| Literature DB >> 33985502 |
Marsa Gholamzadeh1, Hamidreza Abtahi2, Marjan Ghazisaeeidi3.
Abstract
BACKGROUND: One of the main elements of patient-centered care is an enhancement of patient preparedness. Thus, pre-visit planning assessment tools was emerged to prepare and involve patients in their treatment process.Entities:
Keywords: Framework; Patient care planning; Patient-centered care; Pre-visit
Year: 2021 PMID: 33985502 PMCID: PMC8116646 DOI: 10.1186/s12913-021-06456-7
Source DB: PubMed Journal: BMC Health Serv Res ISSN: 1472-6963 Impact factor: 2.655
Fig. 1The conceptual model of pre-visit planning
Fig. 2PRISMA workflow for summarizing the selection of papers process
The frequency of different types of study
| Study type | Frequency | Percentage | References |
|---|---|---|---|
| RCT | 21 | 44.9% | [ |
| Before-after study | 10 | 18.4% | [ |
| Descriptive study | 9 | 18.4% | [ |
| Cross-sectional study | 2 | 4.1% | [ |
| Mixed method | 2 | 4.1% | [ |
| Cohort | 1 | 2.0% | [ |
| Non-Randomized trials | 1 | 2.0% | [ |
| Quasi-experimental study | 1 | 2.0% | [ |
| Sequential prospective study | 1 | 2.0% | [ |
| Time-series analysis | 1 | 2.0% | [ |
Summary of reviewed articles and evidence
| # | Author | Year | Journal | Pre-visit model | Objective | Findings and applied techniques characteristics | CASP SCORE | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sample size | Effectiveness | Clinic | Country | Disease | Type of medical informatics solution | Collected data | Outcome measures | |||||||
| 1 | Allende-Richter, S. H. et al. [ | 2018 | (1) Enhance team working among care team members and (2) Provide early access to existing medical services. | Not mentioned | +++ | Primary care clinic | USA | General | Pre-assessment tools | Demographic data, Medical history, Family history, Reason for referral, Symptoms, Medication | Patient-provider communication, Perceived involvement in care, Patient satisfaction, Identifying referral appropriateness | 18 | ||
| 2 | Rivo, J. et al. [ | 2015 | Improving compliance with recommended tests and screenings. | 7491 patients | +++ | Primary care clinic | USA | Diabetes | Decision aid tools | Demographic data, Reason for referral, Symptoms | Patient-provider communication, Perceived involvement in care, Patient expectations in consultations, Adherence to visit scheduling | 17 | ||
| 3 | Cox, N et al. [ | 2018 | To evaluate the impact of a pre-visit pharmacist consultation for chronic non-cancer pain | 45 patients | +++ | A family medicine residency clinic | USA | Chronic Opioid | Pre-assessment tools | Demographic data, Reason for referral, Symptoms | Patient satisfaction, Patient expectations in consultations, Appointment intake information, Medication and treatment adherence, ITT analysis, Mental health topics | 16 | ||
| 4 | Paget et al. [ | 2015 | To increase patient compliance with scheduled appointments, follow up, and complete exams on time. | 5539 patients | +++ | Diabetic clinic | USA | Diabetes | Decision aid tools | Demographic data, Reason for referral, Symptoms | Illness perceptions, Perceived involvement in care, Patient satisfaction, Patient expectations in consultations, Medication and treatment adherence, Adherence to visit scheduling, Visit length | 17 | ||
| 5 | Bose-Brill, S et al. [ | 2018 | To determine the impact of pre-visit ACP planning using a secure EHR-linked framework | 419 patients aged between 50 and 93 years | +++ | Routine follow-up visit | USA | Primary care clinic | Decision aid tools, Pre-assessment tools, Reminders | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Patient awareness, Drug side effects Symptoms, Medication | Patient-provider communication, Illness perceptions, and knowledge, Patient expectations in consultations, Medication and treatment adherence, visit length, Identifying referral appropriateness | 15 | ||
| 6 | Riese, A et al. [ | 2015 | To determine the efficacy of electronic pre-visit questionnaires (PVCs) | 183 adolescents | +++ | Pediatric primary care clinic | USA | pediatric diseases | Pre-assessment tools | Demographic data, Medical history, Family history, Reason for referral, Symptoms, Medication | Illness perceptions, Perceived involvement in care, Patient satisfaction, Identifying referral appropriateness | 18 | ||
| 7 | Myers, P et al. [ | 2020 | To improve patient understanding of insurance coverage by providing educational materials | 100 patients | ++ | Surgery clinic | USA | Obesity | Patient education | Demographic data, Reason for referral, Patient awareness, Drug side effects Medication | Patient satisfaction, Appointment intake information, Quality of life | 16 | ||
| 8 | Frank, O et al. [ | 2014 | To assess whether ongoing programs are acceptable to patients and feasible in busy routine clinical practice. | 14 GP and 130 patients | +++ | General clinic | Australian | General | Pre-assessment tools | Demographic data, Medical history, Family history, Reason for referral, Symptoms, Medication | Patient-provider communication, Patient satisfaction, Patient expectations in consultations, Identifying referral appropriateness | 13 | ||
| 9 | Lewin, W et al. [ | 2009 | Can Fam Physician | To assess the efficacy of a pre-visit questionnaire (PVQ) | 210 patients aged 13 to 19 | +++ | Primary care | Canada | Psychology | Pre-assessment tools | Demographic data, Medical history, Family history, Reason for referral, Symptoms, Medication | Patient-provider communication, Illness perceptions, Patient satisfaction, Patient expectations in consultations, Identifying referral appropriateness | 18 | |
| 10 | Liu, T et al. [ | 2018 | J Arthroplasty | To clarify the patient preference with hip and knee arthritis regarding pre-visit completion | 51 Patients | ++ | Arthroplasty clinics | USA | Hip and Knee Pain | Pre-assessment tools, Decision aid tools | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Symptoms, Medication | Illness perceptions and knowledge, Perceived involvement in care, Patient waiting times, Identifying referral appropriateness | 18 | |
| 11 | Stankowski-Drengler, T. J et al. [ | 2019 | Ann Surg Oncol | To assess completion, delivery method, and barriers or facilitators to pre-visit completion | 201 patients | ++ | Cancer clinic | USA | Breast cancer | Patient education | Demographic data, Reason for referral, Patient awareness, Symptoms | Illness perceptions, Perceived involvement in care, Appointment intake information | 14 | |
| 12 | Wald, J. S et al. [ | 2010 | J Am Med Inform Assoc | To examine the impact of pre-visit electronic journals in primary care as a decision aid | 2027 patients and 272 physicians | +++ | Primary clinic | USA | General | Decision aid tools, Pre-assessment tools, Reminders | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Patient awareness, Drug side effects Symptoms, Medication | Patient-provider communication, Illness perceptions, Perceived involvement in care, Patient satisfaction, Patient expectations in consultations, Identifying referral appropriateness | 20 | |
| 13 | Zanini, C et al. [ | 2018 | Patient Educ Couns | Assess high-quality websites on patients’ perceptions of | 38 patients | +++ | neurology | Switzerland | Chronic pain | Pre-assessment tools | Demographic data, Medical history, Family history, Lab data, Reason for referral, Symptoms | Patient-provider communication, Patient expectations in consultations | 15 | |
| 14 | Grant. R et al. [ | 2016 | Contemp Clin Trials | To determine a strategy for improving diabetes care | 146 physicians with 2496 of their patients | +++ | Primary care clinic | Canada | Diabetes | Decision aid tools, Pre-assessment tools, Reminders | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Patient awareness, Drug side effects Symptoms, Medication | Illness perceptions, Perceived involvement in care, Medication and treatment adherence, Medication and treatment adherence, Symptom control | 19 | |
| 15 | Frank, O. R et al. [ | 2011 | BMC Fam Pract | Assess satisfaction with the decision process | Sixty patients | +++ | Primary care clinic | Australia | General | Reminders | Demographic data, Medication | Patient satisfaction, Medication and treatment adherence, Adherence to visit scheduling | 18 | |
| 16 | Rodenbach, R et al. [ | 2017 | J Clin Oncol | To examine the impact of a decision aid versus high-quality websites | 24 oncologists and 170 patients | +++ | Oncology clinic | USA | Cancer | Decision aid, Patient education | Demographic data, Drug side effects | Illness perceptions, Patient satisfaction, Patient expectations in consultations, Appointment intake information | 17 | |
| 17 | Hitchings, S., and Barter, J [ | 2009 | J Fam Plann Reprod Health Care | This study examined whether and how a pre-consultation sheet (PCS) can facilitate doctors in identifying targets for medical advice. | 193 patients | +++ | sexual health clinics | UK | Sexual problems | Pre-assessment tools | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Patient awareness, Drug side effects Symptoms, Medication | Patient-provider communication, Illness perceptions, Perceived involvement in care, Adherence to visit scheduling, ITT analysis, Self-care, Mental health topics, Identifying referral appropriateness Symptom control | 19 | |
| 18 | Sleath, B et al. [ | 2017 | Patient Educ Couns | To improve patient-provider communication during time-limited primary care visits and represent a strategy for improving diabetes care. | 259 | +++ | pediatric asthma clinic | USA | Asthma | Patient education | Demographic data, Patient awareness | Patient-provider communication, Appointment intake information | 15 | |
| 19 | Tucholka, J. L. et al. [ | 2018 | J Am Coll Surg | To assess the acceptability of a new strategy of pre-consultation prevention summaries and reminders in general practice. | 377 patients | +++ | Breast cancer clinic | USA | Breast cancer | Patient education | Demographic data, Patient awareness | Illness perceptions, Medication and treatment adherence | 16 | |
| 20 | Aboumatar, H. J et al. [ | 2013 | J Gen Intern Med | Combining patient-oncologist intervention to improve communication in advanced cancer | 41 primary care physicians and 275 of their patients | +++ | Primary care clinic | USA | Hypertension | Patient education | Demographic data, Patient awareness | Appointment intake information | 18 | |
| 21 | Albada, A. et al. [ | 2015 | Patient Educ Couns | To help reduce waiting times and duplication of work, improve patient pathways and decrease wasted visits | 197 patients | + | Breast cancer clinic | Norway | Breast cancer | Decision aid tools, Pre-assessment tools, Reminders | Demographic data, Medical history, Family history, Reason for referral, Symptoms, Medication | Patient-provider communication, Perceived involvement in care, Adherence to visit scheduling, Visit length | 18 | |
| 22 | Bruce, J. et al. [ | 2018 | J Cancer Educ | To evaluate teen feedback on an asthma question intervention designed to motivate teens to be more engaged during visits and | 377 patients | ++ | breast surgery clinic | USA | Breast cancer | Patient education | Patient awareness, Symptoms | Appointment intake information, Identifying referral appropriateness | 15 | |
| 23 | Walker, M. E. et al. [ | 2018 | J Hand Surg Asian Pac | To compare patients’ knowledge after the pre-consultation delivery of standard websites versus a web-based decision aid (DA). | 71 patients | ++ | Surgery clinic | USA | Hand problems | Pre-assessment tools | Demographic data, Medical history, Reason for referral, Symptoms | Patient-provider communication | 14 | |
| 24 | Savage, C. et al. [ | 2019 | Int J Qual Health Care | To elucidate how HL influences patients’ interest in participating in medical visit communication. | 289 questionnaires | +++ | Primary clinic | Sweden | General | Pre-assessment tools | Demographic data, Medical history, Reason for referral, Symptoms | Patient-provider communication, Perceived involvement in care | 14 | |
| 25 | Judson, T. J. et al. [ | 2020 | J Am Med Inform Assoc | To prepare for breast cancer genetic counseling. | 950 unique patients | +++ | the large academic health system | Canada | COVID-19 | Pre-assessment tools | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Drug side effects Symptoms, Medication | Perceived involvement in care, Self-care, Self-care, Symptom control | 18 | |
| 26 | Albada, A. et al. [ | 2012 | Genet Med | To test an approach for delivering web-based information to breast cancer patients. | 200 counselees | +++ | Breast cancer genetic counseling clinic | Netherlands | Breast cancer | Decision aid tools, Pre-assessment tools | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Drug side effects Symptoms, Medication | Patient-provider communication, Appointment intake information, Adherence to visit scheduling | 18 | |
| 27 | Purkaple, B. A et al. [ | 2016 | Ann Fam Med | To measure hand surgery patient understanding compared with a US academic hand surgery practice | 64 encounters | ++ | Primary clinic | USA | General | Pre-assessment tools | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Symptoms, Medication | Patient-provider communication | 13 | |
| 28 | Krist, A. H. et al. [ | 2007 | Ann Fam Med | To explore how the See-and-Treat concept can be applied in primary care and its effect | 497 participants | ++ | Primary clinic | USA | Prostate cancer | Patient education | Demographic data, Patient awareness | Patient expectations in consultations, visit length, Identifying referral appropriateness | 17 | |
| 29 | Fothergill, K. E. et al. [ | 2013 | Acad Pediatr | To direct patients to targeted intake, advice, information, and care for respiratory symptoms and COVID-19 concerns | 172 parents | ++ | primary care pediatric | USA | Mental health | Decision aid tools, Pre-assessment tools, Patient education | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Patient awareness, Drug side effects Symptoms, Medication | Patient-provider communication, Illness perceptions, Appointment intake information, Patient waiting times, Mental health topics | 11 | |
| 30 | Lee, Y. K et al. [ | 2017 | J Eval Clin Pract | To address the unmet needs of patients with chronic diseases regarding the pre-visit website | 15 participants | +++ | Primary care | Malaysia | Chronic disease | Decision aid tools, Pre-assessment tools, Patient education | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Drug side effects Symptoms, Medication | Illness perceptions, Perceived involvement in care, Appointment intake information, Identifying referral appropriateness | 20 | |
| 31 | Johansen, M. A. et al. [ | 2011 | Methods Inf Med | To wonder if patients could encourage primary care physicians by writing goals on pre-encounter forms. | 83 respondents | +++ | visiting university locations | Norway | General | Decision aid tools, Pre-assessment tools, Patient education | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Drug side effects Symptoms, Medication | Patient-provider communication, Illness perceptions, Patient expectations in consultations | 11 | |
| 32 | Hu, X et al. [ | 2012 | J Health Commun | To evaluate whether pre-visit educational decision aids facilitate shared decision making. | 505 respondents | +++ | primary care | USA | General | Patient education | Demographic data, Patient awareness | Patient expectations in consultations, Appointment intake information | 15 | |
| 33 | Albada, A. et al. [ | 2012 | Fam Cancer | To evaluate how parents and physicians perceive the utility of a comprehensive, electronic pre-visit screening, and its impact on the visit. | 371 counselees | +++ | Breast cancer genetic counseling clinic | Netherlands | Breast cancer | Patient education | Demographic data, Patient awareness | Patient satisfaction, Medication and treatment adherence, Illness perceptions, and knowledge | 15 | |
| 34 | Frost, J. et al. [ | 2019 | BMJ Open | To explore the impact of a pre-consultation website in addressing patients’ unmet needs during chronic disease consultations. | 120 patients and 15 diabetologists | +++ | Diabetes clinics | UK | Diabetes | Decision aid tools, Pre-assessment tools, Patient education | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Drug side effects Symptoms, Medication | Appointment intake information, Visit length | 17 | |
| 35 | O’Brien, M et al. [ | 2017 | BMC Fam Pract | To investigate people’s attitude towards providing symptom information electronically before a consultation. | 831 patients | + | Family physician’s clinic | Canada | Lung cancer | Decision aid tools, Pre-assessment tools, Patient education | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Drug side effects Symptoms, Medication | Illness perceptions, Patient expectations in consultations, Appointment intake information, Medication and treatment adherence, Mental health topics | 20 | |
| 36 | Wald, J. S. et al. [ | 2009 | AMIA Annu Symp Proc | To investigate the potential of e-journal to improve patient care during a visit | 126 patients and 230 primary care providers | +++ | Primary care | USA | Diabetes | Decision aid tools, Pre-assessment tools, Patient education, Reminders | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Drug side effects Symptoms, Medication | Appointment intake information, Self-care, Symptom control, Visit length, | 16 | |
| 37 | Albertson, G. et al. [ | 2002 | Am J Manag Care | To tailor information might help the patient to prepare for their first visit | 1495 consecutive patient visits | +++ | internal medicine clinic | USA | General | Pre-assessment tools | Demographic data, Medical history, Reason for referral, Symptoms, Medication | Patient-provider communication, Visit length | 14 | |
| 38 | Wolff, J. L. et al. [ | 2014 | J Am Geriatr Soc | To explore whether a pre-consultation web-based intervention enables patients with diabetes to articulate their agenda in a consultation | Thirty-two patients age 65+ | +++ | Geriatric clinic | USA | Older patients | Pre-assessment tools | Demographic data, Medical history, Patient awareness, Symptoms, Medication | Perceived involvement in care, Patient expectations in consultations | 17 | |
| 39 | Causarano, N. et al. [ | 2015 | Support Care Cancer | To compare the acceptability and feasibility of using brief electronic versus paper screening | 41 patients | +++ | plastic surgery clinic | Canada | Breast Cancer | Patient education | Demographic data, Patient awareness | Patient expectations in consultations, Medication, and treatment adherence | 15 | |
| 40 | Grant, R. W. et al. [ | 2008 | Arch Intern Med | To evaluate a patient chart information in preparation for a scheduled office visit | 244 patients with DM | +++ | primary care | USA | Diabetes | Decision aid tools, Pre-assessment tools, Patient education | Demographic data, Medical history, Lifestyle, Family history, Lab data, Reason for referral, Patient awareness, Drug side effects Symptoms, Medication | Patient-provider communication, Illness perceptions, Medication and treatment adherence, Symptom control | 12 | |
| 41 | Brackett, C., & Kearing, S [ | 2015 | Patient | To determine whether a brief pre-visit questionnaire can improve primary care provider | 11,493 patients | +++ | Cancer clinic | USA | Cancer | Patient education | Demographic data, Patient awareness | Patient expectations in consultations, Mental health topics, Visit length | 15 | |
| 42 | Meropol, N. J. et al. [ | 2013 | Cancer | To assess the acceptability of a pre-consultation checklist for older patients | 1932 patients | +++ | Cancer clinic | USA | Cancer | Decision aid tools, Pre-assessment tools | Demographic data, Medical history, Family history, Lab data, Reason for referral, Drug side effects Symptoms, Medication | Illness perceptions, Perceived involvement in care, Patient expectations in consultations, Visit length | 15 | |
| 43 | Kim-Hwang, J. E. et al. [ | 2010 | J Gen Intern Med | Bridging the gap about post-mastectomy breast by applying a new approach | 540 questionnaires | +++ | Primary care | USA | General | Decision aid tools, Pre-assessment tools, Patient education, Reminders | Demographic data, Medical history, Family history, Reason for referral, Symptoms, Medication | Patient-provider communication, Illness perceptions, Perceived involvement in care, Patient satisfaction, Medication and treatment adherence, Adherence to visit scheduling | 16 | |
| 44 | Muraywid, B. et al. [ | 2020 | J Manag Care Spec Pharm | To evaluate the impact of a DMSPECIFIC PHR | 700 patients | +++ | Primary care | Colombia | Diabetes | Decision aid tools, Pre-assessment tools, Patient education, Reminders | Demographic data, Medical history, Reason for referral, Symptoms, Medication | Patient-provider communication, Illness perceptions, Patient satisfaction, Appointment intake information, Adherence to visit scheduling, Quality of life | 9 | |
| 45 | Vo, M. T. et al. [ | 2019 | Journal of General Internal Medicine | To facilitate shared decision-making by utilizing a web-based survey system before the visit. | 1276 patients | + | primary care | USA | Diabetes | Decision aid tools, Pre-assessment tools | Demographic data, Medical history, Lab data, Reason for referral, Symptoms, Medication | Patient-provider communication, Perceived involvement in care, Patient satisfaction | 19 | |
| 46 | Baker, D. W. et al. [ | 2011 | Journal of the American Medical Informatics Association | To develop an intervention to improve communication between patients and their physicians | 12,288 patients | +++ | Internal medicine | USA | General | Decision aid tools, Pre-assessment tools | Demographic data, Medical history, Lab data, Reason for referral, Symptoms, Medication | Patient-provider communication, Perceived involvement in care, Patient satisfaction, Adherence to visit scheduling, Self-care, Symptom control | 16 | |
| 47 | Grant, Richard W, et al. [ | 2019 | The Annals of Family Medicine | To improve patient values and needs. | 750 English- or Spanish-speaking patients | +++ | Primary care | Canada | General | Decision aid tools, Pre-assessment tools | Demographic data, Medical history, Lab data, Reason for referral, Symptoms, Medication | Patient-provider communication, Patient satisfaction, Appointment intake information | 17 | |
| 48 | Harrington, J. T., & Walsh, M. B [ | 2001 | Arthritis Care & Research: Official Journal | To determine the impact of e-referral and pre-visit planning. | 270 patients | +++ | Rheumatology | USA | Rheumatology diseases | Decision aid tools, Pre-assessment tools, Patient education, Reminders | Demographic data, Medical history, Family history, Reason for referral, Symptoms, Medication | Patient-provider communication, Illness perceptions, Perceived involvement in care, Patient satisfaction, Medication and treatment adherence, Adherence to visit scheduling | 10 | |
| 49 | Gadomski, A. M et al. [ | 2015 | Journal of Adolescent Health | Their objective was to improve health outcomes and reducing costs. | 72 patients | +++ | pediatric primary care | USA | Mental health | Decision aid tools, Pre-assessment tools, Patient education, Reminders | Demographic data, Medical history, Family history, Reason for referral, Symptoms, Medication | Patient-provider communication, Perceived involvement in care, Patient satisfaction, Appointment intake information, Medication adherence, Adherence to visit scheduling, Reductions in prescription costs, Mental health topics | 14 | |
Fig. 3The distribution of studies based on their conducted countries worldwide
Results of study analysis based on main objectives and timing
| Author | Main Approaches | Timing | |||||
|---|---|---|---|---|---|---|---|
| Improve the current visit | Patient preparedness | providing inclusive insight for physician | Before each visit | Between each visit | At end of the current visit | ||
| Allende-Richter, S. et al. | √ | ||||||
| Rivo, J. et al. | √ | √ | |||||
| Cox, N et al. | √ | √ | |||||
| Page.T et al. | √ | √ | |||||
| Bose-Brill, S et al. | √ | √ | √ | √ | |||
| Riese, A et al. | √ | √ | |||||
| Myers, P et al. | √ | √ | √ | √ | |||
| Frank, O et al. | √ | √ | √ | ||||
| Lewin, W et al. | √ | √ | |||||
| Liu, T et al. | √ | √ | |||||
| Stankowsk, T. J et al. | √ | √ | |||||
| Wald, J. S et al. | √ | √ | |||||
| Zanini, C et al. | √ | √ | √ | ||||
| Grant. R et al. | √ | √ | √ | ||||
| Frank, O. R et al. | √ | √ | |||||
| Rodenbach, R. et al. | √ | √ | |||||
| Hitchings, S., and Barter, J. | √ | √ | |||||
| Sleath, B et al. | √ | √ | √ | √ | |||
| Tucholka, J. et al. | √ | √ | |||||
| Aboumatar, H et al. | √ | √ | |||||
| Albada, A. et al. (2012) | √ | √ | √ | ||||
| Bruce, J. G. et al. | √ | √ | |||||
| Walker, M. E. et al. | √ | √ | |||||
| Savage, C. et al. | √ | √ | √ | ||||
| Judson, T. J. et al. | √ | √ | √ | √ | |||
| Albada, A. et al. (2015) | √ | √ | |||||
| Purkaple, B et al. | √ | √ | √ | ||||
| Krist, A. H. et al. | √ | √ | √ | √ | |||
| Fothergill, K. et al. | √ | √ | √ | √ | |||
| Lee, Y. K et al. | √ | √ | √ | √ | |||
| Johansen, M. et al. | √ | √ | |||||
| Hu, X et al. | √ | √ | √ | ||||
| Albada, A. et al. | √ | √ | √ | √ | |||
| Frost, J. et al. | √ | √ | |||||
| O’Brien, M et al. | √ | √ | √ | ||||
| Wald, J. S. et al. | √ | √ | √ | √ | |||
| Albertson, G. et al. | √ | √ | |||||
| Wolff, J. L. et al. | √ | √ | |||||
| Causarano, N. et al. | √ | √ | |||||
| Grant, R. W. et al. | √ | √ | √ | √ | |||
| Brackett, C, & Kearing, S. | √ | √ | √ | √ | |||
| Meropol, N. J. et al. | √ | √ | √ | √ | |||
| Kim-Hwang, J. E. et al. | √ | √ | √ | √ | |||
| Muraywid, B. et al. | √ | √ | √ | √ | √ | √ | |
| Vo, M. T. et al. | √ | √ | |||||
| Baker, D. W. et al. | √ | √ | |||||
| Grant, R et al. | √ | √ | |||||
| Harrington, J, & Walsh, M | √ | √ | √ | √ | √ | √ | |
| Gadomski, A. M et al. | √ | √ | √ | √ | √ | √ | |
Fig. 4Distribution of studies based on sample size, year, and different techniques
Fig. 5Effectiveness of studies concerning applied methods
Outcome measures reported in these articles with their frequency and their effectiveness
| Outcome measure | Low | Medium | High | Total |
|---|---|---|---|---|
| Patient-provider communication | 1 | 4 | 21 | 26 |
| Illness perceptions and knowledge | 1 | 5 | 15 | 21 |
| Perceived involvement in care | 3 | 3 | 14 | 20 |
| Patient satisfaction | 1 | 5 | 12 | 18 |
| Patient expectations in consultations | 5 | 12 | 17 | |
| Appointment intake information | 2 | 4 | 11 | 17 |
| Medication and treatment adherence | 1 | 2 | 9 | 12 |
| Adherence to visit scheduling | 2 | 9 | 11 | |
| Identifying referral appropriateness | 2 | 9 | 11 | |
| Visit length | 2 | 7 | 9 | |
| Symptom control | 1 | 5 | 6 | |
| Mental health topics | 2 | 4 | 6 | |
| Self-care | 2 | 3 | 5 | |
| Intention-to-treat (ITT) analysis | 2 | 2 | ||
| Quality of life | 2 | 2 | ||
| Patient waiting times | 2 | 2 | ||
| Reductions in prescription costs | 1 | 1 |
Fig. 6Frequency of disease regarding applied methods and their effectiveness
Fig. 7Distribution of different kinds of collected data regarding pre-visit techniques
Fig. 8The main applied techniques through pre-visit planning in terms of medical informatics
Fig. 9The overall model of pre-visit planning care