to create this training. Change is underway, in the recent years more attention is paid to communication skills for medical students (Deveugele, 2005). How to deal with online health information and preferably lessons in different communication approaches that can be applied should be the next step in to the transition to an active approach towards online health information.
Final conclusion
The Internet and online health information are a fundamental tool for the active patient. And they are here to stay. The reactive approach to online health information that is now applied by health care providers should make way for an active approach. Only then the most can be made out of the preparatory efforts patients are making by searching for online health information. Not only will this active approach, assist patients in getting high quality online health information, consultations will become more
satisfying. When insights in how this information is used, are not used, we leave the patient outcomes open to chance.
References
Abdul-Muhsin, H., Tyson, M., Raghu, S., & Humphreys, M. (2015). The informed patient: An analysis of information seeking behavior and surgical outcomes among men with benign prostatic hyperplasia. American Journal of Men’s Health. doi:
10.1177/1557988315617527
Ahmad, F., Hudak, P. L., Bercovitz, K., Hollenberg, E., & Levinson, W. (2006). Are
physicians ready for patients with internet-based health information? Journal of Medical Internet Research, 8 (3), e22.
