Personalised mobile services supporting the implementation of clinical guidelines

Telemonitoring is emerging as a compelling application of Body Area Networks (BANs). We describe two health BAN systems developed respectively by a European team and an Australian team and discuss some issues encountered relating to formalization of clinical knowledge to support real-time analysis and interpretation of BAN data. Our example application is an evidence-based telemonitoring and teletreatment application for home-based rehabilitation. The application is intended to support implementation of a clinical guideline for cardiac rehabilitation following myocardial infarction. In addition to this the proposal is to establish the patient’s individual baseline risk profile and, by real-time analysis of BAN data, continually re-assess the current risk level in order to give timely personalised feedback. Static and dynamic risk factors are derived from literature. Many sources express evidence probabilistically, suggesting a requirement for reasoning with uncertainty; elsewhere evidence requires qualitative reasoning: both familiar modes of reasoning in KBSs. However even at this knowledge acquisition stage some issues arise concerning how best to apply the clinical evidence. Furthermore, in cases where insufficient clinical evidence is currently available, telemonitoring can yield large collections of clinical data with the potential for data mining in order to furnish more statistically powerful and accurate clinical evidence

Mobihealth: mobile health services based on body area networks

In this chapter we describe the concept of MobiHealth and the approach developed during the MobiHealth project (MobiHealth, 2002). The concept was to bring together the technologies of Body Area Networks (BANs), wireless broadband communications and wearable medical devices to provide mobile healthcare services for patients and health professionals. These technologies enable remote patient care services such as management of chronic conditions and detection of health emergencies. Because the patient is free to move anywhere whilst wearing the MobiHealth BAN, patient mobility is maximised. The vision is that patients can enjoy enhanced freedom and quality of life through avoidance or reduction of hospital stays. For the health services it means that pressure on overstretched hospital services can be alleviated

Early indication of decompensated heart failure in patients on home-telemonitoring: a comparison of prediction algorithms based on daily weight and noninvasive transthoracic bio-impedance

Background: Heart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation. Objective: In this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations. Methods: We monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM). Results: Algorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines. Conclusions: NITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation

Postoperative Remote Automated Monitoring:Need for and State of the Science

Worldwide, more than 230 million adults have major noncardiac surgery each year. Although surgery can improve quality and duration of life, it can also precipitate major complications. Moreover, a substantial proportion of deaths occur after discharge. Current systems for monitoring patients postoperatively, on surgical wards and after transition to home, are inadequate. On the surgical ward, vital signs evaluation usually occurs only every 4-8 hours. Reduced in-hospital ward monitoring, followed by no vital signs monitoring at home, leads to thousands of cases of undetected/delayed detection of hemodynamic compromise. In this article we review work to date on postoperative remote automated monitoring on surgical wards and strategy for advancing this field. Key considerations for overcoming current barriers to implementing remote automated monitoring in Canada are also presented

REveAL™ and CARElink™ (Real Care): Minimising the time taken to achieve a diagnosis in the implantable loop recorder population

Introduction Syncope accounts for ≈ 2.7/1000 population/year of presentations to UK healthcare, a figure believed to be underestimated by up to 30% due to misdiagnosis. For some patients the cause of their episode/s may remain unexplained. The implantable loop recorder (ILR) is effective for diagnosis of syncope and palpitations, with UK and European guidelines advising its use if symptoms are infrequent. However current follow-up regimes can lead to a slow diagnostic pathway for patients. Remote monitoring technology allows patients to send their ILR data to their clinic Research Questions 1) Does remote monitoring of ILRs reduce time to diagnosis and/or increase diagnostic yield? 2) What is the impact of remote monitoring on logged events requiring analysis? Method New ILR patients at a single implanting centre were recruited. Following informed consent, they were randomised into control or experimental groups. Patients in the control group were reviewed in the conventional manner with routine 6 monthly follow-ups plus additional ad hoc checks if symptoms occurred. Patients in the experimental group were asked to send transmissions fortnightly or following a symptom. All recordings were reviewed and classified as true or false events according to pre-defined criteria. Significant true event ECGs were reviewed blindly by a cardiologist. All data were verified by two physiologists or a physiologist and a cardiologist prior to analysis. The primary outcome variable was median time to clinical diagnosis. Results 37 patients were randomised, 19 to the control and 18 to the experimental group. The control group comprised 11 males and 8 females with a median age of 60 (36-86) years. The experimental group comprised 10 males and 8 females, median age 58 (36-84) years. Mann-Whitney U testing showed no significant differences in group demographics. Following randomisation 5526 events were logged, 1264 in the control and 4262 in the experimental group. 28 (76%) of patients had a true event, which led to a diagnosis in 23 (67%) of patients. There were 13 patients with true events and 10 diagnoses in the experimental group, with 15 true events and 13 diagnoses in the control group. Asystole was the most common event that led to a diagnosis, accounting for 35% of diagnoses. Kaplan-Meier analysis was used to assess the primary outcomes of time from event to follow-up, and time to clinical diagnosis. Compared to the control group, the median time from event to follow-up was reduced from 3 to 1 week (p=0.004). Median time to diagnosis was reduced from 13 to 6 weeks (p=0.049) when remote monitoring was used. Conclusion In patients with ILR, remote monitoring significantly reduced diagnostic delay although the overall diagnostic yield was not increased. However remote monitoring resulted in a three-fold increase in logged events that required analysis with only 1 in 328 proving to be true events: this will have significant resource implications

The Impact of Consumer Smart Device Platforms on Illness Uncertainty and Anxiety in Patients with Atrial Fibrillation

Atrial fibrillation (AFib) is a common cardiac arrhythmia associated with increased risk for comorbid health conditions. Advancements in consumer technology have enabled patients to monitor hearth rhythm independently, yet, much remains unknown about patient outcomes related to the use of these smart device platforms (SDP). The aim of this study was to examine the iatrogenic and/or remedial effects of SDP use on patient reported outcomes of illness uncertainty, cardiac anxiety, body vigilance, AFib symptoms, symptom burden, and healthcare utilization. The sample included 130 AFib participants (65 in SDP group) recruited through ResearchMatch, American Heart Association support forum, and other online AFib communities. Despite being of younger age, participants in the SDP group reported more medical risk factors associated with AFib. Results partially supported the iatrogenic effect, as participants with SDP reported greater cardiac anxiety and healthcare utilization relative to those without, even after accounting for covariates of age and medical risk factors. These findings should be interpreted with caution, as the global pandemic may have impacted the results obtained

Technology applications

A summary of NASA Technology Utilization programs for the period of 1 December 1971 through 31 May 1972 is presented. An abbreviated description of the overall Technology Utilization Applications Program is provided as a background for the specific applications examples. Subjects discussed are in the broad headings of: (1) cancer, (2) cardiovascular disease, (2) medical instrumentation, (4) urinary system disorders, (5) rehabilitation medicine, (6) air and water pollution, (7) housing and urban construction, (8) fire safety, (9) law enforcement and criminalistics, (10) transportation, and (11) mine safety

Patient Monitoring Systems

book chapterBiomedical Informatic