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Pannunzio V, Morales Ornelas HC, Gurung P, van Kooten R, Snelders D, van Os H, Wouters M, Tollenaar R, Atsma D, Kleinsmann M. Patient and Staff Experience of Remote Patient Monitoring-What to Measure and How: Systematic Review. J Med Internet Res 2024; 26:e48463. [PMID: 38648090 DOI: 10.2196/48463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/25/2023] [Accepted: 02/20/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Patient and staff experience is a vital factor to consider in the evaluation of remote patient monitoring (RPM) interventions. However, no comprehensive overview of available RPM patient and staff experience-measuring methods and tools exists. OBJECTIVE This review aimed at obtaining a comprehensive set of experience constructs and corresponding measuring instruments used in contemporary RPM research and at proposing an initial set of guidelines for improving methodological standardization in this domain. METHODS Full-text papers reporting on instances of patient or staff experience measuring in RPM interventions, written in English, and published after January 1, 2011, were considered for eligibility. By "RPM interventions," we referred to interventions including sensor-based patient monitoring used for clinical decision-making; papers reporting on other kinds of interventions were therefore excluded. Papers describing primary care interventions, involving participants under 18 years of age, or focusing on attitudes or technologies rather than specific interventions were also excluded. We searched 2 electronic databases, Medline (PubMed) and EMBASE, on February 12, 2021.We explored and structured the obtained corpus of data through correspondence analysis, a multivariate statistical technique. RESULTS In total, 158 papers were included, covering RPM interventions in a variety of domains. From these studies, we reported 546 experience-measuring instances in RPM, covering the use of 160 unique experience-measuring instruments to measure 120 unique experience constructs. We found that the research landscape has seen a sizeable growth in the past decade, that it is affected by a relative lack of focus on the experience of staff, and that the overall corpus of collected experience measures can be organized in 4 main categories (service system related, care related, usage and adherence related, and health outcome related). In the light of the collected findings, we provided a set of 6 actionable recommendations to RPM patient and staff experience evaluators, in terms of both what to measure and how to measure it. Overall, we suggested that RPM researchers and practitioners include experience measuring as part of integrated, interdisciplinary data strategies for continuous RPM evaluation. CONCLUSIONS At present, there is a lack of consensus and standardization in the methods used to measure patient and staff experience in RPM, leading to a critical knowledge gap in our understanding of the impact of RPM interventions. This review offers targeted support for RPM experience evaluators by providing a structured, comprehensive overview of contemporary patient and staff experience measures and a set of practical guidelines for improving research quality and standardization in this domain.
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Affiliation(s)
- Valeria Pannunzio
- Department of Design, Organisation and Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Hosana Cristina Morales Ornelas
- Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Pema Gurung
- Walaeus Library, Leiden University Medical Center, Leiden, Netherlands
| | - Robert van Kooten
- Department of Surgery, Leiden University Medical Center, Leiden, Netherlands
| | - Dirk Snelders
- Department of Design, Organisation and Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Hendrikus van Os
- National eHealth Living Lab, Department of Public Health & Primary Care, Leiden University Medical Center, Leiden, Netherlands
| | - Michel Wouters
- Department of Surgery, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, Netherlands
| | - Rob Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden, Netherlands
| | - Douwe Atsma
- Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands
| | - Maaike Kleinsmann
- Department of Design, Organisation and Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
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Rakers M, van Hattem N, Simic I, Chavannes N, van Peet P, Bonten T, Vos R, van Os H. Tailoring remote patient management in cardiovascular risk management for healthcare professionals using panel management: a qualitative study. BMC Prim Care 2024; 25:122. [PMID: 38643103 PMCID: PMC11031879 DOI: 10.1186/s12875-024-02355-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 03/28/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND While remote patient management (RPM) has the potential to assist in achieving treatment targets for cardiovascular risk factors in primary care, its effectiveness may vary among different patient subgroups. Panel management, which involves proactive care for specific patient risk groups, could offer a promising approach to tailor RPM to these groups. This study aims to (i) assess the perception of healthcare professionals and other stakeholders regarding the adoption and (ii) identify the barriers and facilitators for successfully implementing such a panel management approach. METHODS In total, nineteen semi-structured interviews and two focus groups were conducted in the Netherlands. Three authors reviewed the audited transcripts. The Consolidated Framework for Implementation Strategies (CFIR) domains were used for the thematic analysis. RESULTS A total of 24 participants (GPs, nurses, health insurers, project managers, and IT consultants) participated. Overall, a panel management approach to RPM in primary care was considered valuable by various stakeholders. Implementation barriers encompassed concerns about missing necessary risk factors for patient stratification, additional clinical and technical tasks for nurses, and reimbursement agreements. Facilitators included tailoring consultation frequency and early detection of at-risk patients, an implementation manager accountable for supervising project procedures and establishing agreements on assessing implementation metrics, and ambassador roles. CONCLUSION Panel management could enhance proactive care and accurately identify which patients could benefit most from RPM to mitigate CVD risk. For successful implementation, we recommend having clear agreements on technical support, financial infrastructure and the criteria for measuring evaluation outcomes.
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Affiliation(s)
- Margot Rakers
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, 2333 ZA, The Netherlands.
| | - Nicoline van Hattem
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, 2333 ZA, The Netherlands
| | - Iris Simic
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, 2333 ZA, The Netherlands
| | - Niels Chavannes
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, 2333 ZA, The Netherlands
| | - Petra van Peet
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, 2333 ZA, The Netherlands
| | - Tobias Bonten
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, 2333 ZA, The Netherlands
| | - Rimke Vos
- Health Campus the Hague, Leiden University Medical Center, The Hague, 2511 DP, The Netherlands
| | - Hendrikus van Os
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, 2333 ZA, The Netherlands
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Gannamani R, Castela Forte J, Folkertsma P, Hermans S, Kumaraswamy S, van Dam S, Chavannes N, van Os H, Pijl H, Wolffenbuttel BHR. A Digitally Enabled Combined Lifestyle Intervention for Weight Loss: Pilot Study in a Dutch General Population Cohort. JMIR Form Res 2024; 8:e38891. [PMID: 38329792 PMCID: PMC10884913 DOI: 10.2196/38891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 05/04/2023] [Accepted: 09/25/2023] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND Overweight and obesity rates among the general population of the Netherlands keep increasing. Combined lifestyle interventions (CLIs) focused on physical activity, nutrition, sleep, and stress management can be effective in reducing weight and improving health behaviors. Currently available CLIs for weight loss (CLI-WLs) in the Netherlands consist of face-to-face and community-based sessions, which face scalability challenges. A digitally enabled CLI-WL with digital and human components may provide a solution for this challenge; however, the feasibility of such an intervention has not yet been assessed in the Netherlands. OBJECTIVE The aim of this study was two-fold: (1) to determine how weight and other secondary cardiometabolic outcomes (lipids and blood pressure) change over time in a Dutch population with overweight or obesity and cardiometabolic risk participating in a pilot digitally enabled CLI-WL and (2) to collect feedback from participants to guide the further development of future iterations of the intervention. METHODS Participants followed a 16-week digitally enabled lifestyle coaching program rooted in the Fogg Behavior Model, focused on nutrition, physical activity, and other health behaviors, from January 2020 to December 2021. Participants could access the digital app to register and track health behaviors, weight, and anthropometrics data at any time. We retrospectively analyzed changes in weight, blood pressure, and lipids for remeasured users. Surveys and semistructured interviews were conducted to assess critical positive and improvement points reported by participants and health care professionals. RESULTS Of the 420 participants evaluated at baseline, 53 participated in the pilot. Of these, 37 (70%) were classified as overweight and 16 (30%) had obesity. Mean weight loss of 4.2% occurred at a median of 10 months postintervention. The subpopulation with obesity (n=16) showed a 5.6% weight loss on average. Total cholesterol decreased by 10.2% and low-density lipoprotein cholesterol decreased by 12.9% on average. Systolic and diastolic blood pressure decreased by 3.5% and 7.5%, respectively. Participants identified the possibility of setting clear action plans to work toward and the multiple weekly touch points with coaches as two of the most positive and distinctive components of the digitally enabled intervention. Surveys and interviews demonstrated that the digital implementation of a CLI-WL is feasible and well-received by both participants and health care professionals. CONCLUSIONS Albeit preliminary, these findings suggest that a behavioral lifestyle program with a digital component can achieve greater weight loss than reported for currently available offline CLI-WLs. Thus, a digitally enabled CLI-WL is feasible and may be a scalable alternative to offline CLI-WL programs. Evidence from future studies in a Dutch population may help elucidate the mechanisms behind the effectiveness of a digitally enabled CLI-WL.
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Affiliation(s)
- Rahul Gannamani
- Ancora Health BV, Groningen, Netherlands
- Department of Neurology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - José Castela Forte
- Ancora Health BV, Groningen, Netherlands
- Department of Clinical Pharmacy and Pharmacology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Pytrik Folkertsma
- Ancora Health BV, Groningen, Netherlands
- Department of Endocrinology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | | | | | - Sipko van Dam
- Ancora Health BV, Groningen, Netherlands
- Department of Endocrinology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Niels Chavannes
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden University, Leiden, Netherlands
- National eHealth Living Lab, Leiden, Netherlands
| | - Hendrikus van Os
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden University, Leiden, Netherlands
- National eHealth Living Lab, Leiden, Netherlands
| | - Hanno Pijl
- Department of Endocrinology, Leiden University Medical Center, Leiden University, Leiden, Netherlands
| | - Bruce H R Wolffenbuttel
- Department of Endocrinology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
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Ramos LA, van Os H, Hilbert A, Olabarriaga SD, van der Lugt A, Roos YBWEM, van Zwam WH, van Walderveen MAA, Ernst M, Zwinderman AH, Strijkers GJ, Majoie CBLM, Wermer MJH, Marquering HA. Combination of Radiological and Clinical Baseline Data for Outcome Prediction of Patients With an Acute Ischemic Stroke. Front Neurol 2022; 13:809343. [PMID: 35432171 PMCID: PMC9010547 DOI: 10.3389/fneur.2022.809343] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background Accurate prediction of clinical outcome is of utmost importance for choices regarding the endovascular treatment (EVT) of acute stroke. Recent studies on the prediction modeling for stroke focused mostly on clinical characteristics and radiological scores available at baseline. Radiological images are composed of millions of voxels, and a lot of information can be lost when representing this information by a single value. Therefore, in this study we aimed at developing prediction models that take into account the whole imaging data combined with clinical data available at baseline. Methods We included 3,279 patients from the MR CLEAN Registry; a prospective, observational, multicenter registry of patients with ischemic stroke treated with EVT. We developed two approaches to combine the imaging data with the clinical data. The first approach was based on radiomics features, extracted from 70 atlas regions combined with the clinical data to train machine learning models. For the second approach, we trained 3D deep learning models using the whole images and the clinical data. Models trained with the clinical data only were compared with models trained with the combination of clinical and image data. Finally, we explored feature importance plots for the best models and identified many known variables and image features/brain regions that were relevant in the model decision process. Results From 3,279 patients included, 1,241 (37%) patients had a good functional outcome [modified Rankin Scale (mRS) ≤ 2] and 1,954 (60%) patients had good reperfusion [modified Thrombolysis in Cerebral Infarction (eTICI) ≥ 2b]. There was no significant improvement by combining the image data to the clinical data for mRS prediction [mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.81 vs. 0.80] above using the clinical data only, regardless of the approach used. Regarding predicting reperfusion, there was a significant improvement when image and clinical features were combined (mean AUC of 0.54 vs. 0.61), with the highest AUC obtained by the deep learning approach. Conclusions The combination of radiomics and deep learning image features with clinical data significantly improved the prediction of good reperfusion. The visualization of prediction feature importance showed both known and novel clinical and imaging features with predictive values.
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Affiliation(s)
- Lucas A. Ramos
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
- Department of Clinical Epidemiology and Data Science, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
- *Correspondence: Lucas A. Ramos
| | - Hendrikus van Os
- Department of Neurology, Leiden University Medical Center, Leiden, Netherlands
| | - Adam Hilbert
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
- CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Silvia D. Olabarriaga
- Department of Clinical Epidemiology and Data Science, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center (MC) - University Medical Center, Rotterdam, Netherlands
| | - Yvo B. W. E. M. Roos
- Department of Neurology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Wim H. van Zwam
- Department of Radiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, Netherlands
| | | | - Marielle Ernst
- Centre for Radiology and Endoscopy, Department of Diagnostic and Interventional Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Aeiko H. Zwinderman
- Department of Clinical Epidemiology and Data Science, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Gustav J. Strijkers
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Charles B. L. M. Majoie
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | | | - Henk A. Marquering
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
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