Remote Monitoring of Patient- and Family-Generated Health Data in Pediatrics.

In this article, we provide an overview of remote monitoring of pediatric PGHD and family-generated health data, including its current uses, future opportunities, and implementation resources.

Technological Advances in Clinical Definition and Surveillance Methodology for Surgical Site Infection Incorporating Surgical Site Imaging and Patient-Generated Health Data.

Background: Surgical site infection (SSI) continues to be a common and costly complication after surgery. The current commonly used definitions of SSI were devised more than two decades ago and do not take in to account more modern technology that could be used to make diagnosis more consistent and precise. Patient-generated health data (PGHD), including digital imaging, may be able to fulfill this objective. Methods: The published literature was examined to determine the current state of development in terms of using digital imaging as an aide to diagnose SSI. This information was used to devise possible methodology that could be used to integrate digital images to more objectively define SSI, as well as using these data for both surveillance activities and clinical management. Results: Digital imaging is a highly promising means to help define and diagnose SSI, particularly in remote settings. Multiple groups continue to actively study these emerging technologies, however, present methods remain based generally on subjective rather than objective observations. Although current images may be useful on a case-by-case basis, similar to physical examination information, integrating imaging in the definition of SSI to allow more automated diagnosis in the future will require complex image analysis combined with other available quantified data. Conclusions: Digital imaging technology, once adequately evolved, should become a cornerstone of the criteria for both the clinical and surveillance definitions of SSI.

Five-year trends in acetaminophen use exceeding the recommended daily maximum dose.

Temporal patterns of acetaminophen use exceeding the recommended daily maximum dosage of 4 g over a 5-year period (4/1/2011-3/31/2016) were evaluated in an online 1-week diary study of 14 434 adult acetaminophen users who also reported acetaminophen use in the previous month. Specific medications taken were identified by list-based prompting; respondents were not required to know their medications contained acetaminophen. Details of use were recorded daily; total daily dosage was determined programmatically. Prevalence of >4 g use over time was modelled and tested for linear changes. The overall prevalence of >4 g use (6.3% of users and 3.7% of usage days) did not change over the 5 years: odds ratio (OR) persons, 1.02 (95% CI, 0.98-1.09); OR days, 0.98 (0.92-1.05). Deviations from label directions were largely unchanged, though concomitant use increased slightly. Thus, over a recent 5-year period, there was no evidence of change in how often acetaminophen use exceeded the labelled maximum daily dose.

[Relevance of big data for molecular diagnostics]. / Bedeutung von Big Data für die molekulare Diagnostik.

Big data analysis raises the expectation that computerized algorithms may extract new knowledge from otherwise unmanageable vast data sets. What are the algorithms behind the big data discussion? In principle, high throughput technologies in molecular research already introduced big data and the development and application of analysis tools into the field of rheumatology some 15 years ago. This includes especially omics technologies, such as genomics, transcriptomics and cytomics. Some basic methods of data analysis are provided along with the technology, however, functional analysis and interpretation requires adaptation of existing or development of new software tools. For these steps, structuring and evaluating according to the biological context is extremely important and not only a mathematical problem. This aspect has to be considered much more for molecular big data than for those analyzed in health economy or epidemiology. Molecular data are structured in a first order determined by the applied technology and present quantitative characteristics that follow the principles of their biological nature. These biological dependencies have to be integrated into software solutions, which may require networks of molecular big data of the same or even different technologies in order to achieve cross-technology confirmation. More and more extensive recording of molecular processes also in individual patients are generating personal big data and require new strategies for management in order to develop data-driven individualized interpretation concepts. With this perspective in mind, translation of information derived from molecular big data will also require new specifications for education and professional competence.

[Big data in imaging]. / Big Data in der Bildgebung.

Until now, most major medical advancements have been achieved through hypothesis-driven research within the scope of clinical trials. However, due to a multitude of variables, only a certain number of research questions could be addressed during a single study, thus rendering these studies expensive and time consuming. Big data acquisition enables a new data-based approach in which large volumes of data can be used to investigate all variables, thus opening new horizons. Due to universal digitalization of the data as well as ever-improving hard- and software solutions, imaging would appear to be predestined for such analyses. Several small studies have already demonstrated that automated analysis algorithms and artificial intelligence can identify pathologies with high precision. Such automated systems would also seem well suited for rheumatology imaging, since a method for individualized risk stratification has long been sought for these patients. However, despite all the promising options, the heterogeneity of the data and highly complex regulations covering data protection in Germany would still render a big data solution for imaging difficult today. Overcoming these boundaries is challenging, but the enormous potential advances in clinical management and science render pursuit of this goal worthwhile.

[Big data from clinical routine]. / Big Data aus dem klinischen Alltag.

BACKGROUND: Over the past 100 years, evidence-based medicine has undergone several fundamental changes. Through the field of physiology, medical doctors were introduced to the natural sciences. Since the late 1940s, randomized and epidemiological studies have come to provide the evidence for medical practice, which led to the emergence of clinical epidemiology as a new field in the medical sciences. Within the past few years, big data has become the driving force behind the vision for having a comprehensive set of health-related data which tracks individual healthcare histories and consequently that of large populations. OBJECTIVES: The aim of this article is to discuss the implications of data-driven medicine, and to examine how it can find a place within clinical care. MATERIALS AND METHODS: The EU-wide discussion on the development of data-driven medicine is presented. RESULTS: The following features and suggested actions were identified: harmonizing data formats, data processing and analysis, data exchange, related legal frameworks and ethical challenges. For the effective development of data-driven medicine, pilot projects need to be conducted to allow for open and transparent discussion on the advantages and challenges. The Federal Ministry of Education and Research ("Bundesministerium für Bildung und Forschung," BMBF) Arthromark project is an important example. Another example is the Medical Informatics Initiative of the BMBF. DISCUSSION AND CONCLUSION: The digital revolution affects clinic practice. Data can be generated and stored in quantities that are almost unimaginable. It is possible to take advantage of this for development of a learning healthcare system if the principles of medical evidence generation are integrated into innovative IT-infrastructures and processes.

Improved Hand Function, Self-Rated Health, and Decreased Activity Limitations: Results After a Two-Month Hand Osteoarthritis Group Intervention.

OBJECTIVE: To evaluate the effects on hand function, activity limitations, and self-rated health of a primary care hand osteoarthritis (OA) group intervention. Hand OA causes pain, impaired mobility, and reduced grip force, which cause activity limitations. OA group interventions in primary care settings are sparsely reported. METHODS: Sixty-four individuals with hand OA agreed to participate; 15 were excluded due to not fulfilling the inclusion criteria. The 49 remaining (90% female) participated in an OA group intervention at a primary care unit with education, paraffin wax bath, and hand exercise over a 6-week period. Data were collected at baseline, end of intervention, and after 1 year. Instruments used were the Grip Ability Test (GAT), the Signals of Functional Impairment (SOFI), dynamometry (grip force), hand pain at rest using a visual analog scale (VAS), the Patient-Specific Functional Scale (PSFS), the Quick Disabilities of the Arm, Shoulder, and Hand (Quick-DASH), and the EuroQol VAS (EQ VAS). Data were analyzed using nonparametric statistics. RESULTS: Hand function, activity limitation, and self-rated health significantly improved from baseline to end of intervention, grip force (right hand: P < 0.001; left hand: P = 0.008), SOFI (P = 0.011), GAT (P < 0.001), hand pain at rest (P < 0.001), PSFS (1: P = 0.008, 2: P < 0.001, and 3: P = 0.004), Quick-DASH (P = 0.001), and EQ VAS (P = 0.039), and the effects were sustained after 1 year. CONCLUSION: The hand OA group intervention in primary care improves hand function, activity limitation, and self-rated health. The benefits are sustained 1 year after completion of the intervention.