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Table 6 Patient-, clinical-, hospital-, and ICT-related factors associated with the perceived usefulness of a personal health record. 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

1.02 (0.98–1.06)

–

0.99 (0.95–1.04)

–

Gender

 Male

Referent

–

Referent

–

 Female

1.18 (0.49–2.82)

–

1.05 (0.31–3.57)

–

Highest educational level

 Primary school

Referent

–

–

–

 Secondary school: low level

2.60 (0.14–50.0)

–

–

–

 Secondary vocational education

1.00 (0.06–17.5)

–

–

–

 Secondary school: high level

0.80 (0.04–17.2)

–

–

–

 Universities of applied sciences

0.63 (0.04–11.2)

–

–

–

 University

0.50 (0.02–11.1)

–

–

–

Hours a week of private internet use

 0–7

Referent

–

Referent

–

 7–14

0.43 (0.16–1.10)**

0.46 (0.17–1.24)

1.07 (0.32–3.63)

–

 14–28

0.61 (0.18–2.02)

0.57 (0.16–1.96)

2.68 (0.45–16.1)

–

  > 28

0.70 (0.13–3.90)

0.73 (0.12–4.64)

–

–

Experience with digital devices

1.00 (0.82–1.21)

–

0.96 (0.73–1.27)

–

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

 Yes

Referent

–

Referent

–

 No

–

–

0.48 (0.04–5.65)

–

 Partly

0.50 (0.09–2.87)

–

–

–

Number of drugs on the BPMH

1.03 (0.95–1.12)

–

1.10 (0.96–1.25)

–

Number of specialism-related drugs on the BPMH

1.01 (0.87–1.18)

–

1.35 (0.87–2.11)

–

Number of over-the-counter medication

0.97 (0.72–1.29)

–

1.11 (0.71–1.74)

–

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

1.01 (0.99–1.04)

–

0.99 (0.96–1.03)

–

Number of known comorbidities

1.08 (0.93–1.25)

–

1.16 (0.90–1.51)

–

Number of years under treatment of the specialist

–

–

–

–

Number of different prescribers

0.81 (0.54–1.21)

–

1.50 (0.89–2.54)

–

Type of prescriber

 Physician assistant

–

–

Referent

–

 Rheumatologist

–

–

0.64 (0.16–2.58)

–

Reason for the outpatient visit

 Diagnosis

–

–

Referent

–

 Follow-up appointment

–

–

0.63 (0.09–4.28)

–

 New disease

–

–

2.81 (0.49–16.2)

–

 Other

–

–

0.47 (0.10–2.22)

–

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

0.87 (0.76–0.98)*

0.90 (0.78–1.03)

1.07 (0.46–2.48)

–

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

0.43 (0.22–0.84)*

0.51 (0.24–1.09)**

–

–

Device used to log in to the PHR

 Computer

Referent

–

Referent

–

 Tablet

0.47 (0.14–1.57)

–

1.43 (0.32–6.39)

–

 Smartphone

0.60 (0.20–1.77)

–

1.53 (0.42–5.47)

–

Data input from the NMRS in the PHR

 No

Referent

–

Referent

–

 Yes

0.82 (0.29–2.36)

–

0.75 (0.15–3.73)

–

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

1.01 (0.73–1.38)

–

1.01 (0.83–1.22)

–

PHR used for other drug-related purposes

 No

–

–

Referent

Referent

 Yes

–

–

20.00 (2.36–170)*

20.0 (2.36–170)*

Percentage of logins 12 months before the hospital visit

1.02 (0.99–1.06)

–

1.03 (0.98–1.07)

–

Percentage of logins 12 months after the hospital visit

0.98 (0.94–1.01)

–

1.00 (0.99–1.02)

–

  1. Abbreviations: BPMH best possible medication history, NMRS nationwide medication record system
  2. aThe significant risk factors (P < .1): hours a week of private internet use, the number of outpatient visits to the specialist in the last 12 months, and the number of hospital admissions at the admitted department in the last 12 months were selected and incorporated in the full model logistic regression analyses
  3. bThe significant risk factor (P < .1) ‘PHR used for other drug-related purposes’ was selected and incorporated in the full model logistic regression analyses
  4. * P < .05
  5. ** P < .1