Integration issues in the healthcare supply chain

The efficacy, quality, responsiveness, and value of healthcare services provided is increasingly attracting the attention and the questioning of governments, payers, patients, and healthcare providers. Investments on integration technologies and integration of supply chain processes, has been considered as a way towards removing inefficiencies in the sector. This chapter aims to initially provide an in depth analysis of the healthcare supply chain and to present core entities, processes, and flows. Moreover, the chapter explores the concept of integration in the context of the healthcare sector, and indentifies the integration drivers, as well as challenges.

24-Karat or fool’s gold? Consequences of real team and co-acting group membership in healthcare organizations

Although theory on team membership is emerging, limited empirical attention has been paid to the effects of different types of team membership on outcomes. We propose that an important but overlooked distinction is that between membership of real teams and membership of co-acting groups, with the former being characterized by members who report that their teams have shared objectives, and structural interdependence and engage in team reflexivity. We hypothesize that real team membership will be associated with more positive individual- and organizational-level outcomes. These predictions were tested in the English National Health Service, using data from 62,733 respondents from 147 acute hospitals. The results revealed that individuals reporting the characteristics of real team membership, in comparison with those reporting the characteristics of co-acting group membership, witnessed fewer errors and incidents, experienced fewer work related injuries and illness, were less likely to be victims of violence and harassment, and were less likely to intend to leave their current employment. At the organizational level, hospitals with higher proportions of staff reporting the characteristics of real team membership had lower levels of patient mortality and sickness absence. The results suggest the need to clearly delineate real team membership in order to advance scientific understanding of the processes and outcomes of organizational teamwork.

Handling varying amounts of missing data when classifying mental-health risk levels

One of the main challenges of classifying clinical data is determining how to handle missing features. Most research favours imputing of missing values or neglecting records that include missing data, both of which can degrade accuracy when missing values exceed a certain level. In this research we propose a methodology to handle data sets with a large percentage of missing values and with high variability in which particular data are missing. Feature selection is effected by picking variables sequentially in order of maximum correlation with the dependent variable and minimum correlation with variables already selected. Classification models are generated individually for each test case based on its particular feature set and the matching data values available in the training population. The method was applied to real patients' anonymous mental-health data where the task was to predict the suicide risk judgement clinicians would give for each patient's data, with eleven possible outcome classes: zero to ten, representing no risk to maximum risk. The results compare favourably with alternative methods and have the advantage of ensuring explanations of risk are based only on the data given, not imputed data. This is important for clinical decision support systems using human expertise for modelling and explaining predictions.

Understanding data collection behaviour of mental health practitioners

Failure to detect patients at risk of attempting suicide can result in tragic consequences. Identifying risks earlier and more accurately helps prevent serious incidents occurring and is the objective of the GRiST clinical decision support system (CDSS). One of the problems it faces is high variability in the type and quantity of data submitted for patients, who are assessed in multiple contexts along the care pathway. Although GRiST identifies up to 138 patient cues to collect, only about half of them are relevant for any one patient and their roles may not be for risk evaluation but more for risk management. This paper explores the data collection behaviour of clinicians using GRiST to see whether it can elucidate which variables are important for risk evaluations and when. The GRiST CDSS is based on a cognitive model of human expertise manifested by a sophisticated hierarchical knowledge structure or tree. This structure is used by the GRiST interface to provide top-down controlled access to the patient data. Our research explores relationships between the answers given to these higher-level 'branch' questions to see whether they can help direct assessors to the most important data, depending on the patient profile and assessment context. The outcome is a model for dynamic data collection driven by the knowledge hierarchy. It has potential for improving other clinical decision support systems operating in domains with high dimensional data that are only partially collected and in a variety of combinations.

Developing an intervention to equip nurses for acute life threatening events (ALTEs) in hospital:a phenomenological approach to healthcare research

Objectives: To understand staff's experiences of acute life threatening events (ALTEs) in a pediatric hospital setting. These data will inform an intervention to equip nurses with clinical and emotional skills for dealing with ALTEs. Method: A mixed design was used in the broader research program; this paper focuses on phenomenon-focused interviews analyzed using interpretative phenomenological analysis (IPA). Results: Emerging themes included staff's relationships with patients and the impact of personhood on their ability to perform competently in an emergency. More experienced nurses described "automatic" competence generated through increased exposure to ALTEs and were able to recognize "fumbling and shaking" as a normal stress response. Designating a role was significant to staff experience of effectiveness. Key to nurses' learning experience was reflection and identifying experiences as "teachable moments." Findings were considered alongside existing theories of self-efficacy, reflective thought, and advocacy inquiry to create an experiential learning intervention involving a series of clinical and role-related scenarios. Conclusion: The phenomenological work facilitated an in-depth reading of experience. It accentuated the importance of exposure to ALTEs giving nurses experiential knowledge to prepare them for the impact of these events. Challenges included bracketing the personhood of child patients, shifting focus to clinical tasks during the pressured demands of managing an ALTE, normalizing the physiological stress response, and the need for a forum and structure for reflection and learning. An intervention will be designed to provide experiential learning and encourage nurses to realize and benefit from their embodied knowledge.

Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages.

Leadership and organisational outcomes in healthcare

This research examined to what extent and how leadership is related to organisational outcomes in healthcare. Based on the Job Demands-Resource model, a set of hypotheses was developed, which predicted that the effect of leadership on healthcare outcomes would be mediated by job design, employee engagement, work pressure, opportunity for involvement, and work-life balance. The research focused on the National Health Service (NHS) in England, and examined the relationships between senior leadership, first line supervisory leadership and outcomes. Three years of data (2008 – 2010) were gathered from four data sources: the NHS National Staff Survey, the NHS Inpatient Survey, the NHS Electronic Record, and the NHS Information Centre. The data were drawn from 390 healthcare organisations and over 285,000 staff annually for each of the three years. Parallel mediation regressions modelled both cross sectional and longitudinal designs. The findings revealed strong relationships between senior leadership and supervisor support respectively and job design, engagement, opportunity for involvement, and work-life balance, while senior leadership was also associated with work pressure. Except for job design, there were significant relationships between the mediating variables and the outcomes of patient satisfaction, employee job satisfaction, absenteeism, and turnover. Relative importance analysis showed that senior leadership accounted for significantly more variance in relationships with outcomes than supervisor support in the majority of models tested. Results are discussed in relation to theoretical and practical contributions. They suggest that leadership plays a significant role in organisational outcomes in healthcare and that previous research may have underestimated how influential senior leaders may be in relation to these outcomes. Moreover, the research suggests that leaders in healthcare may influence outcomes by the way they manage the work pressure, engagement, opportunity for involvement and work-life balance of those they lead.

Using the Big -Five personality factors in the Minnesota Multiphasic Personality Inventory, California Psychological Inventory, and Inwald Personality Inventory to predict police performance

The purpose of this study was to determine the degree to which the Big-Five personality taxonomy, as represented by the Minnesota Multiphasic Personality Inventory (MMPI), California Psychological Inventory (CPI), and Inwald Personality Inventory (IPI) scales, predicted a variety of police officer job performance criteria. Data were collected archivally for 270 sworn police officers from a large Southeastern municipality. Predictive data consisted of scores on the MMPI, CPI, and IPI scales as grouped in terms of the Big-Five factors. The overall score on the Wonderlic was included in order to assess criterion variance accounted for by cognitive ability. Additionally, a psychologist's overall rating of predicted job fit was utilized to assess the variance accounted for by a psychological interview. Criterion data consisted of supervisory ratings of overall job performance, State Examination scores, police academy grades, and termination. Based on the literature, it was hypothesized that officers who are higher on Extroversion, Conscientiousness, Agreeableness, Openness to Experience, and lower on Neuroticism, otherwise known as the Big-Five factors, would outperform their peers across a variety of job performance criteria. Additionally, it was hypothesized that police officers who are higher in cognitive ability and masculinity, and lower in mania would also outperform their counterparts. Results indicated that many of the Big-Five factors, namely, Neuroticism, Conscientiousness, Agreeableness, and Openness to Experience, were predictive of several of the job performance criteria. Such findings imply that the Big-Five is a useful predictor of police officer job performance. Study limitations and implications for future research are discussed. ^

The effects of advanced practice nurses (APNs) as intensivists in a surgical intensive care unit (SICU) on patient outcomes, healthcare charges, and apn intensivist services in the SICU

Intensive Care Units (ICUs) account for over 10 percent of all US hospital beds, have over 4.4 million patient admissions yearly, approximately 360,000 deaths, and account for close to 30% of acute care hospital costs. The need for critical care services has increased due to an aging population and medical advances that extend life. The result is efforts to improve patient outcomes, optimize financial performance, and implement models of ICU care that enhance quality of care and reduce health care costs. This retrospective chart review study examined the dose effect of APN Intensivists in a surgical intensive care unit (SICU) on differences in patient outcomes, healthcare charges, SICU length of stay, charges for APN intensivist services, and frequency of APNs special initiatives when the SICU was staffed by differing levels of APN Intensivist staffing over four time periods (T1-T4) between 2009 and 2011. The sample consisted of 816 randomly selected (204 per T1-T4) patient chart data. Study findings indicated reported ventilator associated pneumonia (VAP) rates, ventilator days, catheter days and catheter associated urinary tract infection (CAUTI) rates increased at T4 (when there was the lowest number of APN Intensivists), and there was increased pressure ulcer incidence in first two quarters of T4. There was no statistically significant difference in post-surgical glycemic control (M = 142.84, SD = 40.00), t (223) = 1.40, p = .17, and no statistically significant difference in the SICU length of stay among the time-periods (M = 3.27, SD = 3.32), t (202) = 1.02, p = .31. Charges for APN services increased over the 4 time periods from $11,268 at T1 to $51,727 at T4 when a system to capture APN billing was put into place. The number of new APN initiatives declined in T4 as the number of APN Intensivists declined. Study results suggest a dose effect of APN Intensivists on important patient health outcomes and on the number of APNs initiatives to prevent health complications in the SICU. ^

Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

Pharmacovigilance in children : detecting adverse drug reactions in routine electronic healthcare records. A systematic review

This article is protected by copyright. All rights reserved.

Pharmacovigilance in children : detecting adverse drug reactions in routine electronic healthcare records. A systematic review

This article is protected by copyright. All rights reserved.

Pharmacovigilance in children : detecting adverse drug reactions in routine electronic healthcare records. A systematic review

This article is protected by copyright. All rights reserved.

Putting Adequate Financial and Human Resources in Healthcare for Effective Universal Health Coverage in Kenya: Lessons and Experiences from China

In recent years, most low and middle-income countries, have adopted different approaches to universal health coverage (UHC), to ensure equity and financial risk protection in accessing essential healthcare services. UHC-related policies and delivery strategies are largely based on existing healthcare systems, a result of gradual development (based on local factors and priorities). Most countries have emphasized on health financing, and human resources for health (HRH) reform policies, based on good practices of several healthcare plans to deliver UHC for their population.

Health financing and labor market frameworks were used, to understand health financing, HRH dynamics, and to analyze key health policies implemented over the past decade in Kenya’s effort to achieve UHC. Through the understanding, policy options are proposed to Kenya; analyzing, and generating lessons from health financing, and HRH reforms experiences in China. Data was collected using mixed methods approach, utilizing both quantitative (documents and literature review), and qualitative (in-depth interviews) data collection techniques.

The problems in Kenya are substantial: high levels of out-of-pocket health expenditure, slow progress in expanding health insurance among informal sector workers, inefficiencies in pulling of health are revenues, inadequate deployed HRH, maldistribution of HRH, and inadequate quality measures in training health worker. The government has identified the critical role of strengthening primary health care and the National Hospital Insurance Fund (NHIF) in Kenya’s move towards UHC. Strengthening primary health care requires; re-defining the role of hospitals, and health insurance schemes, and training, deploying and retaining primary care professionals according to the health needs of the population; concepts not emphasized in Kenya’s healthcare reforms or programs design. Kenya’s top leadership commitment is urgently needed for tougher reforms implementation, and important lessons from China’s extensive health reforms in the past decade are beneficial. Key lessons from China include health insurance expansion through rigorous research, monitoring, and evaluation, substantially increasing government health expenditure, innovative primary healthcare strengthening, designing, and implementing health policy reforms that are responsive to the population, and regional approaches to strengthening HRH.