Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review

BACKGROUND

Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care.

OBJECTIVE

This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings.

METHODS

The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O'Malley's methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria.

RESULTS

In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth.

CONCLUSIONS

All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.

Big Data in cardiac surgery: real world and perspectives

Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.

Sequential Pattern Mining of Longitudinal Adverse Events After Left Ventricular Assist Device Implant

Left ventricular assist devices (LVADs) are an increasingly common therapy for patients with advanced heart failure. However, implantation of the LVAD increases the risk of stroke, infection, bleeding, and other serious adverse events (AEs). Most post-LVAD AEs studies have focused on individual AEs in isolation, neglecting the possible interrelation, or causality between AEs. This study is the first to conduct an exploratory analysis to discover common sequential chains of AEs following LVAD implantation that are correlated with important clinical outcomes. This analysis was derived from 58,575 recorded AEs for 13,192 patients in International Registry for Mechanical Circulatory Support (INTERMACS) who received a continuous-flow LVAD between 2006 and 2015. The pattern mining procedure involved three main steps: (1) creating a bank of AE sequences by converting the AEs for each patient into a single, chronologically sequenced record, (2) grouping patients with similar AE sequences using hierarchical clustering, and (3) extracting temporal chains of AEs for each group of patients using Markov modeling. The mined results indicate the existence of seven groups of sequential chains of AEs, characterized by common types of AEs that occurred in a unique order. The groups were identified as: GRP1: Recurrent bleeding, GRP2: Trajectory of device malfunction & explant, GRP3: Infection, GRP4: Trajectories to transplant, GRP5: Cardiac arrhythmia, GRP6: Trajectory of neurological dysfunction & death, and GRP7: Trajectory of respiratory failure, renal dysfunction & death. These patterns of sequential post-LVAD AEs disclose potential interdependence between AEs and may aid prediction, and prevention, of subsequent AEs in future studies.

Effects of an e-Prescribing interface redesign on rates of generic drug prescribing: exploiting default options

OBJECTIVE

Increasing the use of generic medications could help control medical costs. However, educational interventions have limited impact on prescriber behavior, and e-prescribing alerts are associated with high override rates and alert fatigue. Our objective was to evaluate the effect of a less intrusive intervention, a redesign of an e-prescribing interface that provides default options intended to "nudge" prescribers towards prescribing generic drugs.

METHODS

This retrospective cohort study in an academic ambulatory multispecialty practice assessed the effects of customizing an e-prescribing interface to substitute generic equivalents for brand-name medications during order entry and allow a one-click override to order the brand-name medication.

RESULTS

Among drugs with generic equivalents, the proportion of generic drugs prescribed more than doubled after the interface redesign, rising abruptly from 39.7% to 95.9% (a 56.2% increase; 95% confidence interval, 56.0-56.4%; P < .001). Before the redesign, generic drug prescribing rates varied by therapeutic class, with rates as low as 8.6% for genitourinary products and 15.7% for neuromuscular drugs. After the redesign, generic drug prescribing rates for all but four therapeutic classes were above 90%: endocrine drugs, neuromuscular drugs, nutritional products, and miscellaneous products.

DISCUSSION

Changing the default option in an e-prescribing interface in an ambulatory care setting was followed by large and sustained increases in the proportion of generic drugs prescribed at the practice.

CONCLUSIONS

Default options in health information technology exert a powerful effect on user behavior, an effect that can be leveraged to optimize decision making.

Prediction of time dependent survival in HF patients after VAD implantation using pre- and post-operative data

Heart failure is one of the most common diseases worldwide. In recent years, Ventricular Assist Devices (VADs) have become a valuable option for patients with advanced HF. Although it has been shown that VADs improve patient survival rates, several complications persist during left VAD (LVAD) support. The stratification scores currently employed are mostly generic, i.e. not specifically built for LVAD patients, and are based on pre-implantation patient data. In this work we apply data mining approaches for the prediction of time dependent survival in patients after LVAD implantation. Moreover, the predictions acquired with the use of pre-implantation data are enriched by employing post-implantation data, i.e. follow-up data. Different clinical scenarios have been depicted and the subsequent conditions are tested in order to identify the optimal set of pre- and post-implant features, as well as the most suitable algorithms for feature selection and prediction. The proposed approach is applied to a real dataset of 71 patients, reporting an accuracy of 84.5%, sensitivity of 87% and specificity of 82%. Based on the reported results, expert cardio-surgeons can be supported in planning the treatment of VAD patients.

A dynamic Bayesian network approach for time-specific survival probability prediction in patients after ventricular assist device implantation

In this work we present a decision support tool for the calculation of time-dependent survival probability for patients after ventricular assist device implantation. Two different models have been developed, a short term one which predicts survival for the first three months and a long term one that predicts survival for one year after implantation. In order to model the time dependencies between the different time slices of the problem, a dynamic Bayesian network (DBN) approach has been employed. DBNs order to capture the temporal events of the patient disease and the temporal data availability. High accuracy results have been reported for both models. The short and long term DBNs reached an accuracy of 96.97% and 93.55% respectively.