MoSBi: Automated signature mining for molecular stratification and subtyping.

The improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (molecular signature identification using biclustering), an automated multialgorithm ensemble approach that integrates results utilizing an error model-supported similarity network. We systematically evaluated the performance of 11 available and established biclustering algorithms together with MoSBi. For this, we used transcriptomics, proteomics, and metabolomics data, as well as synthetic datasets covering various data properties. Profiting from multialgorithm integration, MoSBi identified robust group and disease-specific signatures across all scenarios, overcoming single algorithm specificities. Furthermore, we developed a scalable network-based visualization of bicluster communities that supports biological hypothesis generation. MoSBi is available as an R package and web service to make automated biclustering analysis accessible for application in molecular sample stratification.

The reasonable patient - A Swedish discursive construction.

The aim of this study was to analyse how the patient is constructed and socially positioned in Swedish patient information. Corpus-assisted critical discourse analysis methodology was utilised on a sample of 56 online patient information texts about cancer containing a total of 126,711 words. The findings show an overarching discourse of informed consent guided by specific features to produce a patient norm that we name "the reasonable patient", who is receptive to arguments, emotionally restrained and makes decisions based on information. Through the discourse of informed consent, the norm of the reasonable patient emerges, apparently to even out the imbalance of power between patient and professional, but in reality, more likely to construct a patient who is easily controlled and managed. When the self-responsibility towards health is incorporated into the everyday domestic spaces via digital health technologies, the ideas and concepts of the patient role need to be reconsidered based on these new conditions. We conclude that it is important for nursing researchers to broaden the research on patients to include the relationship of power created through language. This study demonstrates both methodological and empirical possibilities to do so.

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications.

Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients' classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications.

[Clinical validation of 2 morbidity groups in the primary care setting]. / Validación clínica de 2 agrupadores de morbilidad en el ámbito de atención primaria.

INTRODUCTION: Adjusted Morbidity Groups (GMAs) and the Clinical Risk Groups (CRGs) are population morbidity based stratification tools which classify patients into mutually exclusive categories. OBJETIVE: To compare the stratification provided by the GMAs, CRGs and that carried out by the evaluators according to the levels of complexity. DESIGN: Random sample stratified by morbidity risk. LOCATION: Catalonia. PARTICIPANTS: Forty paired general practitioners in the primary care, matched pairs. INTERVENTIONS: Each pair of evaluators had to review 25 clinical records. MAIN OUTPUTS: The concordance by evaluators, and between the evaluators and the results obtained by the 2 morbidity tools were evaluated according to the kappa index, sensitivity, specificity, and positive and negative predicted values. RESULTS: The concordance between general practitioners pairs was around the kappa value 0.75 (mean value=0.67), between the GMA and the evaluators was similar (mean value=0.63), and higher than for the CRG (mean value=0.35). The general practitioners gave a score of 7.5 over 10 to both tools, although for the most complex strata, according to the professionals' assignment, the GMA obtained better scores than the CRGs. The professionals preferred the GMAs over the CRGs. These differences increased with the complexity level of the patients according to clinical criteria. Overall, less than 2% of serious classification errors were found by both groupers. CONCLUSION: The evaluators considered that both grouping systems classified the studied population satisfactorily, although the GMAs showed a better performance for more complex strata. In addition, the clinical raters preferred the GMAs in most cases.

Using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity.

BACKGROUND: Many clinical concepts are standardized under a categorical and hierarchical taxonomy such as ICD-10, ATC, etc. These taxonomic clinical concepts provide insight into semantic meaning and similarity among clinical concepts and have been applied to patient similarity measures. However, the effects of diverse set sizes of taxonomic clinical concepts contributing to similarity at the patient level have not been well studied. METHODS: In this paper the most widely used taxonomic clinical concepts system, ICD-10, was studied as a representative taxonomy. The distance between ICD-10-coded diagnosis sets is an integrated estimation of the information content of each concept, the similarity between each pairwise concepts and the similarity between the sets of concepts. We proposed a novel method at the set-level similarity to calculate the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity. A real-world clinical dataset with ICD-10 coded diagnoses and hospital length of stay (HLOS) information was used to evaluate the performance of various algorithms and their combinations in predicting whether a patient need long-term hospitalization or not. Four subpopulation prototypes that were defined based on age and HLOS with different diagnoses set sizes were used as the target for similarity analysis. The F-score was used to evaluate the performance of different algorithms by controlling other factors. We also evaluated the effect of prototype set size on prediction precision. RESULTS: The results identified the strengths and weaknesses of different algorithms to compute information content, code-level similarity and set-level similarity under different contexts, such as set size and concept set background. The minimum weighted bipartite matching approach, which has not been fully recognized previously showed unique advantages in measuring the concepts-based patient similarity. CONCLUSIONS: This study provides a systematic benchmark evaluation of previous algorithms and novel algorithms used in taxonomic concepts-based patient similarity, and it provides the basis for selecting appropriate methods under different clinical scenarios.

Patient similarity for precision medicine: A systematic review.

Evidence-based medicine is the most prevalent paradigm adopted by physicians. Clinical practice guidelines typically define a set of recommendations together with eligibility criteria that restrict their applicability to a specific group of patients. The ever-growing size and availability of health-related data is currently challenging the broad definitions of guideline-defined patient groups. Precision medicine leverages on genetic, phenotypic, or psychosocial characteristics to provide precise identification of patient subsets for treatment targeting. Defining a patient similarity measure is thus an essential step to allow stratification of patients into clinically-meaningful subgroups. The present review investigates the use of patient similarity as a tool to enable precision medicine. 279 articles were analyzed along four dimensions: data types considered, clinical domains of application, data analysis methods, and translational stage of findings. Cancer-related research employing molecular profiling and standard data analysis techniques such as clustering constitute the majority of the retrieved studies. Chronic and psychiatric diseases follow as the second most represented clinical domains. Interestingly, almost one quarter of the studies analyzed presented a novel methodology, with the most advanced employing data integration strategies and being portable to different clinical domains. Integration of such techniques into decision support systems constitutes and interesting trend for future research.

Patient ranking with temporally annotated data.

Modern medical information systems enable the collection of massive temporal health data. Albeit these data have great potentials for advancing medical research, the data exploration and extraction of useful knowledge present significant challenges. In this work, we develop a new pattern matching technique which aims to facilitate the discovery of clinically useful knowledge from large temporal datasets. Our approach receives in input a set of temporal patterns modeling specific events of interest (e.g., doctor's knowledge, symptoms of diseases) and it returns data instances matching these patterns (e.g., patients exhibiting the specified symptoms). The resulting instances are ranked according to a significance score based on the p-value. Our experimental evaluations on a real-world dataset demonstrate the efficiency and effectiveness of our approach.

The Impact of Patient Characteristics and Postoperative Opioid Exposure on Prolonged Postoperative Opioid Use: An Integrative Review.

OBJECTIVES: The United States is experiencing an opioid overdose crisis. Research suggests prolonged postoperative opioid use, a common complication following surgery, is associated with opioid misuse, which, in turn, is the greatest risk factor of heroin misuse. The objective of this review is to evaluate how postoperative opioid exposure relates to prolonged use and to identify factors that predict prolonged postoperative opioid use. DESIGN: An integrative review of the literature. DATA SOURCES: Electronic and hand searching methods were used in PubMed, Embase, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, CINAHL, and SCOPUS. Search terms included opioid, opiate, postoperative pain, drug administration, prescribing pattern, prescription, inappropriate prescribing, self-medication, patient-controlled analgesia, opioid-naïve patients, and prolonged opioid use. REVIEW/ANALYSIS METHODS: Data were synthesized by identifying themes reflecting the results of the review. A quality assessment of the articles was also conducted. RESULTS: Fourteen articles were included and two main themes emerged: (1) Surgery places opioid naïve patients at risk for prolonged opioid use and (2) Certain patient characteristics may be predictive of prolonged postoperative opioid use. CONCLUSIONS: Prolonged postoperative opioid use is related to factors in addition to prescribing practices. Researchers consistently found that patients who are already on opioids, benzodiazepines, or addicted to alcohol; who have mental health disorders, depressive symptoms, or a self-perceived risk of addiction; and patients with multiple co-morbidities are at greater risk of prolonged use; demographics were inconsistent. NURSING IMPLICATIONS: Studies are needed to determine the predicting characteristics of prolonged postoperative opioid use, the type of surgeries that place patients at most risk, and the effect postoperative exposure to opioids has on prolonged use. This information can be used to develop and implement protocols to prevent misuse among high-risk patients.

The being a patient effect: negative expectations based on group labeling and corresponding treatment affect patient performance.

Patient studies provide insights into mechanisms underlying diseases and thus represent a cornerstone of clinical research. In this study, we report evidence that differences between patients and controls might partly be based on expectations generated by the patients' knowledge of being invited and treated as a patient: the Being a Patient effect (BP effect). This finding extends previous neuropsychological reports on diagnosis threat. Participants with mild allergies were addressed either as patients or control subjects in a clinical study. We measured the impact of this group labeling and corresponding instructions on pain perception and cognitive performance. Our results provide evidence that the BP effect can indeed affect physiological and cognitive measures in clinical settings. Importantly, these effects can lead to systematic overestimation of genuine disease effects and should be taken into account when disease effects are investigated. Finally, we propose strategies to avoid or minimize this critical confound.

Redesigning the patient classification system in China: A pilot study.

The current prototype patient classification system in China has not been updated for over six decades. In the present study, we adopted a hybrid patient classification method using both disease severity and activities of daily living scores to classify patients. The time motion approach was used to measure the direct nursing time of 551 general acute care patients. We found that patients in old Categories according to ability of self-care and disease severity 1-4 received approximately 7.1, 4.6, 3.4, and 4.5 h of direct nursing care, and the number of hours was not significantly different between Categories 2, 3, and 4. In contrast, patients in new Categories 1-4 received approximately 10.1, 6.9, 4.4, and 2.4 h of direct nursing care in 24 h. The nursing hours were significantly different between all pairings of the new categories. The new classification system can be used to make nursing care assignments and adjust staffing.

Classification of patients and nursing staff's sizing: contributions of a management technology. / Classificação de pacientes e dimensionamento de profissionais de enfermagem: contribuições de uma tecnologia de gestão.

OBJECTIVE: Applying PRAXIS® technology resources for patient classification and nursing professional sizing in university hospital inpatient unit. METHOD: Convergent Care Research following the design and instrumentation phases - defined the research theme and purpose, performed in a medical clinic hospital unit involving 633 participants; scrutiny - classification of patients during 30 days of June 2016, followed by sizing, analysis and interpretation of the results - elaborated with the support of the management theorization in hospital nursing. RESULTS: Amongst the total of 633 classifications made, 29.38% were patients in minimal care, 35.71% were intermediate care patients, 33.02% were highly dependent, 1.42% were semi-intensive and 0.47% were in intensive care. Two references were used to carry out the sizing; in both the available team showed to be in deficit. CONCLUSION: The classification of patients and the sizing of nursing professionals are directly related, they are indispensable for management in nursing and difficult to perform daily. Computerized technologies are useful for performing these activities.

Evaluation of a practical expert defined approach to patient population segmentation: a case study in Singapore.

BACKGROUND: Segmenting the population into groups that are relatively homogeneous in healthcare characteristics or needs is crucial to facilitate integrated care and resource planning. We aimed to evaluate the feasibility of segmenting the population into discrete, non-overlapping groups using a practical expert and literature driven approach. We hypothesized that this approach is feasible utilizing the electronic health record (EHR) in SingHealth. METHODS: In addition to well-defined segments of "Mostly healthy", "Serious acute illness but curable" and "End of life" segments that are also present in the Ministry of Health Singapore framework, patients with chronic diseases were segmented into "Stable chronic disease", "Complex chronic diseases without frequent hospital admissions", and "Complex chronic diseases with frequent hospital admissions". Using the electronic health record (EHR), we applied this framework to all adult patients who had a healthcare encounter in the Singapore Health Services Regional Health System in 2012. ICD-9, 10 and polyclinic codes were used to define chronic diseases with a comprehensive look-back period of 5 years. Outcomes (hospital admissions, emergency attendances, specialist outpatient clinic attendances and mortality) were analyzed for years 2012 to 2015. RESULTS: Eight hundred twenty five thousand eight hundred seventy four patients were included in this study with the majority being healthy without chronic diseases. The most common chronic disease was hypertension. Patients with "complex chronic disease" with frequent hospital admissions segment represented 0.6% of the eligible population, but accounted for the highest hospital admissions (4.33 ± 2.12 admissions; p < 0.001) and emergency attendances (ED) (3.21 ± 3.16 ED visits; p < 0.001) per patient, and a high mortality rate (16%). Patients with metastatic disease accounted for the highest specialist outpatient clinic attendances (27.48 ± 23.68 visits; p < 0.001) per patient despite their relatively shorter course of illness and high one-year mortality rate (33%). CONCLUSION: This practical segmentation framework can potentially distinguish among groups of patients, and highlighted the high disease burden of patients with chronic diseases. Further research to validate this approach of population segmentation is needed.

Feasibility of an electronic participant information system for adult day services: A qualitative study of staff perspectives.

Adult day services (ADS) professionals have begun to explore assessment systems focused on participants. Barriers include inadequate technology, software costs, and personnel requirements. We present data from staff interviews at an ADS with an electronic participant information system. Contrary to reports about difficulties learning to use electronic systems, staff found the system manageable and data meaningful. We identify ways that community-based centers can build partnerships and utilize software to integrate assessment and electronic records to improve center performance and participant outcomes. ADS programs should explore how outcome data systems can be used to improve care, promote family caregiver engagement, optimize staff workload, and promote fiscal stability.

[ASA classification : Transition in the course of time and depiction in the literature]. / ASA-Klassifikation : Wandel im Laufe der Zeit und Darstellung in der Literatur.

The American Society of Anesthesiologists classification of physical status (ASA PS) is a widely used system for categorizing the preoperative status of patients. The ASA class is a good independent predictor of perioperative morbidity and mortality. The definitions of the ASA classes have been amended several times since 1941, resulting in inconsistent and confusing usage in the current literature. Conflicting definitions of ASA PS exist, particularly for classes III, IV and V. The high variability of individual classifications by different anesthesiologist, however, can be explained by the previous lack of examples for diagnoses. In 2014, the ASA has added a catalogue of examples for a simplified definition for classification of the ASA PS. This has so far received limited attention in German-speaking countries. This article describes the transition of the ASA classification over the past 75 years und summarizes the currently valid definitions.

Classification of patients and nursing workload in intensive care: comparison between instruments. / Classificação de pacientes e carga de trabalho de enfermagem em terapia intensiva: comparação entre instrumentos.

OBJECTIVES: To evaluate the mean nursing workload obtained through the Nursing Activities Score (NAS) and extract the degree of dependency of patients using Perroca's Patient Classification System (PCS). METHODS: Prospective study conducted at the intensive care unit of a private hospital that is a center of reference in oncology. The instruments were applied daily in a sample of 40 patients with a minimum stay of 24 hours. RESULTS: Two hundred and seventy-seven measurements were performed with the instruments. The NAS mean was 69.8% (± 24.1%) and Perroca's Patient Classification System score was 22.7% (± 4.2%). The hours of care found by averaging NAS were almost twice those estimated by Perroca's, showing a difference of 7.3 hours. CONCLUSION: The direct instrument NAS was more appropriate to measure nursing workload when compared to Perroca's indirect instrument in the studied intensive care unit.

A quantitative evidence base for population health: applying utilization-based cluster analysis to segment a patient population.

BACKGROUND: To improve population health it is crucial to understand the different care needs within a population. Traditional population groups are often based on characteristics such as age or morbidities. However, this does not take into account specific care needs across care settings and tends to focus on high-needs patients only. This paper explores the potential of using utilization-based cluster analysis to segment a general patient population into homogenous groups. METHODS: Administrative datasets covering primary and secondary care were used to construct a database of 300,000 patients, which included socio-demographic variables, morbidities, care utilization, and cost. A k-means cluster analysis grouped the patients into segments with distinct care utilization, based on six utilization variables: non-elective inpatient admissions, elective inpatient admissions, outpatient visits, GP practice visits, GP home visits, and prescriptions. These segments were analyzed post-hoc to understand their morbidity and demographic profile. RESULTS: Eight population segments were identified, and utilization of each care setting was significantly different across all segments. Each segment also presented with different morbidity patterns and demographic characteristics, creating eight distinct care user types. Comparing these segments to traditional patient groups shows the heterogeneity of these approaches, especially for lower-needs patients. CONCLUSIONS: This analysis shows that utilization-based cluster analysis segments a patient population into distinct groups with unique care priorities, providing a quantitative evidence base to improve population health. Contrary to traditional methods, this approach also segments lower-needs populations, which can be used to inform preventive interventions. In addition, the identification of different care user types provides insight into needs across the care continuum.

Using concept hierarchies to improve calculation of patient similarity.

OBJECTIVE: We introduce a new distance measure that is better suited than traditional methods at detecting similarities in patient records by referring to a concept hierarchy. MATERIALS AND METHODS: The new distance measure improves on distance measures for categorical values by taking the path distance between concepts in a hierarchy into account. We evaluate and compare the new measure on a data set of 836 patients. RESULTS: The new measure shows marked improvements over the standard measures, both qualitatively and quantitatively. Using the new measure for clustering patient data reveals structure that is otherwise not visible. Statistical comparisons of distances within patient groups with similar diagnoses shows that the new measure is significantly better at detecting these similarities than the standard measures. CONCLUSION: The new distance measure is an improvement over the current standard whenever a hierarchical arrangement of categorical values is available.

'The poor historian': Heart sink? Or time for a re-think?

In this commentary article, the oft-heard expression, 'the poor historian', will be discussed. We will consider who the poor historian is and reflect on medical training to speculate how and why the expression has entered the medical lexicon. The potential negative impact of this terminology on patients and junior learners will be considered and strategies for re-framing this concept, for both clinical teachers and learners, will be presented.

Dental service provision by oral health therapists, dental hygienists and dental therapists in Australia: implications for workforce modelling.

OBJECTIVE: Dental service provision rates are necessary for workforce planning. This study estimates patient and service rates for oral health therapists (OHTs), dental hygienists (DHs) and dental therapists (DTs). To identify important variables for workforce modelling, variations in rates by practice characteristics were assessed. DESIGN: A cross-sectional self-complete mailed questionnaire collected demographic and employment characteristics, and clinical activity on a self-selected typical day of practice. SETTING: Private and public dental practices in Australia. PARTICIPANTS: Members of the two professional associations representing DHs, DTs and OHTs. METHODS: For each practitioner type, means and adjusted rate ratios of patients per hour, services per visit and preventive services per visit were estimated. Comparisons by practice characteristics were assessed by negative binomial regression models. RESULTS: Response rate was 60.6% (n = 1,083), 90.9% were employed of which 86.3% were working in clinical practice and completed the service log. Mean services per patient visit provided by OHTs, DHs and DTs were 3.7, 3.5 and 3.3 and mean preventive services per patient were 2.1, 2.1 and 1.8 respectively. For all three groups, adjusting for explanatory variables, the rate of preventive services per patient varied significantly by practice type (general or specialist) and by the proportion of child patients treated. CONCLUSION: Services rates varied by age distribution of patients and type of practice. If these factors were anticipated to vary over-time, then workforce planning models should consider accounting for the potential impact on capacity to supply services by these dental workforce groups.

Retrograde Intrarenal Surgery is equally efficient and safe in patients with different American Society of Anesthesia physical status.

OBJECTIVE: To assess the efficacy and safety of Retrograde Intrarenal Surgery to treat renal stones in patients with different American Society of Anesthesia (ASA) physical status. MATERIAL AND METHODS: We performed a retrospective analysis of 150 patients who underwent Retrograde Intrarenal Surgery for renal stone between October 2013 and December 2014. Patients were categorized into three groups according to their ASA physical status: ASA Class 1 (Group 1, n = 23), ASA Class 2 (Group 2, n = 113) and ASA Class 3 (Group 3, n = 14). We documented and stratified the per-operative and postoperative complications according to modified Satava Classification System and Clavien-Dindo Classification. RESULTS: The mean age of the patients was 44 years. The total stone-free rate was 81.2%. According to the groups, the stone-free rate was 75% in Group 1, 82.5% in Group 2, and 83.3% in Group 3 (p = 0.340). Per-operative and postoperative complications were recorded in 12% (n = 18) and 5.3% (n = 8) of the patients. We did not find significant difference in terms of per-operative and postoperative complication rates among patients with different ASA physical status (p(per-operative) = 0.392 and p(postoperative) = 0.136). CONCLUSIONS: Retrograde Intrarenal Surgery is an effective and safe surgery with high stone-free rates and low morbidity in patients with different ASA physical status.