| Literature DB >> 31094342 |
Abstract
BACKGROUND: In China, the utilization of medical resources is tense, and most hospitals are highly congested because of the large population and uneven distribution of medical resources. Online health communities (OHCs) play an important role in alleviating hospital congestions, thereby improving the utilization of medical resources and relieving medical resource shortages. OHCs have positive effects on physician-patient relationships and health outcomes. Moreover, as one of the main ways for patients to seek health-related information in OHCs, physician-patient communication may affect patient compliance in various ways. In consideration of the inevitable development of OHCs, although they have several shortcomings, identifying how physician-patient communication can impact patient compliance is important to improve patients' health outcomes through OHCs.Entities:
Keywords: communication; consumer health information; decision making; patient compliance; patient portals; personal autonomy; physician-patient relations
Mesh:
Year: 2019 PMID: 31094342 PMCID: PMC6535977 DOI: 10.2196/12891
Source DB: PubMed Journal: J Med Internet Res ISSN: 1438-8871 Impact factor: 5.428
Figure 1Research model. H1-H6: hypothesis number.
Sample demographics (N=423).
| Demographic characteristics | n (%) | |
| <20 | 19 (4.5) | |
| 20-29 | 127 (30.0) | |
| 30-39 | 124 (29.3) | |
| 40-49 | 97 (22.9) | |
| 50-59 | 49 (11.6) | |
| 60 and above | 7 (1.7) | |
| Male | 203 (48.0) | |
| Female | 220 (52.0) | |
| Urban | 240 (56.7) | |
| Rural | 183 (43.3) | |
| Junior middle school | 22 (5.2) | |
| High school | 60 (14.2) | |
| Junior college | 124 (29.3) | |
| Bachelor’s degree | 159 (37.6) | |
| Master’s degree | 48 (11.3) | |
| Doctorate | 10 (2.4) | |
Cronbach alpha of constructs.
| Constructs | Cronbach alpha |
| Physician-patient communication | .909 |
| Perceived quality of internet health information | .919 |
| Decision-making preference | .749 |
| Physician-patient concordance | .750 |
| Patient compliance | .787 |
Composite reliability and average variance extracted.
| Construct | Composite reliability | Average variance extracted | Square root of average variance extracted |
| Physician-patient communication | .940 | .527 | .726 |
| Perceived quality of internet health information | .947 | .528 | .727 |
| Decision-making preference | .866 | .519 | .721 |
| Physician-patient concordance | .869 | .570 | .755 |
| Patient compliance | .864 | .561 | .749 |
Correlations between each of the 2 constructs.
| Construct | PPCOMa | PQIHIb | DMPc | PPCONd | PCe |
| PPCOM | 1.000 | —f | — | — | — |
| PQIHI | .724 | 1.000 | — | — | — |
| DMP | .624 | .665 | 1.000 | — | — |
| PPCON | .705 | .722 | .617 | 1.000 | — |
| PC | .725 | .700 | .595 | .686 | 1.000 |
aPPCOM: Physician-patient communication.
bPQIHI: Perceived quality of internet health information.
cDMP: Decision-making preference.
dPPCON: Physician-patient concordance.
ePC: Patient compliance.
fNot applicable.
Multivariate coefficient of determination (R2) results.
| Variables | Control variable effects | ||||
| With control variables | Without control variables | ∆ | ƒ2b | Effects | |
| Perceived quality of internet health information | 0.532 | 0.524 | 0.008 | 0.017 | Insignificant |
| Decision-making preference | 0.394 | 0.389 | 0.005 | 0.008 | Insignificant |
| Physician-patient concordance | 0.501 | 0.496 | 0.005 | 0.010 | Insignificant |
| Patient compliance | 0.588 | 0.570 | 0.018 | 0.044 | Small |
a∆R: R2with control variables− R2without control variables.
bƒ2: Cohen ƒ2.
Figure 2Research model with path coefficients. H1-H6: hypothesis number.
Hypothesis testing.
| Hypothesis | Path coefficient | ||
| Physician-patient communication has a positive impact on patients’ perceived quality of internet health information | .700 | 18.693 | <.001 |
| Physician-patient communication has a positive impact on patients’ decision-making preference | .620 | 16.629 | <.001 |
| Physician-patient communication has a positive impact on physician-patient concordance | .684 | 19.677 | <.001 |
| Patients’ perceived quality of internet health information has a positive impact on patient compliance | .333 | 4.569 | <.001 |
| Patients’ decision-making preference has a positive impact on patient compliance | .151 | 3.002 | .003 |
| Physician-patient concordance has a positive impact on patient compliance | .321 | 3.951 | <.001 |
Partial least squares effect size analysis.
| Constructs | ∆ | ƒ2c | Effect size | |||
| In | Out | |||||
| Perceived quality of internet health information | 0.588 | 0.545 | 0.043 | 0.104 | Small | |
| Decision-making preference | 0.588 | 0.577 | 0.011 | 0.027 | Small | |
| Physician-patient concordance | 0.588 | 0.543 | 0.045 | 0.109 | Small | |
| Physician-patient communication | 0.532 | 0.094 | 0.438 | 0.936 | Large | |
| Physician-patient communication | 0.394 | 0.050 | 0.344 | 0.568 | Large | |
| Physician-patient communication | 0.501 | 0.083 | 0.418 | 0.838 | Large | |
aR: Multivariate coefficient of determination.
b∆ R2: R2with control variables− R2without control variables.
cƒ2: Cohen ƒ2.
Path coefficients by bootstrapping.
| Effect | Path coefficients (SD) | ||
| PPCOMa→PQIHIb | 0.703 (0.038) | .000 | |
| PPCOM→DMPc | 0.623 (0.038) | .000 | |
| PPCOM→PPCONd | 0.687 (0.035) | .000 | |
| PQIHI → PCe | 0.215 (0.066) | .001 | |
| DMP→PC | 0.094 (0.045) | .04 | |
| PPCON→PC | 0.209 (0.082) | .010 | |
| PPCOM→PC | 0.339 (0.067) | .000 | |
| PPCOM→PC | 0.693 (0.035) | .000 | |
aPPCOM: Physician-patient communication.
bPQIHI: Perceived quality of internet health information.
cDMP: Decision-making preference.
dPPCON: Physician-patient concordance.
ePC: Patient compliance.