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Improving inflammatory arthritis management through tighter monitoring of patients and the use of innovative electronic tools.

ABSTRACT: Treating to target by monitoring disease activity and adjusting therapy to attain remission or low disease activity has been shown to lead to improved outcomes in chronic rheumatic diseases such as rheumatoid arthritis and spondyloarthritis. Patient-reported outcomes, used in conjunction with clinical measures, add an important perspective of disease activity as perceived by the patient. Several validated PROs are available for inflammatory arthritis, and advances in electronic patient monitoring tools are helping patients with chronic diseases to self-monitor and assess their symptoms and health. Frequent patient monitoring could potentially lead to the early identification of disease flares or adverse events, early intervention for patients who may require treatment adaptation, and possibly reduced appointment frequency for those with stable disease. A literature search was conducted to evaluate the potential role of patient self-monitoring and innovative monitoring of tools in optimising disease control in inflammatory arthritis. Experience from the treatment of congestive heart failure, diabetes and hypertension shows improved outcomes with remote electronic self-monitoring by patients. In inflammatory arthritis, electronic self-monitoring has been shown to be feasible in patients despite manual disability and to be acceptable to older patients. Patients' self-assessment of disease activity using such methods correlates well with disease activity assessed by rheumatologists. This review also describes several remote monitoring tools that are being developed and used in inflammatory arthritis, offering the potential to improve disease management and reduce pressure on specialists.

SUBMITTER: van Riel P

PROVIDER: S-EPMC5133416 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Improving inflammatory arthritis management through tighter monitoring of patients and the use of innovative electronic tools.

van Riel Piet P Alten Rieke R Combe Bernard B Abdulganieva Diana D Bousquet Paola P Courtenay Molly M Curiale Cinzia C Gómez-Centeno Antonio A Haugeberg Glenn G Leeb Burkhard B Puolakka Kari K Ravelli Angelo A Rintelen Bernhard B Sarzi-Puttini Piercarlo P

RMD open 20161124 2

Treating to target by monitoring disease activity and adjusting therapy to attain remission or low disease activity has been shown to lead to improved outcomes in chronic rheumatic diseases such as rheumatoid arthritis and spondyloarthritis. Patient-reported outcomes, used in conjunction with clinical measures, add an important perspective of disease activity as perceived by the patient. Several validated PROs are available for inflammatory arthritis, and advances in electronic patient monitorin ...[more]

PMID: 27933206

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Project description:BACKGROUND:Electronic health (eHealth) tools are becoming increasingly popular for helping patients' self-manage chronic conditions. Little research, however, has examined the effect of patients using eHealth tools to self-report their medication management and use. Similarly, there is little evidence showing how eHealth tools might prompt patients and health care providers to make appropriate changes to medication use. OBJECTIVE:The objective of this systematic review was to determine the impact of patients' use of eHealth tools on self-reporting adverse effects and symptoms that promote changes to medication use. Related secondary outcomes were also evaluated. METHODS:MEDLINE, EMBASE, and CINAHL were searched from January 1, 2000, to April 25, 2018. Reference lists of relevant systematic reviews and included articles from the literature search were also screened to identify relevant studies. Title, abstract, and full-text review as well as data extraction and risk of bias assessment were performed independently by 2 reviewers. Due to high heterogeneity, results were not meta-analyzed and instead presented as a narrative synthesis. RESULTS:A total of 14 studies, including 13 randomized controlled trials (RCTs) and 1 open-label intervention, were included, from which 11 unique eHealth tools were identified. In addition, 14 RCTs found statistically significant increases in positive medication changes as a result of using eHealth tools, as did the single open-label study. Moreover, 8 RCTs found improvement in patient symptoms following eHealth tool use, especially in adolescent asthma patients. Furthermore, 3 RCTs showed that eHealth tools might improve patient self-efficacy and self-management of chronic disease. Little or no evidence was found to support the effectiveness of eHealth tools at improving medication recommendations and reconciliation by clinicians, medication-use behavior, health service utilization, adverse effects, quality of life, or patient satisfaction. eHealth tools with multifaceted functionalities and those allowing direct patient-provider communication may be more effective at improving patient self-management and self-efficacy. CONCLUSIONS:Evidence suggests that the use of eHealth tools may improve patient symptoms and lead to medication changes. Patients generally found eHealth tools useful in improving communication with health care providers. Moreover, health-related outcomes among frequent eHealth tool users improved in comparison with individuals who did not use eHealth tools frequently. Implementation issues such as poor patient engagement and poor clinician workflow integration were identified. More high-quality research is needed to explore how eHealth tools can be used to effectively manage use of medications to improve medication management and patient outcomes.

Project description:BackgroundElectronic health record (EHR)-based clinical decision support tools can improve the use of evidence-based clinical guidelines for preeclampsia management that can reduce maternal mortality and morbidity. No study has investigated the organizational capabilities that enable hospitals to use EHR-based decision support tools to manage preeclampsia.ObjectiveTo examine the association of organizational capabilities and hospital adoption of EHR-based decision support tools for preeclampsia management.MethodsCross-sectional analyses of hospitals providing obstetric care in 2017. In total, 739 hospitals responded to the 2017-2018 National Survey of Healthcare Organizations and Systems (NSHOS) and were linked to the 2017 American Hospital Association (AHA) Annual Survey Database and the Area Health Resources File (AHRF). A total of 425 hospitals providing obstetric care across 49 states were included in the analysis. The main outcome was whether a hospital adopted EHR-based clinical decision support tools for preeclampsia management. Hospital organizational capabilities assessed as predictors include EHR functions, adoption of evidence-based clinical treatments, use of quality improvement methods, and dissemination processes to share best patient care practices. Logistic regression estimated the association of hospital organizational capabilities and hospital adoption of EHR-based decision support tools to manage preeclampsia, controlling for hospital structural and patient sociodemographic characteristics.ResultsTwo-thirds of the hospitals (68%) adopted EHR-based decision support tools for preeclampsia, and slightly more than half (56%) of hospitals had a single EHR system. Multivariable regression results indicate that hospitals with a single EHR system were more likely to adopt EHR-based decision support tools for preeclampsia (17.4 percentage points; 95% CI, 1.9 to 33.0; P < .05) than hospitals with a mixture of EHR and paper-based systems. Compared with hospitals having multiple EHRs, on average, hospitals having a single EHR were also more likely to adopt the tools by 9.3 percentage points, but the difference was not statistically significant (95% CI, -1.3 to 19.9). Hospitals with more processes to aid dissemination of best patient care practices were also more likely to adopt EHR-based decision-support tools for preeclampsia (0.4 percentage points; 95% CI, 0.1 to 0.6, for every 1-unit increase in dissemination processes; P < .01).ConclusionStandardized EHRs and policies to disseminate evidence are foundational hospital capabilities that can help advance the use of EHR-based decision support tools for preeclampsia management in the approximately one-third of US hospitals that still do not use them.

Project description:BackgroundThe current classification for acute kidney injury (AKI) according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria integrates both serum creatinine (SCr) and urine output (UO). Most reports on AKI claim to use KDIGO guidelines but fail to include the UO criterion. It has been shown that patients who had intensive UO monitoring, with or without AKI, had significantly less cumulative fluid volume and fluid overload, reduced vasopressor use, and improved 30-day mortality. We examined whether real-time monitoring of this simple, sensitive, and easy-to-use biomarker in the ICU led to more appropriate intervention by healthcare providers and better outcomes. METHODS: RenalSense Clarity RMS Consoles were installed in the General ICU at the Hadassah Medical Center, Israel, from December 2019 to November 2020. The Clarity RMS system continuously and electronically monitors UO in real-time. 100 patients were randomly selected from this period as the study group (UOelec) and compared to a matched control group (UOmanual) from the same period two years earlier. To test whether there was an association between oliguric hours and fluid treatment in each group, the correlation was calculated and analyzed for each of the different UO monitoring methods.ResultsTherapeutic intervention: The correlation of the sum of all oliguric hours on Day 1 and 2 with the sum of any therapeutic intervention (fluid bolus or furosemide) showed a significant correlation for the study group UOelec (P = 0.017). The matched control group UOmanual showed no such correlation (P = 0.932). Length of Stay (LOS): Median LOS [IQR] in the ICU of UOelec versus UOmanual was 69.46 [44.7, 125.9] hours and 116.5 [62.46, 281.3] hours, respectively (P = 0.0002).ConclusionsThe results of our study strongly suggest that ICU patients had more meaningful and better medical intervention, and improved outcomes, with electronic UO monitoring than with manual monitoring.

Project description:BackgroundClinical decision support (CDS) tools in electronic health records (EHRs) are often used as core strategies to support quality improvement programs in the clinical setting. Monitoring the impact (intended and unintended) of these tools is crucial for program evaluation and adaptation. Existing approaches for monitoring typically rely on health care providers' self-reports or direct observation of clinical workflows, which require substantial data collection efforts and are prone to reporting bias.ObjectiveThis study aims to develop a novel monitoring method leveraging EHR activity data and demonstrate its use in monitoring the CDS tools implemented by a tobacco cessation program sponsored by the National Cancer Institute's Cancer Center Cessation Initiative (C3I).MethodsWe developed EHR-based metrics to monitor the implementation of two CDS tools: (1) a screening alert reminding clinic staff to complete the smoking assessment and (2) a support alert prompting health care providers to discuss support and treatment options, including referral to a cessation clinic. Using EHR activity data, we measured the completion (encounter-level alert completion rate) and burden (the number of times an alert was fired before completion and time spent handling the alert) of the CDS tools. We report metrics tracked for 12 months post implementation, comparing 7 cancer clinics (2 clinics implemented the screening alert and 5 implemented both alerts) within a C3I center, and identify areas to improve alert design and adoption.ResultsThe screening alert fired in 5121 encounters during the 12 months post implementation. The encounter-level alert completion rate (clinic staff acknowledged completion of screening in EHR: 0.55; clinic staff completed EHR documentation of screening results: 0.32) remained stable over time but varied considerably across clinics. The support alert fired in 1074 encounters during the 12 months. Providers acted upon (ie, not postponed) the support alert in 87.3% (n=938) of encounters, identified a patient ready to quit in 12% (n=129) of encounters, and ordered a referral to the cessation clinic in 2% (n=22) of encounters. With respect to alert burden, on average, both alerts fired over 2 times (screening alert: 2.7; support alert: 2.1) before completion; time spent postponing the screening alert was similar to completing (52 vs 53 seconds) the alert, and time spent postponing the support alert was more than completing (67 vs 50 seconds) the alert per encounter. These findings inform four areas where the alert design and use can be improved: (1) improving alert adoption and completion through local adaptation, (2) improving support alert efficacy by additional strategies including training in provider-patient communication, (3) improving the accuracy of tracking for alert completion, and (4) balancing alert efficacy with the burden.ConclusionsEHR activity metrics were able to monitor the success and burden of tobacco cessation alerts, allowing for a more nuanced understanding of potential trade-offs associated with alert implementation. These metrics can be used to guide implementation adaptation and are scalable across diverse settings.

Project description:BACKGROUND:The increase in life expectancy and recent advancements in technology and medical science have changed the way we deliver health services to the aging societies. Evidence suggests that home telemonitoring can significantly decrease the number of readmissions, and continuous monitoring of older adults' daily activities and health-related issues might prevent medical emergencies. OBJECTIVE:The primary objective of this review was to identify advances in assistive technology devices for seniors and aging-in-place technology and to determine the level of evidence for research on remote patient monitoring, smart homes, telecare, and artificially intelligent monitoring systems. METHODS:A literature review was conducted using Cumulative Index to Nursing and Allied Health Literature Plus, MEDLINE, EMBASE, Institute of Electrical and Electronics Engineers Xplore, ProQuest Central, Scopus, and Science Direct. Publications related to older people's care, independent living, and novel assistive technologies were included in the study. RESULTS:A total of 91 publications met the inclusion criteria. In total, four themes emerged from the data: technology acceptance and readiness, novel patient monitoring and smart home technologies, intelligent algorithm and software engineering, and robotics technologies. The results revealed that most studies had poor reference standards without an explicit critical appraisal. CONCLUSIONS:The use of ubiquitous in-home monitoring and smart technologies for aged people's care will increase their independence and the health care services available to them as well as improve frail elderly people's health care outcomes. This review identified four different themes that require different conceptual approaches to solution development. Although the engineering teams were focused on prototype and algorithm development, the medical science teams were concentrated on outcome research. We also identified the need to develop custom technology solutions for different aging societies. The convergence of medicine and informatics could lead to the development of new interdisciplinary research models and new assistive products for the care of older adults.

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Improving inflammatory arthritis management through tighter monitoring of patients and the use of innovative electronic tools.

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Improving inflammatory arthritis management through tighter monitoring of patients and the use of innovative electronic tools.

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