Evaluation of integration technology approaches in the healthcare supply chain

Healthcare organisations are increasingly being challenged to look at their operations and find opportunities to improve the quality, efficiency and effectiveness of their supply chain services. In light of this situation, there is an apparent need for healthcare organisations to invest in integration technologies and to achieve the integration of supply chain processes, in order to break up the historical structure characterised by numerous interfaces and the segregation of responsibilities. The aim of this paper is to take an independent look at the healthcare supply chain and identify at different levels the core entities, processes, information flows, and system integration challenges which impede supply chain quality improvements to be realised. Moreover, this paper proposes, from an information systems perspective, a framework for the evaluation of different integration technology approaches, which can be used as a potential guideline tool for assessing integration technology alternatives, in order to add value to a healthcare-supply-chain management system. Copyright © 2007 Inderscience Enterprises Ltd.

Medication errors in mental healthcare:a systematic review

Methods: It has been estimated that medication error harms 1-2% of patients admitted to general hospitals. There has been no previous systematic review of the incidence, cause or type of medication error in mental healthcare services. Methods: A systematic literature search for studies that examined the incidence or cause of medication error in one or more stage(s) of the medication-management process in the setting of a community or hospital-based mental healthcare service was undertaken. The results in the context of the design of the study and the denominator used were examined. Results: All studies examined medication management processes, as opposed to outcomes. The reported rate of error was highest in studies that retrospectively examined drug charts, intermediate in those that relied on reporting by pharmacists to identify error and lowest in those that relied on organisational incident reporting systems. Only a few of the errors identified by the studies caused actual harm, mostly because they were detected and remedial action was taken before the patient received the drug. The focus of the research was on inpatients and prescriptions dispensed by mental health pharmacists. Conclusion: Research about medication error in mental healthcare is limited. In particular, very little is known about the incidence of error in non-hospital settings or about the harm caused by it. Evidence is available from other sources that a substantial number of adverse drug events are caused by psychotropic drugs. Some of these are preventable and might probably, therefore, be due to medication error. On the basis of this and features of the organisation of mental healthcare that might predispose to medication error, priorities for future research are suggested.

Exploring value-added applications of RFID systems in industry and service sectors

Radio-frequency identification technology (RFID) is a popular modern technology proven to deliver a range of value-added benefits to achieve system and operational efficiency, as well as cost-effectiveness. The operational characteristics of RFID outperform barcodes in many aspects. One of the main challenges for RFID adoption is proving its ability to improve competitiveness. In this paper, we examine multiple real-world examples where RFID technology has been demonstrated to provide significant benefits to industry competitiveness, and also to enhance human experience in the service sector. This paper will explore and survey existing value-added applications of RFID systems in industry and the service sector, with particular focus on applications in retail, logistics, manufacturing, healthcare, leisure and the public sector. © 2012 AICIT.

"Customer" knowledge management in healthcare

This paper reports on a work-in-progress project on the management of patient knowledge in a UK general hospital. Greater involvement of patients is generally seen as crucial to the effective provision of healthcare in the future. However, this presents many challenges, especially in the light of the ageing population in most developed countries and the consequent increasing demand for healthcare. In the UK, there have been many attempts to increase patient involvement by the systematisation of patient feedback, but typically they have not been open to academic scrutiny or formal evaluation, nor have they used any knowledge management principles. The theoretical foundations for this project come first from service management and thence from customer knowledge management. Service management stresses the importance of the customer perspective. Healthcare clearly meets the definitions of a service even though it may also include some tangible elements such as surgery and provision of medication. Although regarding hospital patients purely as "customers" is a viewpoint that needs to be used with care, application of the theory offers potential benefits in healthcare. The two main elements we propose to use from the theory are the type of customer knowledge and its relationship to attributes of the quality of the service provided. The project is concerned with investigating various knowledge management systems (KMS) that are currently in use (or proposed) to systematise patient feedback in an NHS Trust hospital, to manage knowledge from and to a lesser extent about patients. The study is a mixed methods (quantitative and qualitative) action research investigation intended to answer the following three research questions: • How can a KMS be used as a mechanism to capture and evaluate patient experiences to provoke patient service change • How can the KMS assist in providing a mechanism for systematising patient engagement? • How can patient feedback be used to stimulate improvements in care, quality and safety?

Representing human expertise by the OWL web ontology language to support knowledge engineering in decision support systems

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.

Participatory design with older adults for healthcare apps

The global population of people aged 60 years and older is growing rapidly [1]. Ongoing advances in mobile technologies have the potential to improve independence and quality of life of older adults by supporting the delivery of personalised and ubiquitous healthcare solutions. Suggested healthcare reforms reflect the need for a future model of healthcare delivery wherein older adults take more responsibility for their own healthcare in their own homes in an attempt to moderate healthcare costs without impairing healthcare quality. For such a paradigm shift to be realised, the supporting technology must address the needs of older patients efficiently and effectively to ensure technology acceptance and use. We argue this is not possible without employing participatory approaches for the informed and effective design and development of such technologies and outline recommendations for engaging in participatory design with older adults with impairments based on practical experience.

Perceptions and experiences of the implementation, management, use and optimisation of electronic prescribing systems in hospital settings:protocol for a systematic review of qualitative studies

Introduction: There is increasing evidence that electronic prescribing (ePrescribing) or computerised provider/physician order entry (CPOE) systems can improve the quality and safety of healthcare services. However, it has also become clear that their implementation is not straightforward and may create unintended or undesired consequences once in use. In this context, qualitative approaches have been particularly useful and their interpretative synthesis could make an important and timely contribution to the field. This review will aim to identify, appraise and synthesise qualitative studies on ePrescribing/CPOE in hospital settings, with or without clinical decision support. Methods and analysis: Data sources will include the following bibliographic databases: MEDLINE, MEDLINE In Process, EMBASE, PsycINFO, Social Policy and Practice via Ovid, CINAHL via EBSCO, The Cochrane Library (CDSR, DARE and CENTRAL databases), Nursing and Allied Health Sources, Applied Social Sciences Index and Abstracts via ProQuest and SCOPUS. In addition, other sources will be searched for ongoing studies (ClinicalTrials.gov) and grey literature: Healthcare Management Information Consortium, Conference Proceedings Citation Index (Web of Science) and Sociological abstracts. Studies will be independently screened for eligibility by 2 reviewers. Qualitative studies, either standalone or in the context of mixed-methods designs, reporting the perspectives of any actors involved in the implementation, management and use of ePrescribing/CPOE systems in hospital-based care settings will be included. Data extraction will be conducted by 2 reviewers using a piloted form. Quality appraisal will be based on criteria from the Critical Appraisal Skills Programme checklist and Standards for Reporting Qualitative Research. Studies will not be excluded based on quality assessment. A postsynthesis sensitivity analysis will be undertaken. Data analysis will follow the thematic synthesis method. Ethics and dissemination: The study does not require ethical approval as primary data will not be collected. The results of the study will be published in a peer-reviewed journal and presented at relevant conferences.

A lean elvek alkalmazása az egészségügyi folyamatok fejlesztésében = Applying lean principles in healthcare process development

A fejlett társadalmak egészségügyi szolgáltató rendszerei napjainkban kettős kihívással néznek szembe: miközben a társadalom a szolgáltatási színvonal emelkedését, a hibák számának a csökkenését várja el, addig a költségvetési terhek miatt a költségcsökkentés is feltétlenül szükséges. Ez a kihívás nagyságában összevethető azzal, amellyel az USA autóipara nézett szembe az 1970-es évektől. A megoldást az autóipar esetében a konkurens „lean” menedzsment elvek és eszközök megértése és alkalmazása jelentette. A tanulmány arra keresi a választ, hogy vajon lehetséges-e ennek a megoldásnak az alkalmazása az egészségügy esetében is. A cikk az egészségügy problémájának bemutatása után tárgyalja a lean menedzsment kialakulását és hogy milyen módon került köztudatba. A tanulmány második felében a szakirodalomban fellelhető, a témával kapcsolatos tapasztalatokat foglalja össze, majd levonja a következtetéseket. = In developed societies healthcare service systems are facing double challenge; society expects service level to rise and the number of mistakes to drop, but at the same time, because of the overloaded budgets, cutting cost is also absolutely necessary. This challenge compares to the one the US automotive industry was facing in the 1970-s. In case of the automotive industry the solution was the comprehension and application of the principles and the tools of lean management. This study aims to answer the question whether it is possible to apply this solution also in the case of the healthcare system. The article first introduces the problems in the healthcare system, than describes the formation of lean management concept and its wide spread. The second half of the study summarizes the available knowledge in the literature and drives conclusions.

The unintended [or intended?] political consequences of electoral systems: The case of Bolivia

The present study was concerned with evaluating one basic institution in Bolivian democracy: its electoral system. The study evaluates the impact of electoral systems on the interaction between presidents and assemblies. It sought to determine whether it is possible to have electoral systems that favor multipartism but can also moderate the likelihood of executive-legislative confrontation by producing the necessary conditions for coalition building. ^ This dissertation utilized the case study method as a methodology. Using the case of Bolivia, the research project studied the variations in executive-legislative relations and political outcomes from 1985 to the present through a model of executive-legislative relations that provided a typology of presidents and assemblies based on the strategies available to them to bargain with each other for support. A complementary model that evaluated the state of their inter-institutional interaction was also employed. ^ Results indicated that executive-legislative relations are profoundly influenced by the choice of the electoral system. Similarly, the project showed that although the Bolivian mixed system for legislative elections, and executive formula favor multipartism, these electoral systems do not necessarily engender executive-legislative confrontation in Bolivia. This was mainly due to the congressional election of the president, and the formulas utilized to translate the popular vote into legislative seats. However, the study found that the electoral system has also allowed for anti-systemic forces to emerge and gain political space both within and outside of political institutions. ^ The study found that government coalitions in Bolivia that are promoted by the system of congressional election of the president and the D'Hondt system to allocate legislative seats have helped ameliorate one of the typical problems of presidential systems in Latin America: the presence of a minority government that is blocked in its capacity to govern. This study was limited to evaluating the impact of the electoral system, as the independent variable, on executive-legislative interaction. However, the project revealed a need for more theoretical and empirical work on executive-legislative bargaining models in order to understand how institutional reforms can have an impact on the incentives of presidents and legislators to form coherent coalitions. ^

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

Geographic Information Systems-Based Approaches to Study Congressional Redistricting in the United States

The ability for the citizens of a nation to determine their own representation has long been regarded as one of the most critical objectives of any electoral system. Without having the assurance of equality in representation, the fundamental nature and operation of the political system is severely undermined. Given the centuries of institutional reforms and population changes in the American system, Congressional Redistricting stands as an institution whereby this promise of effective representation can either be fulfilled or denied. The broad set of processes that encapsulate Congres- sional Redistricting have been discussed, experimented, and modified to achieve clear objectives and have long been understood to be important. Questions remain about how the dynamics which link all of these processes operate and what impact the real- ities of Congressional Redistricting hold for representation in the American system. This dissertation examines three aspects of how Congressional Redistricting in the Untied States operates in accordance with the principle of “One Person, One Vote.” By utilizing data and data analysis techniques of Geographic Information Systems (GIS), this dissertation seeks to address how Congressional Redistricting impacts the principle of one person, one vote from the standpoint of legislator accountability, redistricting institutions, and the promise of effective minority representation.

Mobile Clinics & Bundled Healthcare: An Exploration of Patient Perceptions of the SughaVazhvu Approach to Chronic Care Management

As the burden of non-communicable diseases increases worldwide, it is imperative that health systems adopt delivery approaches that will enable them to provide accessible, high-quality, and low-cost care to patients that need consistent management of their lifelong conditions. This is especially true in low- and middle-income country settings, such as India, where the disease burden is high and the health sector resources to address it are limited. The subscription-based, managed care model that SughaVazhvu Healthcare—a non-profit social enterprise operating in rural Thanjavur, Tamil Nadu—has deployed demonstrates potential for ensuring continuity of care among chronic care patients in resource-strained areas. However, its effectiveness and sustainability will depend on its ability to positively impact patient health status and patient satisfaction with the care management they are receiving. Therefore, this study is not only a program appraisal to aid operational quality improvement of the SughaVazhvu Healthcare model, but also an attempt to identify the factors that affect patient satisfaction among individuals with chronic conditions actively availing services.

Health reforms in Latin America: processes and results

Two types of health reforms in Latin America are analysed: one based on insurance and service commodification and the one referred to the unified public systems of progressive governments. Health insurance with explicit service packages has not fulfilled their purposes of universal coverage, equal access to necessary health services and improvement of health conditions but has opened health as a field of profit making for insurance companies and private health providers. The national health services as a state obligation have developed territorialized health services and widened substantially timely access to the majority of the population. The adoption of an integrated and wide social policy has an impact on population well fare. It faces some problems derived from the old health systems and the power of the insurance and medical complex.

Healthy Food, Healthy Iowans, Healthy Communities Public Health Tools to Advance Healthy, Sustainable Food Systems, June 2014

The purposes of the Healthy Food, Healthy Iowans, Healthy communities Series are to demonstrate the interconnectedness of the food system to public health issues (Part 1) and to provide tools to local public health agencies for assessing, planning, implementing and evaluating food system initiatives (Part 2).

Organisational and systems factors impacting on patient safety in acute care organisations: lessons from four multi-site research studies

Background Patient safety is concerned with preventable harm in healthcare, a subject that became a focus for study in the UK in the late 1990s. How to improve patient safety, presented both a practical and a research challenge in the early 2000s, leading to the eleven publications presented in this thesis. Research question The overarching research question was: What are the key organisational and systems factors that impact on patient safety, and how can these best be researched? Methods Research was conducted in over 40 acute care organisations in the UK and Europe between 2006 and 2013. The approaches included surveys, interviews, documentary analysis and non-participant observation. Two studies were longitudinal. Results The findings reveal the nature and extent of poor systems reliability and its effect on patient safety; the factors underpinning cases of patient harm; the cultural issues impacting on safety and quality; and the importance of a common language for quality and safety across an organisation. Across the publications, nine key organisational and systems factors emerged as important for patient safety improvement. These include leadership stability; data infrastructure; measurement capability; standardisation of clinical systems; and creating an open and fair collective culture where poor safety is challenged. Conclusions and contribution to knowledge The research presented in the publications has provided a more complete understanding of the organisation and systems factors underpinning safer healthcare. Lessons are drawn to inform methods for future research, including: how to define success in patient safety improvement studies; how to take into account external influences during longitudinal studies; and how to confirm meaning in multi-language research. Finally, recommendations for future research include assessing the support required to maintain a patient safety focus during periods of major change or austerity; the skills needed by healthcare leaders; and the implications of poor data infrastructure.