Assessment of the many faces of PsA: single and composite measures in PsA clinical trials.

PsA is a complex, heterogeneous disease that can place a large burden on patients' psychological and physical well-being. The multifaceted nature of PsA poses a significant assessment challenge, both in randomized control trials and in clinical practice. In recent years, there has been much progress in the development of unidimensional and composite measures of disease activity, as well as of questionnaires that capture the patient's perspective of the condition. Despite these advances, there remains uncertainty around which tools to implement within a research setting. This review aims to summarize the currently available clinical and patient-derived assessment tools, providing a practical and informative resource for the assessment of PsA. This review will also explore recent advancements in digital approaches to the assessment of rheumatological conditions. This will highlight the potential for digitalization in the assessment and monitoring of PsA, outlining innovative means of capturing disease activity and treatment response.

Impact of a computerized decision support tool deployed in two intensive care units on acute kidney injury progression and guideline compliance: a prospective observational study.

BACKGROUND: Acute kidney injury (AKI) affects a large proportion of the critically ill and is associated with worse patient outcomes. Early identification of AKI can lead to earlier initiation of supportive therapy and better management. In this study, we evaluate the impact of computerized AKI decision support tool integrated with the critical care clinical information system (CCIS) on patient outcomes. Specifically, we hypothesize that integration of AKI guidelines into CCIS will decrease the proportion of patients with Stage 1 AKI deteriorating into higher stages of AKI. METHODS: The study was conducted in two intensive care units (ICUs) at University Hospitals Bristol, UK, in a before (control) and after (intervention) format. The intervention consisted of the AKIN guidelines and AKI care bundle which included guidance for medication usage, AKI advisory and dashboard with AKI score. Clinical data and patient outcomes were collected from all patients admitted to the units. AKI stage was calculated using the Acute Kidney Injury Network (AKIN) guidelines. Maximum AKI stage per admission, change in AKI stage and other metrics were calculated for the cohort. Adherence to eGFR-based enoxaparin dosing guidelines was evaluated as a proxy for clinician awareness of AKI. RESULTS: Each phase of the study lasted a year, and a total of 5044 admissions were included for analysis with equal numbers of patients for the control and intervention stages. The proportion of patients worsening from Stage 1 AKI decreased from 42% (control) to 33.5% (intervention), p = 0.002. The proportion of incorrect enoxaparin doses decreased from 1.72% (control) to 0.6% (intervention), p < 0.001. The prevalence of any AKI decreased from 43.1% (control) to 37.5% (intervention), p < 0.05. CONCLUSIONS: This observational study demonstrated a significant reduction in AKI progression from Stage 1 and a reduction in overall development of AKI. In addition, a reduction in incorrect enoxaparin dosing was also observed, indicating increased clinical awareness. This study demonstrates that AKI guidelines coupled with a newly designed AKI care bundle integrated into CCIS can impact patient outcomes positively.

Innovation in surgery/operating room driven by Internet of Things on medical devices.

BACKGROUND: With the improvement of sensor technology, the trend of Internet of Things (IoT) is affecting the medical devices. The aim of this study is to verify whether it is possible to "visualize instrument usage in specific procedures" by automatically accumulating the digital data related to the behavior of surgical instruments/forceps in laparoscopic surgery. METHODS: Five board-certified surgeons (PGY 9-24 years) performed laparoscopic cholecystectomy on 35-kg porcine (n = 5). Radio frequency identifier (RFID) was attached to each forceps with RFID readers installed on the left/right of the operating table. We automatically recorded the behavior by tracking the operator's right/left hands' forceps with RFID. The output sensor was installed in the electrocautery circuit for automatic recordings of the ON/OFF times and the activation time. All data were collected in dedicated software and used for analysis. RESULTS: In all cases, the behaviors of forceps and electrocautery were successfully recorded. The median operation time was 1828 s (range 1159-2962 s), of which the electrocautery probe was the longest held on the right hand (1179 s, 75%), followed by Maryland dissectors (149 s, 10%), then clip appliers (91 s, 2%). In contrast, grasping forceps were mainly used in the left hand (1780 s, 93%). The activation time of electrocautery was only 8% of the total use and the remaining was mainly used for dissection. These situations were seen in common by all operators, but as a mentor surgeon, there was a tendency to change the right hand's instruments more frequently. The median activation time of electrocautery was 0.41 s, and these were confirmed to be 0.14-0.57 s among the operators. CONCLUSION: By utilization of IoT for surgery, surgical procedure could be "visualized." This will improve the safety on surgery such as optimal usage of surgical devices, proper use of electrocautery, and standardization of the surgical procedures.

Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery.

Standardized clinical pathways are useful tool to reduce variation in clinical management and may improve quality of care. However the evidence supporting a specific clinical pathway for a patient or patient population is often imperfect limiting adoption and efficacy of clinical pathway. Machine intelligence can potentially identify clinical variation and may provide useful insights to create and optimize clinical pathways. In this quality improvement project we analyzed the inpatient care of 1786 patients undergoing colorectal surgery from 2015 to 2016 across multiple Ohio hospitals in the Cleveland Clinic System. Data from four information subsystems was loaded in the Clinical Variation Management (CVM) application (Ayasdi, Inc., Menlo Park, CA). The CVM application uses machine intelligence and topological data analysis methods to identify groups of similar patients based on the treatment received. We defined "favorable performance" as groups with lower direct variable cost, lower length of stay, and lower 30-day readmissions. The software auto-generated 9 distinct groups of patients based on similarity analysis. Overall, favorable performance was seen with ketorolac use, lower intra-operative fluid use (< 2000 cc) and surgery for cancer. Multiple sub-groups were easily created and analyzed. Adherence reporting tools were easy to use enabling almost real time monitoring. Machine intelligence provided useful insights to create and monitor care pathways with several advantages over traditional analytic approaches including: (1) analysis across disparate data sets, (2) unsupervised discovery, (3) speed and auto-generation of clinical pathways, (4) ease of use by team members, and (5) adherence reporting.

Secure count query on encrypted genomic data.

Human genomic information can yield more effective healthcare by guiding medical decisions. Therefore, genomics research is gaining popularity as it can identify potential correlations between a disease and a certain gene, which improves the safety and efficacy of drug treatment and can also develop more effective prevention strategies [1]. To reduce the sampling error and to increase the statistical accuracy of this type of research projects, data from different sources need to be brought together since a single organization does not necessarily possess required amount of data. In this case, data sharing among multiple organizations must satisfy strict policies (for instance, HIPAA and PIPEDA) that have been enforced to regulate privacy-sensitive data sharing. Storage and computation on the shared data can be outsourced to a third party cloud service provider, equipped with enormous storage and computation resources. However, outsourcing data to a third party is associated with a potential risk of privacy violation of the participants, whose genomic sequence or clinical profile is used in these studies. In this article, we propose a method for secure sharing and computation on genomic data in a semi-honest cloud server. In particular, there are two main contributions. Firstly, the proposed method can handle biomedical data containing both genotype and phenotype. Secondly, our proposed index tree scheme reduces the computational overhead significantly for executing secure count query operation. In our proposed method, the confidentiality of shared data is ensured through encryption, while making the entire computation process efficient and scalable for cutting-edge biomedical applications. We evaluated our proposed method in terms of efficiency on a database of Single-Nucleotide Polymorphism (SNP) sequences, and experimental results demonstrate that the execution time for a query of 50 SNPs in a database of 50,000 records is approximately 5 s, where each record contains 500 SNPs. And, it requires 69.7 s to execute the query on the same database that also includes phenotypes.

Informatics for the Modern Intensive Care Unit.

Advanced informatics systems can help improve health care delivery and the environment of care for critically ill patients. However, identifying, testing, and deploying advanced informatics systems can be quite challenging. These processes often require involvement from a collaborative group of health care professionals of varied disciplines with knowledge of the complexities related to designing the modern and "smart" intensive care unit (ICU). In this article, we explore the connectivity environment within the ICU, middleware technologies to address a host of patient care initiatives, and the core informatics concepts necessary for both the design and implementation of advanced informatics systems.

A user-centered model for designing consumer mobile health (mHealth) applications (apps).

BACKGROUND: Mobile technologies are a useful platform for the delivery of health behavior interventions. Yet little work has been done to create a rigorous and standardized process for the design of mobile health (mHealth) apps. This project sought to explore the use of the Information Systems Research (ISR) framework as guide for the design of mHealth apps. METHODS: Our work was guided by the ISR framework which is comprised of 3 cycles: Relevance, Rigor and Design. In the Relevance cycle, we conducted 5 focus groups with 33 targeted end-users. In the Rigor cycle, we performed a review to identify technology-based interventions for meeting the health prevention needs of our target population. In the Design Cycle, we employed usability evaluation methods to iteratively develop and refine mock-ups for a mHealth app. RESULTS: Through an iterative process, we identified barriers and facilitators to the use of mHealth technology for HIV prevention for high-risk MSM, developed 'use cases' and identified relevant functional content and features for inclusion in a design document to guide future app development. Findings from our work support the use of the ISR framework as a guide for designing future mHealth apps. DISCUSSION: Results from this work provide detailed descriptions of the user-centered design and system development and have heuristic value for those venturing into the area of technology-based intervention work. Findings from this study support the use of the ISR framework as a guide for future mHealth app development. CONCLUSION: Use of the ISR framework is a potentially useful approach for the design of a mobile app that incorporates end-users' design preferences.

Use of an appendicitis medical information sheet in the pediatric primary care system.

BACKGROUND: Accurate and prompt diagnosis is required for the primary evaluation of pediatric appendicitis. Among pediatricians and surgeons working in Yamanashi Prefecture, the pediatric appendicitis medical information (PAMI) sheet was edited in April 2011 to reflect the diagnostic results of the pediatric primary and emergency medical service and used as a referral document for surgical consultation to secondary hospitals. METHODS: The PAMI sheet consisted of sections for history taking, symptoms, physical signs and laboratory findings without a scoring system. For 32 consecutive months starting in April 2011, 59 patients hospitalized for suspected appendicitis were retrospectively reviewed. In particular, a total of 17 referral patients evaluated with the PAMI sheet were assessed in order to evaluate the utility of the form. RESULTS: The pediatric surgeons were able to easily determine patient condition from the PAMI sheet. In total, 13 of 17 patients had appendicitis. According to the physical findings of the 17 studied patients, the judgment of right lower quadrant tenderness (κ = 0.63) and guarding (κ = 1.00) was consistent between the pediatric surgeons and primary attending pediatricians. CONCLUSIONS: The PAMI sheet aids in the collection of detailed history and objective data with a high level of accuracy, and provides useful referral diagnostic information to the secondary-level hospitals.

Clinical microbiology informatics.

The clinical microbiology laboratory has responsibilities ranging from characterizing the causative agent in a patient's infection to helping detect global disease outbreaks. All of these processes are increasingly becoming partnered more intimately with informatics. Effective application of informatics tools can increase the accuracy, timeliness, and completeness of microbiology testing while decreasing the laboratory workload, which can lead to optimized laboratory workflow and decreased costs. Informatics is poised to be increasingly relevant in clinical microbiology, with the advent of total laboratory automation, complex instrument interfaces, electronic health records, clinical decision support tools, and the clinical implementation of microbial genome sequencing. This review discusses the diverse informatics aspects that are relevant to the clinical microbiology laboratory, including the following: the microbiology laboratory information system, decision support tools, expert systems, instrument interfaces, total laboratory automation, telemicrobiology, automated image analysis, nucleic acid sequence databases, electronic reporting of infectious agents to public health agencies, and disease outbreak surveillance. The breadth and utility of informatics tools used in clinical microbiology have made them indispensable to contemporary clinical and laboratory practice. Continued advances in technology and development of these informatics tools will further improve patient and public health care in the future.

Comparing image search behaviour in the ARRS GoldMiner search engine and a clinical PACS/RIS.

Information search has changed the way we manage knowledge and the ubiquity of information access has made search a frequent activity, whether via Internet search engines or increasingly via mobile devices. Medical information search is in this respect no different and much research has been devoted to analyzing the way in which physicians aim to access information. Medical image search is a much smaller domain but has gained much attention as it has different characteristics than search for text documents. While web search log files have been analysed many times to better understand user behaviour, the log files of hospital internal systems for search in a PACS/RIS (Picture Archival and Communication System, Radiology Information System) have rarely been analysed. Such a comparison between a hospital PACS/RIS search and a web system for searching images of the biomedical literature is the goal of this paper. Objectives are to identify similarities and differences in search behaviour of the two systems, which could then be used to optimize existing systems and build new search engines. Log files of the ARRS GoldMiner medical image search engine (freely accessible on the Internet) containing 222,005 queries, and log files of Stanford's internal PACS/RIS search called radTF containing 18,068 queries were analysed. Each query was preprocessed and all query terms were mapped to the RadLex (Radiology Lexicon) terminology, a comprehensive lexicon of radiology terms created and maintained by the Radiological Society of North America, so the semantic content in the queries and the links between terms could be analysed, and synonyms for the same concept could be detected. RadLex was mainly created for the use in radiology reports, to aid structured reporting and the preparation of educational material (Lanlotz, 2006) [1]. In standard medical vocabularies such as MeSH (Medical Subject Headings) and UMLS (Unified Medical Language System) specific terms of radiology are often underrepresented, therefore RadLex was considered to be the best option for this task. The results show a surprising similarity between the usage behaviour in the two systems, but several subtle differences can also be noted. The average number of terms per query is 2.21 for GoldMiner and 2.07 for radTF, the used axes of RadLex (anatomy, pathology, findings, …) have almost the same distribution with clinical findings being the most frequent and the anatomical entity the second; also, combinations of RadLex axes are extremely similar between the two systems. Differences include a longer length of the sessions in radTF than in GoldMiner (3.4 and 1.9 queries per session on average). Several frequent search terms overlap but some strong differences exist in the details. In radTF the term "normal" is frequent, whereas in GoldMiner it is not. This makes intuitive sense, as in the literature normal cases are rarely described whereas in clinical work the comparison with normal cases is often a first step. The general similarity in many points is likely due to the fact that users of the two systems are influenced by their daily behaviour in using standard web search engines and follow this behaviour in their professional search. This means that many results and insights gained from standard web search can likely be transferred to more specialized search systems. Still, specialized log files can be used to find out more on reformulations and detailed strategies of users to find the right content.

A supervised adverse drug reaction signalling framework imitating Bradford Hill's causality considerations.

Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data. In this paper we propose using a set of causality considerations developed by the epidemiologist Bradford Hill as a basis for engineering features that enable the application of supervised learning for the problem of detecting negative side effects. The Bradford Hill considerations look at various perspectives of a drug and outcome relationship to determine whether it shows causal traits. We taught a classifier to find patterns within these perspectives and it learned to discriminate between association and causality. The novelty of this research is the combination of supervised learning and Bradford Hill's causality considerations to automate the Bradford Hill's causality assessment. We evaluated the framework on a drug safety gold standard known as the observational medical outcomes partnership's non-specified association reference set. The methodology obtained excellent discrimination ability with area under the curves ranging between 0.792 and 0.940 (existing method optimal: 0.73) and a mean average precision of 0.640 (existing method optimal: 0.141). The proposed features can be calculated efficiently and be readily updated, making the framework suitable for big observational data.

The chronic care model and technological research and innovation: a scoping review at the crossroads.

BACKGROUND: Information and communication technologies (ICT) are key to optimizing the outcomes of the Chronic Care Model (CCM), currently acknowledged as the best synthesis of available evidence for chronic illness prevention and management. At the same time, CCM can offer a needed framework for increasing the relevance and feasibility of ICT innovation and research in health care. Little is known about how and to what extent CCM and ICT research inform each other to leverage mutual strengths. The current study examines: What characterizes work being done at the crossroads of CCM and ICT research and innovation? OBJECTIVE: Our aim is identify the gaps and potential that lie between the research domains CCM and ICT, thus enabling more substantive questions and opportunities for accelerating improvements in ICT-supported chronic care. METHODS: Using a scoping study approach, we developed a search strategy applied to medical and technical databases resulting in 1054 titles and abstracts that address CCM and ICT. After iteratively adapting our inclusion/exclusion criteria to balance between breadth and feasibility, 26 publications from 20 studies were found to fulfill our criteria. Following initial coding of each article according to predefined categories (eg, type of article, CCM component, ICT, health issue), a 1st level analysis was conducted resulting in a broad range of categories. These were gradually reduced by constantly comparing them for underlying commonalities and discrepancies. RESULTS: None of the studies included were from technical databases and interventions relied mostly on "old-fashioned" technologies. Technologies supporting "productive interactions" were often one-way (provider to patient), and it was sometimes difficult to decipher how CCM was guiding intervention design. In particular, the major focus on ICT to support providers did not appear unique to the challenges of chronic care. Challenges in facilitating CCM components through ICT included poorly designed user interfaces, digital divide issues, and lack of integration with existing infrastructure. CONCLUSIONS: The CCM is a highly influential guide for health care development, which recognizes the need for alignment of system tools such as ICT. Yet, there seem to be alarmingly few touch points between the subject fields of "health service development" and "ICT-innovation". Bridging these gaps needs explicit and urgent attention as the synergies between these domains have enormous potential. Policy makers and funding agencies need to facilitate the joining of forces between high-tech innovative expertise and experts in the chronic care system redesign that is required for tackling the current epidemic of long-term multiple conditions.

Using the Analytic Hierarchy Process (AHP) to understand the most important factors to design and evaluate a telehealth system for Parkinson's disease.

BACKGROUND: The assessment of a new health technology is a multidisciplinary and multidimensional process, which requires a complex analysis and the convergence of different stakeholders into a common decision. This task is even more delicate when the assessment is carried out in early stage of development processes, when the maturity of the technology prevents conducting a large scale trials to evaluate the cost effectiveness through classic health economics methods. This lack of information may limit the future development and deployment in the clinical practice. This work aims to 1) identify the most relevant user needs of a new medical technology for managing and monitoring Parkinson's Disease (PD) patients and to 2) use these user needs for a preliminary assessment of a specific system called PERFORM, as a case study. METHODS: Analytic Hierarchy Process (AHP) was used to design a hierarchy of 17 needs, grouped into 5 categories. A total of 16 experts, 6 of them with a clinical background and the remaining 10 with a technical background, were asked to rank these needs and categories. RESULTS: On/Off fluctuations detection, Increase wearability acceptance, and Increase self-management support have been identified as the most relevant user needs. No significant differences were found between the clinician and technical groups. These results have been used to evaluate the PERFORM system and to identify future areas of improvement. CONCLUSIONS: First of all, the AHP contributed to the elaboration of a unified hierarchy, integrating the needs of a variety of stakeholders, promoting the discussion and the agreement into a common framework of evaluation. Moreover, the AHP effectively supported the user need elicitation as well as the assignment of different weights and priorities to each need and, consequently, it helped to define a framework for the assessment of telehealth systems for PD management and monitoring. This framework can be used to support the decision-making process for the adoption of new technologies in PD.

Design and implementation of a hospital wide waveform capture system.

The use of telemetry and invasive monitoring is exceptionally common in modern healthcare. To date the vast majority of this information is not stored for more than a brief duration on the local monitor. This prohibits extensive investigation into waveform data. We describe a system to collect such data in a quaternary care facility. Using standardized "packet sniffing" technology along with routine manual documentation, we reverse engineered the Unity network protocol used to transmit waveform data across the University of Michigan mission critical monitor network. Data was subsequently captured using a proprietary piece of software writing waveform data to local disks. Nightly, this data is post-processed using data from the admit-discharge-transfer system into individual patient waveforms for the day regardless of location. Over a 10 month period, over 2,785 individual patients had a total of 65,112 waveforms captured 15,978 from the operating rooms and 49,134 from the ICUs. The average OR case collected over 11 MB of data. The average single day data collection consisted of 8.6 GB of data. Entire hospital waveform data collection is possible using internally developed software enabling research on waveform data with minimal technical burden. Further research is required to determine the long-term storage and processing of such data.

[Research on the High Efficiency Data Communication Repeater Based on STM32F103].

OBJECTIVE: To improve the radio frequency (RF) transmission distance of the wireless terminal of the medical internet of things (LOT), to realize the real-time and efficient data communication, the intelligent relay system based on STM32F103 single chip microcomputer (SCM) is proposed. METHODS: The system used nRF905 chip to achieve the collection, of medical and health information of patients in the 433 MHz band, used SCM to control the serial port to Wi-Fi module to transmit information from 433 MHz to 2.4 GHz wireless Wi-Fi band, and used table look-up algorithm of ready list to improve the efficiency of data communications. RESULTS: The design can realize real-time and efficient data communication. CONCLUSION: The relay which is easy to use with high practical value can extend the distance and mode of data transmission and achieve real-time transmission of data.

Analysis of biomedical data with multilevel glyphs.

BACKGROUND: This paper presents multilevel data glyphs optimized for the interactive knowledge discovery and visualization of large biomedical data sets. Data glyphs are three- dimensional objects defined by multiple levels of geometric descriptions (levels of detail) combined with a mapping of data attributes to graphical elements and methods, which specify their spatial position. METHODS: In the data mapping phase, which is done by a biomedical expert, meta information about the data attributes (scale, number of distinct values) are compared with the visual capabilities of the graphical elements in order to give a feedback to the user about the correctness of the variable mapping. The spatial arrangement of glyphs is done in a dimetric view, which leads to high data density, a simplified 3D navigation and avoids perspective distortion. RESULTS: We show the usage of data glyphs in the disease analyser a visual analytics application for personalized medicine and provide an outlook to a biomedical web visualization scenario. CONCLUSIONS: Data glyphs can be successfully applied in the disease analyser for the analysis of big medical data sets. Especially the automatic validation of the data mapping, selection of subgroups within histograms and the visual comparison of the value distributions were seen by experts as an important functionality.

Informatics for neurocritical care: challenges and opportunities.

Neurocritical care relies on the continuous, real-time measurement of numerous physiologic parameters. While our capability to obtain such measurements from patients has grown markedly with multimodal monitoring in many neurologic or neurosurgical intensive care units (ICUs), our ability to transform the raw data into actionable information is limited. One reason is that the proprietary nature of medical devices and software often prevents neuro-ICUs from capturing and centrally storing high-density data. Also, ICU alarm systems are often unreliable because the data that are captured are riddled with artifacts. Informatics is the process of acquiring, processing, and interpreting these complex arrays of data. The development of next-generation informatics tools allows for detection of complex physiologic events and brings about the possibility of decision support tools to improve neurocritical care. Although many different approaches to informatics are discussed and considered, here we focus on the Bayesian probabilistic paradigm. It quantifies the uncertainty inherent in neurocritical care instead of ignoring it, and formalizes the natural clinical thought process of updating prior beliefs using incoming patient data. We review this and other opportunities, as well as challenges, for the development and refinement of informatics tools in neurocritical care.