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Table 4 Patient-, clinical-, hospital-, and ICT-related factors associated with a good usability of a PHR. The data were collected for patients with an outpatient visit (at the rheumatology ward, N = 78) or a planned admission in the hospital (at the cardiology, neurology, internal medicine or pulmonary wards, N = 177)

From: Usability and perceived usefulness of patient-centered medication reconciliation using a personalized health record: a multicenter cross-sectional study

 

Inpatients

(N = 177)

Outpatients

(N = 78)

Crude OR (95%CI)

Adjusted ORa (95%CI)

Crude OR (95%CI)

Adjusted ORb (95%CI)

Age

0.99 (0.96–1.02)

–

0.96 (0.91–1.01)

–

Gender

 Male

Referent

–

Referent

–

 Female

0.79 (0.38–1.67)

–

2.68 (0.80–9.01)

–

Highest educational level

 Primary school

Referent

–

Referent

–

 Secondary school: low level

0.32 (0.03–3.04)

–

1.17 (0.07–18.3)

–

 Secondary vocational education

0.43 (0.05–3.85)

–

3.00 (0.23–39.6)

–

 Secondary school: high level

1.50 (0.12–19.6)

–

0.63 (0.04–9.65)

–

 Universities of applied sciences

0.72 (0.08–6.77)

–

9.00 (0.39–207)

–

 University

1.08 (0.08–14.4)

–

–

–

Hours a week of private internet use

 0–7

Referent

–

Referent

Referent

 7–14

1.28 (0.56–2.93)

–

4.60 (0.94–22.6)**

4.03 (0.80–20.2)**

 14–28

1.02 (0.41–2.56)

–

–

–

  > 28

2.29 (0.26–19.9)

–

–

–

Experience with digital devices

1.39 (1.18–1.64)*

–

1.36 (1.01–1.83)*

1.26 (0.93–1.72)

Patients with knowledge about the indication(s) of their drug(s)

 Yes

Referent

–

Referent

–

 No

–

–

0.36 (0.03–4.26)

–

 Partly

0.30 (0.10–0.87)*

–

–

– 

Number of drugs on the BPMH

0.94 (0.87–1.02)

–

1.07 (0.91–1.25)

–

Number of specialism-related drugs on the BPMH

1.00 (0.87–1.15)

–

1.60 (0.89–2.90)

–

Number of over-the-counter medication

0.95 (0.78–1.16)

–

0.85 (0.61–1.19)

–

Number of changes in patient’s drug list in the last 12 months

1.00 (0.97–1.01)

–

1.00 (0.96–1.04)

–

Number of known comorbidities

0.90 (0.79–1.02)

–

0.96 (0.74–1.26)

–

Number of years under treatment of the specialist

0.96 (0.90–1.03)

–

0.74 (0.47–1.17)

–

Number of different prescribers

0.69 (0.49–0.98)*

–

1.09 (0.65–1.84)

–

Type of prescriber

 Physician assistant

–

–

–

–

 Rheumatologist

–

–

–

–

Reason for the outpatient visit

 Diagnosis

–

–

Referent

–

 Follow-up appointment

–

–

0.58 (0.10–3.47)

–

 New disease

–

–

–

–

 Other

–

–

0.77 (0.18–3.38)

–

Number of outpatient visits to the specialist in the last 12 months

1.03 (0.94–1.14)

–

0.68 (0.34–1.36)

–

Number of hospital admissions at the admitted department in the last 12 months

1.10 (0.71–1.69)

–

–

–

Device used to log in to the PHR

    

 Computer

Referent

–

Referent

–

 Tablet

0.37 (0.15–0.96)*

–

1.67 (0.32–8.70)

–

 Smartphone

0.40 (0.17–0.93)*

–

2.22 (0.44–11.3)

–

Data input from the NMRS in the PHR

    

 No

Referent

–

Referent

–

 Yes

0.78 (0.25–2.48)

–

0.72 (0.14–3.66)

–

Available time for patients to log in to the PHR

0.92 (0.82–1.04)

–

1.08 (0.75–1.56)

–

Number of days between sending the invitation and the patient login to the PHR

1.03 (0.80–1.33)

–

0.94 (0.77–1.16)

–

Help received for using the PHR

 Yes

Referent

–

Referent

–

 No

3.55 (0.48–26.1)

–

–

–

PHR used for other drug-related purposes

 No

–

– 

Referent

–

 Yes

–

–

4.33 (0.53–35.8)

–

Percentage of logins 12 months before the hospital visit

1.01 (0.98–1.04)

–

1.00 (0.96–1.05)

–

Percentage of logins 12 months after the hospital visit

1.02 (1.00–1.05)**

1.01 (0.95–1.07)

1.00 (0.98–1.02)

–

  1. Abbreviations: BPMH best possible medication history, NMRS nationwide medication record system
  2. * P < .05
  3. ** P < .1
  4. aThe significant risk factors (P < .1): experience with digital devices, patient’s knowledge about the indication(s) of their drug(s), the number of different prescribers, device used to log in to the PHR, and the percentage of logins 12 months after the hospital visit were selected and incorporated in the full model logistic regression analyses
  5. bThe significant risk factors (P < .1): hours a week of private internet use and experience with digital devices were selected and incorporated in the full model logistic regression analyses