Table 1 Characteristics of key studies

Mukai et al. (2012) [12]

To test whether access to existing information can be increased by inserting a hyperlink into electronic test results

n = 300 GPs

1. Standard communication (n = 100)

2. Standard communication and 1 email (n = 100)

3. Standard communication and 2 emails (n = 100)

Breast cancer

Hyperlink / information technology

1 February 2009 to 31 October 2009

Self-reported use of hyperlink

Schroll et al. (2012) [13]

To describe the changes in quality of care in general practice using the program

n = 14.173 patients^a / 196 practices

1. Patients with two HbA1c measurements (n = 7.988)

2. Patients with two blood pressure measurements (n = 5.805)

3. Patients with two cholesterol measurements (n = 7.123)

Type 2 Diabetes

Electronic data capture tool and report server

October 2009 to October 2010

Proportion of diabetes cases classified as either controlled or uncontrolled based on different parameters

Smidth et al. (2013) [14]

To test the impact of a model for a Chronic Obstructive Pulmonary Disease (COPD) management program

n = 1.372 patients

1. Intervention group (n = 458)

2. Control group (n = 376)

3. External control group (n = 538)

COPD

Patient identification algorithm

November 2008 to November 2010

Program adherence measured through the use of specific services, as well as the use of out-of-hours services, hospital admissions, etc.

Mukai et al. (2013) [15]

To test whether a web-based clinical decision support system affects PSA testing in general practice

n = 348 practices / 740 GPs

1. Intervention group (n = 114 practices / 247 GPs)

2. Control group (n = 243 practices / 493 GPs)

Prostate cancer

Clinical decision support system

1 January 2010 to 30 Jun 2011

Number of PSA tests (age-standardized) per 1000 men per general practice

Kristiansen et al. (2017) [16]

To test the effect of reminders to GPs regarding missed follow-up after abnormal cervical cytology results

n = 152.551 patients

1. Before group (n = 33.020)

2. Transition group (n = 52.363)

3. After group (n = 60.725)

Cervical cancer

Electronic reminder system

1 January 2009 to 30 May 2014

Proportion of abnormal cervical cytologies without follow-up

Christensen et al. (2018) [17]

To examine the unfolding of the TeleCare North program in three different healthcare settings

n = 15 health professionals

1. Municipal nurses (n = 5)

2. Hospital nurses (n = 2)

3. Lung physicians (n = 2)

4. GPs (n = 6)

COPD

Telemonitoring system

February 2014 to February 2015

Qualitative measures based on interviews, observations, and document studies

Krog et al. (2018) [18]

To identify barriers and facilitating factors to using the web-based tool

n = 9 GPs / 8 practices

N/A

Depression

Telemedicine intervention

February 2017 to April 2017

Qualitative measures based on interview responses

Winthereik et al. (2018) [19]

To develop and conduct pilot testing of an intervention supporting end-of-life care

Unclear^c

1. CME^d meeting attendants (n = 120 GPs)

2. EDS sign-ups (n = 50 GPs)

Cancer and COPD

CME^d and clinical decision support system

Spring 2014

Questionnaires, interviews, and emails to gage GP experiences

Data regarding EDS^e use

Patient-related outcomes, e.g. number of terminal declarations, prescriptions, and home deaths

Mønsted (2019) [20]

To examine challenges related to achieving veracity in development and use of a stratification algorithm

n = 13 patients and 5 GPs

N/A

Multiple lifestyle-related diseases^f

Stratification algorithm

2016

Qualitative measures based on interview responses

Larsen et al. (2019) [21]

To examine attendance in a targeted preventive program and the characteristics of patients who took up the program

n = 2.661 patients

1. Patients diagnosed and/or receiving medical treatment for lifestyle-related disease(n = 699)

2. Patients at high risk of lifestyle-related disease - advised to consult GP (n = 582)

3.Patients engaging in risk behavior - advised to schedule phone-based counseling (n = 618)

4.Patients not exhibiting risk behaviors and not receiving medical treatment (n = 762)

Multiple lifestyle-related diseases^f

Stratification algorithm and personal health profile

April 2016 to December 2016

Attendance, defined as attending a GP medical examination or telephone-based counselling

Broholm-Jørgensen et al. (2020) [22]

To examine preventive health dialogues from both GP and patient perspectives

n = 11 patients 7 GPs^g

N/A

Multiple lifestyle-related diseases^f

Stratification algorithm, digital support system, and personal health profile

2016

Qualitative measures based on interviews and observations

Soerensen et al. (2021) [23]

To develop and validate an AI model to predict 90-day cancer risk based on blood tests

n = 6.592 analytical profiles^h

1. Development cohort (n = 5.224)

2. Validation cohort (n = 1.368)

Cancer

AI model

29 November 2011 to 1 March 2020

Cancer diagnosis within 90 days of blood test

Jakobsen et al. (2021) [24]

To describe behavior, test feasibility, and identify important factors in digital lifestyle coaching of patients with type 2 Diabetes

n = 15 health professionals / 4 practices

1. Practice nurses (n = 6)

2. GPs (n = 9)

Type 2 Diabetes

Digital lifestyle coaching and treatment

August 2019 to September 2019

Qualitative measures based on interviews

Charles et al. (2022) [25]

To examine whether participation in the program increased the probability of GPs prescribing lipid-lowering medication

n = 9.071 patients and 300 GPs

1. Patients attending one of the 165 ‘exposed’ GPs (n = 5.135)

2. Patients attending one of the 135 ‘control’ ‘GPs (n = 3.936)

Type 2 Diabetes

Electronic disease management program

2011 to 2013

Odds ratio, describing the odds of receiving a prescription for lipid-lowering medication

Blanes-Vidal et al. (2022) [26]

To develop and evaluate AI models capable of predicting significant liver stiffness

n = 3.352 patients

1. Training, validation, and testing data set (n = 3.017)

2. Hold-out dataset (n = 335)

Liver disease

AI models

2013 to 2020

Area Under the Curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value