Methodological reflections on using pilot data from fracture patients to develop a qualitative study

Background: Qualitative studies are particularly valued for their exploratory nature but, like other research methods, they do require careful planning to ensure rigorous study design. Our objective was to undertake a pilot study to inform the development of a larger qualitative study.

Results: We conducted a series of brief interviews with out-patients in a hospital setting. The interviews were designed to elicit superficial information about whether (and how) post-fracture osteoporosis investigation and/or treatment were being initiated among patients receiving treatment or follow-up for a current or recent fracture. We used thematic analysis to identify key themes in the data that related to the broader research questions. We analysed data obtained from 11 out of a total of 12 interviews conducted. Participants were male and female, aged 19-83 years of age (median age 57 years). Participants attended 2-8 medical appointments to seek treatment and follow up for a current or recent fracture. The following four overarching themes emerged from thematic analysis of the data: fracture event, referral pathway, osteoporosis investigation and/or treatment, and communication by health practitioners and staff.

Conclusions: This pilot study was necessarily tentative and exploratory in nature, but provided a helpful snapshot of some typical experiences in the public health system following fracture. Several themes emerged for consideration in the design of the main study. Despite its critics, theoretical sampling and saturation continue to provide sustainable methods for ensuring that relevant themes and categories are covered in sufficient depth and breadth, appropriate to the needs of the study.

Multifactorial influences on and deviations from medication administration safety and quality in the acute medical/surgical context

Background and Aims: Although numerous factors influence medication administration, our understanding of the interplay of these factors on medication quality and safety is limited. The aim of this study was to explore the multifactorial influences on medication quality and safety in the context of a single checking policy for medication administration in acute care.

Approach: An exploratory/descriptive study using non-participant observation and follow-up interview was used to identify factors influencing medication quality and safety in medication administration episodes (n = 30). Observations focused on nurses’ interactions with patients during medication administration, and the characteristics of the environment in which these took place. Confirmation of observed data occurred on completion of the observation period during short semi-structured interviews with participant nurses.

Findings: Findings showed nurses developed therapeutic relationships with patients in terms of assessing patients before administering medications and educating patients about drugs during medication administration. Nurses experienced more frequent distractions when medications were stored and prepared in a communal drug room according to ward design. Nurses deviated from best-practice guidelines during medication administration.

Implications: Nurses’ abilities and readiness to develop therapeutic relationships with patients increased medication quality and safety, thereby protecting patients from potential adverse events. Deviations from best-practice medication administration had the potential to decrease medication safety. System factors such as ward design determining medication storage areas can be readily addressed to minimise potential error.

Conclusions: Nurses displayed behaviours that increased medication administration quality and safety; however, violations of practice standards were observed. These findings will inform future intervention studies to improve medication quality and safety.

Illness burden and medical comorbidity in the systematic treatment enhancement program for bipolar disorder

Objective:  Coexisting chronic medical conditions are common in bipolar disorder. Here, we report the prevalence and correlates of medical comorbidity in patients enrolled in the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). We were particularly interested in associations between variables reflecting illness chronicity and burden with comorbid medical conditions.

Method:  We used intake data from the open-label component of the STEP-BD. History of medical comorbidity was obtained from the affective disorders evaluation, and its presence was the outcome of interest. The sample size in analyses varied from 3399 to 3534. We used multiple Poisson regression to obtain prevalence ratios.

Results:  The prevalence of any medical comorbidity in the sample was 58.8%. In addition to demographic variable, several clinical characteristics were associated with the frequency of medical comorbidity. Having more than 10 previous mood episodes, childhood onset, smoking, lifetime comorbidity with anxiety, and substance use disorders were independently associated with having a medical comorbidity in the final multivariate model.

Conclusion:  The results presented here reveal strong associations between variables related to illness chronicity and medical burden in bipolar disorder. This lends further support to recent multidimensional models incorporating medical morbidity as a core feature of bipolar disorder.

Fuzzy ARTMAP and a hybrid chaos genetic algorithm for medical pattern classification

In this paper, an Evolutionary Artificial Neural Network (EANN), which combines the Fuzzy ARTMAP (FAM) neural network and a hybrid Chaos Genetic Algorithm (CGA), is proposed for undertaking pattern classification tasks. The hybrid CGA is a modified version of the hybrid real-coded genetic algorithms that includes a Chaotic Mapping Operator (CMO) in its search and adaptation process. It is used to evolve the connection weights in FAM, and the resulting EANN is known as FAM-hybrid CGA. The CMO in the hybrid CGA is used to generate a group of chromosomes that incorporates the characteristics of chaos. The chromosomes are then adapted with an arbitrary small amount of variation in every generation. As the evolution procedure proceeds, chromosomes with considerable differences are produced. Such chromosomes, which are located at different regions of interest in the solution space, are able to provide good solutions to undertake search and adaption problems. The effectiveness of the proposed FAM-hybrid CGA model is first evaluated using benchmark medical data sets from the UCI machine learning repository. Its applicability to medical decision support is then demonstrated using a real database of patient records with suspected Acute Coronary Syndrome. The results indicate that FAM-hybrid CGA is able to outperform its neural network counterpart (i.e., FAM), and it can be employed as a useful pattern classification tool for tackling medical decision support tasks.

General medical practitioners’ knowledge and beliefs about osteoporosis and its investigation and management

Summary This qualitative study explored beliefs and attitudes regarding osteoporosis and its management. General medical practitioners (GPs) were ambivalent about osteoporosis due to concern about financial barriers for patients and their own beliefs about the salience of osteoporosis. GPs considered investigation and treatment in the context of patients' whole lives.

Purpose We aimed to investigate barriers, enablers, and other factors influencing the investigation and management of osteoporosis using a qualitative approach. This paper analyses data from discussions with general medical practitioners (GPs) about their beliefs and attitudes regarding osteoporosis and its management.

Methods Fourteen GPs and two practice nurses aged 27–89 years participated in four focus groups, from June 2010 to March 2011. Each group comprised 3–5 participants, and discussions were semi-structured, according to the protocol developed for the main study. Discussion points ranged from the circumstances under which GPs would initiate investigation for osteoporosis and their subsequent actions to their views about treatment efficacy and patient adherence to prescribed treatment. Audio recordings were transcribed and coded for analysis using analytic comparison to identify the major themes.

Results The GPs were not particularly concerned about osteoporosis in their patients or the general population, ranking diabetes, osteoarthritis, cardiovascular disease, and hypertension higher than concern about osteoporosis. They expressed confidence in the efficacy of anti-fracture medications but were concerned about the potential financial burden on patients with limited incomes. The GPs were unsure about guidelines for investigation and management of osteoporosis in men and the appropriate duration of treatment, particularly for the bisphosphonates in all patients.

Conclusions The GPs' ambivalence about osteoporosis appeared to stem from structural factors such as financial barriers for patients and their own beliefs about the salience of osteoporosis. GPs considered the impact of investigating and prescribing treatment in the context of patients' whole lives.

Implementing scanned medical record systems in Australia : a structured case study on envisioned changes to elective admissions process in a Victorian hospital

In recent years, influenced by the pervasive power of technology, standards and mandates, Australian hospitals have begun exploring digital forms of keeping this record. The main rationale is the ease of accessing different data sources at the same time by varied staff members. The initial step in this transition was implementation of scanned medical record systems, which converts the paper based records to digitised form, which required process flow redesign and changes to existing modes of work. For maximising the benefits of scanning implementation and to better prepare for the changes, Austin Hospital in the State of Victoria commissioned this research focused on elective admissions area. This structured case study redesigned existing processes that constituted the flow of external patient forms and recommended a set of best practices at the same time highlighting the significance of user participation in maximising the potential benefits anticipated. In the absence of published academic studies focused on Victorian hospitals, this study has become a conduit for other departments in the hospital as well as other hospitals in the incursion.

An efficient solution for privacy-preserving, secure remote access to sensitive data

Sharing data that contains personally identifiable or sensitive information, such as medical records, always has privacy and security implications. The issues can become rather complex when the methods of access can vary, and accurate individual data needs to be provided whilst mass data release for specific purposes (for example for medical research) also has to be catered for. Although various solutions have been proposed to address the different aspects individually, a comprehensive approach is highly desirable. This paper presents a solution for maintaining the privacy of data released en masse in a controlled manner, and for providing secure access to the original data for authorized users. The results show that the solution is provably secure and maintains privacy in a more efficient manner than previous solutions.

Analysing stillbirth data using dynamic self organizing maps

Even with the presence of modern obstetric care, stillbirth rate seems to stay stagnant or has even risen slightly in countries such as England and has become a significant public health concern [1]. In the light of current medical research, maternal risk factors such as diabetes and hypertensive disease were identified as possible risk factors and are taken into consideration in antenatal care. However, medical practitioners and researchers suspect possible relationships between trends in maternal demographics, antenatal care and pregnancy information of current stillbirth in consideration [2]. Although medical data and knowledge is available appropriate computing techniques to analyze the data may lead to identification of high risk groups. In this paper we use an unsupervised clustering technique called Growing Self organizing Map (GSOM) to analyse the stillbirth data and present patterns which can be important to medical researchers.

Knowledge and perceptions of newly graduated medical practitioners in Malaysia of their role in medical care of people with developmental disabilities

Improving content and consistency on developmental disabilities in undergraduate medical curricula has been recommended as a means of improving health outcomes for people with developmental disabilities. Although often the subject of studies in Western countries, little is known about content on developmental disabilities in undergraduate curricula in developing countries. A study was undertaken to: (1) explore content and experience with developmental disabilities received in undergraduate training by medical practitioners in Malaysia; and (2) explore perceptions of their role in the identification and management of developmental disabilities in practice. Comparisons were made according to location of training. Data were collected using a 107-item questionnaire that was administered to 230 newly graduated house officers on their first rotations in seven public hospitals in Peninsula Malaysia. Deficits and inconsistencies were indicated in both content and experience of developmental disabilities during training. Uncertainty about their role in the identification and management of developmental disabilities was evident. Greater inconsistencies and deficits were evident for respondents trained in Eastern and Middle Eastern countries compared with those trained in Malaysia and Western countries. Results suggest a need for increased content, consistency, and intentional exposure in relation to developmental disabilities during undergraduate training across all training settings.

Are material safety data sheets (MSDS) useful in the diagnosis and management of occupational contact dermatitis?

This study assesses both the success of medical practitioners in accessing hazardous substances' information from product manufacturers and the accuracy and clinical usefulness of Material Safety Data Sheets (MSDS) presented by workers with suspected occupational contact dermatitis (OCD). 00 consecutively presented MSDS were collected from 42 workers attending an occupational dermatology clinic. Product manufacturers were contacted to verify ingredients. MSDS were evaluated for compliance with the Australian criteria for listing of OCD relevant information (sensitizers present at a concentration > or =1%, irritants present at a concentration > or =20%), and for clinical usefulness. All sensitizers were checked for clinical relevance to the worker's dermatitis. Manufacturers supplied product constituents for 77/100 MSDS. 58 MSDS satisfied the Australian standard. 57/58 MSDS were deemed clinically useful. Irritants were listed for 19/23 MSDS and sensitizers were listed for 30/68 MSDS (P = 0.001). 3 MSDS contained sensitizers, which were clinically relevant to the presenting worker's dermatitis, 1 appropriately listed, 1 present at > or =1% but not listed, and 1 present at <1% in the product and therefore, not required to be listed. Sensitizers are frequently omitted from MSDS and clinicians are often unsuccessful in obtaining crucial information from manufacturers. MSDS are inadequate for the protection and diagnosis of workers with suspected OCD.

Stabilized sparse ordinal regression for medical risk stratification

The recent wide adoption of electronic medical records (EMRs) presents great opportunities and challenges for data mining. The EMR data are largely temporal, often noisy, irregular and high dimensional. This paper constructs a novel ordinal regression framework for predicting medical risk stratification from EMR. First, a conceptual view of EMR as a temporal image is constructed to extract a diverse set of features. Second, ordinal modeling is applied for predicting cumulative or progressive risk. The challenges are building a transparent predictive model that works with a large number of weakly predictive features, and at the same time, is stable against resampling variations. Our solution employs sparsity methods that are stabilized through domain-specific feature interaction networks. We introduces two indices that measure the model stability against data resampling. Feature networks are used to generate two multivariate Gaussian priors with sparse precision matrices (the Laplacian and Random Walk). We apply the framework on a large short-term suicide risk prediction problem and demonstrate that our methods outperform clinicians to a large margin, discover suicide risk factors that conform with mental health knowledge, and produce models with enhanced stability. © 2014 Springer-Verlag London.

Classification of healthcare data using genetic fuzzy logic system and wavelets

Healthcare plays an important role in promoting the general health and well-being of people around the world. The difficulty in healthcare data classification arises from the uncertainty and the high-dimensional nature of the medical data collected. This paper proposes an integration of fuzzy standard additive model (SAM) with genetic algorithm (GA), called GSAM, to deal with uncertainty and computational challenges. GSAM learning process comprises three continual steps: rule initialization by unsupervised learning using the adaptive vector quantization clustering, evolutionary rule optimization by GA and parameter tuning by the gradient descent supervised learning. Wavelet transformation is employed to extract discriminative features for high-dimensional datasets. GSAM becomes highly capable when deployed with small number of wavelet features as its computational burden is remarkably reduced. The proposed method is evaluated using two frequently-used medical datasets: the Wisconsin breast cancer and Cleveland heart disease from the UCI Repository for machine learning. Experiments are organized with a five-fold cross validation and performance of classification techniques are measured by a number of important metrics: accuracy, F-measure, mutual information and area under the receiver operating characteristic curve. Results demonstrate the superiority of the GSAM compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus helpful as a decision support system for medical practitioners in the healthcare practice.