Healthcare Quality Improvement Research Articles

Using Behavioral Insights to Strengthen Strategies for Change. Practical Applications for Quality Improvement in Healthcare.

For over 30 years, quality improvement (QI) methods have been used as a means of increasing the quality and safety of healthcare services, but with mixed success. One explanation highlighted in the literature for this outcome is the overemphasis on technical elements of change, and a failure to fully appreciate the human side of change. Behavioral insights (BI) is an approach that utilizes knowledge and tools from a broad range of scientific disciplines, such as neuroscience and behavioral psychology, to support behavior change. The aim of this paper is to explore the possibility of supplementing QI methods with tools and understanding from BI. We outline a practical case that involved applying aspects BI methods into a QI program aimed at reducing the use of intravenous antibiotics in patients accessing services at a busy university hospital in Copenhagen, Denmark. We exemplify how to use BI tools to guide the analysis of staff behaviors during standard clinical processes and develop targeted interventions aimed at increasing actions and behaviors more aligned to best clinical practice. Outcomes suggest that it is possible to combine the models and methods from BI and QI in a way that is helpful in focusing attention on the human side of change when developing strategies for change. Potential psychological barriers identified from the analysis included the following: 'default inertia,' 'decision complexity,' 'risk aversion,' and biases related to confidence, confirmation, and omission. Future quality improvement projects could benefit from integrating models and tools from BI to guide and support behavior change.

Managing the quality of primary health care in urban China: the impact of organizational and physician features.

Global health care quality improvement efforts have focussed on management practices. However, knowledge in primary care settings, especially in developing countries, such as China, is lacking. To examine the organizational and physician features associated with health care quality in China's community health centres (CHCs). We conducted a cross-sectional survey of 224 primary care physicians (PCPs) in 38 CHCs in Jinan, Tianjin, Shenzhen, and Shanghai. Clinical and prevention care quality with a 5-level scale (1 = never, 5 = always) reported by the PCPs were used to measure the quality of care. Two-level hierarchical linear models were estimated to examine the organization and physician-level variables associated with primary care quality. The average clinical care quality score was 4.08 and 3.59 for preventative care out of 5. At the organizational level, organizational culture and organizational support were the strongest predictors of physician-reported quality of care. At the physician level, professional fulfilment, psychological safety, and organizational citizenship behaviour were positively associated with care quality. Chinese CHCs clinical quality ranked high by PCPs, but the quality of preventative care provision required improvement. To improve primary care quality, managers of CHCs should implement optimal organizational structures, supportive organizational cultures, and strong organizational support at the organization level and cultivate high professional fulfilment, safe, and trustful relationships with colleagues at the physician level.

Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries

BackgroundIn low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data.MethodsWe implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software.ResultsThe model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%.ConclusionsWe demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries.

Quality Coordinators' Perspectives on Quality Improvement in Primary Healthcare in Kosovo: A Qualitative Study.

This qualitative descriptive study provides insights into the experiences of quality coordinators (QCs) in primary healthcare to inform policy and practice actions and empower QCs to enhance healthcare quality. We conducted focus group discussions with purposefully selected QCs to understand their motivations, job experiences, factors influencing healthcare quality, and suggestions for quality improvement. Content analysis and deductive coding were used to scrutinize the responses and answer the research questions. The QCs thought highly about their job performance and were motivated by both extrinsic and intrinsic factors. Clinical audits, collegial reviews, and managerial support positively affected QCs' performance. In contrast, a lack of managerial support, limited working hours, and changes in organizing work caused the opposite. Empowerment and external support positively influenced healthcare quality, whereas lack of resources, managerial support, or training had a negative influence. Suggestions to improve quality include the role of QCs, external supervision, and centralization of the QCs' network. Appointing QCs alone does not guarantee quality improvement. It is essential to ensure that QCs have the appropriate skills, tools, management support, and open communication channels. Further research is required to evaluate the effects of sex and age on QCs' performance.

From not knowing to doing: An interprofessional approach to clinician training in use of Goal Attainment Scaling (GAS) as a recovery-oriented outcome measure in a rural mental health service.

Outcome measurement and feedback are key to quality improvement in healthcare. Goal attainment scaling (GAS) is a tool that could be used to measure outcomes of mental health services delivering recovery-oriented care. The objective of this prospective study was to evaluate the effectiveness of tailored, interprofessional, multilevel and adaptable GAS training on clinician views, learning, competence, performance and confidence in the use of GAS. Thematic analysis of eight clinician participant views was done using the method proposed by Braun and Clarke (Thematic analysis: a practical guide to understanding and doing, 2022). Four main themes were generated: clinicians found that this type of training is useful, GAS influenced the way they thought about their roles in goal setting and recovery-oriented care and COVID-19 pandemic impacts. Furthermore, clinicians' skills to set scalable GAS goals with consumers and clinician confidence in using GAS improved. The results of this study show a positive impact of tailored, interprofessional, multilevel and adaptable training supporting development of clinician skills in the GAS process. The training design had a favourable effect on clinician views, learning, competence, performance and confidence of GAS as a recovery-oriented outcome measure. The approach to GAS training and use of GAS as a recovery-oriented outcome measure should be considered in response to mental health service reform.

Sustainable Healthcare Quality and Job Satisfaction through Organizational Culture: Approaches and Outcomes

This paper explores innovative strategies to enhance sustainable healthcare quality, emphasizing the pivotal role of organizational culture and placing a special focus on employee job satisfaction. Recognizing the indispensable nature of contented employees in delivering superior patient care, our discussion explores how an organizational culture that prioritizes optimized work processes, and a supportive work environment can significantly boost employee well-being. By mitigating workplace stress through the refinement of operations and the minimization of waste, healthcare organizations can foster a setting that greatly enhances job satisfaction. This enhancement, in turn, catalyzes superior patient care and underscores the healthcare system’s dedication to sustainability and the well-being of its employees. Our analysis indicates that concentrating on employee satisfaction, deeply rooted in the organizational culture, is not merely advantageous but essential for nurturing a sustainable, high-quality healthcare system. The insights offered aim to guide healthcare organizations toward embracing strategies such as Green Lean Six Sigma, with a heightened focus on improving workforce well-being through the lens of organizational culture. This approach is expected to produce a synergistic improvement in healthcare quality by promoting operational efficiency and advancing environmental sustainability in tandem.

OA42 Effect of intra-articular corticosteroid injections for osteoarthritis on subsequent use of pain medications: an instrumental variable and propensity score analysis

Abstract Background/Aims Intra-articular corticosteroid injections (IACI) are recommended in the UK by NICE as an adjunctive treatment for uncontrolled pain from osteoarthritis (OA). NICE guidance states there is no evidence for therapeutic benefit lasting beyond three months, and that more evidence is needed on effectiveness at non-knee joints. We therefore aimed to assess the longer-term effect of IACI use on pain medication prescriptions among people with hand, hip, knee or shoulder OA. Methods We conducted a cohort study using UK primary care (CPRD GOLD) data for patients aged ≥20 years with a first diagnosis of OA during 2005-2019, excluding those with prior orthopaedic surgery referral or OA diagnosed at multiple joints. Exposure to IACI was defined using Read codes. The primary outcome was incident prescribing of pain medication: oral non-steroidal anti-inflammatory drugs (NSAIDs), opioids, oral corticosteroids, paracetamol, partial opioids, and topical NSAIDs. In main analyses, an instrumental variable (IV) approach was used. Index date was six months after OA diagnosis date and IV was defined as practice preference for IACI use in the year prior to index date (single and recurrent use considered separately). Outcomes were measured over five years from index date. Covariate balance was assessed, and two-step Poisson regression used where assumptions met. In secondary analyses, propensity score (PS) matching was carried out and time-varying exposure Cox models used to estimate treatment effect. Results Of 181,818 eligible patients, 92,157 (50.7%) were retained in IV analyses of single IACI and 89,103 (49.0%) of repeat IACI. Covariates were well balanced (SMD ≤0.1) across IV groups for hand (single IACI only) and knee OA, but not hip or shoulder OA. In IV analyses of hand OA, single IACI was associated with lower 5-year cumulative incidence of several pain medications including oral corticosteroids (relative risk [RR] 0.27 [95% CI 0.10, 0.76], paracetamol (RR 0.27 [0.09, 0.78] and partial opioids (RR 0.24 [0.10, 0.60]). IACI for knee OA was associated with lower 5-year cumulative incidence of most pain medications, e.g., for partial opioids a RR 0.46 [95% CI 0.34, 0.61] and RR 0.33 [0.21, 0.51] for single and recurrent IACI, respectively. Most PS matched analyses had >20,000 included patients (all joints combined), with covariates well balanced across IACI exposure status. PS matching confirmed a reduction in incidence of several pain medications associated with single IACI, e.g., lower incidence of partial opioids following IACI for any of hand, hip, knee or shoulder OA. Conclusion Our main findings suggest IACI use for hand or knee OA may reduce subsequent need for analgesics. Among knee OA patients, generally larger reductions were observed following recurrent than single IACI. In secondary analyses, single IACI for hand, hip, knee or shoulder OA was associated with lower subsequent use of partial opioids. Disclosure S. Hawley: None. A. Prats-Uribe: Grants/research support; European Medicines Agency. G. Matharu: Grants/research support; National Institute of Health and Care Research, Academy of Medical Sciences. A. Delmestri: None. D. Prieto-Alhambra: Consultancies; AstraZeneca, UCB Biopharma. Grants/research support; European Medicines Agency, Innovative Medicines Initiative, Amgen, Chiesi-Taylor, Lilly, Janssen, Novartis, UCB Biopharma. Other; Amgen, Astrellas, Janssen, Synapse Management Partners, UCB Biopharma. A. Judge: Grants/research support; National Institute of Health and Care Research, Health Data Research UK, Versus Arthritis, Healthcare Quality Improvement Partnership (HQIP), Royal College of Physicians (RCP), Tommy's, The Health Foundation. M. Whitehouse: Royalties; Taylor Francis. Member of speakers’ bureau; Heraeus. Grants/research support; National Institute of Health and Care Research, Healthcare Quality Improvement Partnership (HQIP), Ceramtec.

Using active learning strategies during a quality improvement collaborative: exploring educational games to enhance learning among healthcare professionals.

The Breakthrough Series model uses learning sessions (LS) to promote education, professional development and quality improvement (QI) in healthcare. Staff divergences regarding prior knowledge, previous experience, preferences and motivations make selecting which pedagogic strategies to use in LS a challenge. We aimed to assess new active-learning strategies: two educational games, a card game and an escape room-type game, for training in healthcare-associated infection prevention. This descriptive case study evaluated the performance of educational strategies during a Collaborative to reduce healthcare-associated infections in Brazilian intensive care units (ICUs). A post-intervention survey was voluntarily offered to all participants in LS activities. Seven regional 2-day LS were held between October and December 2022 (six for adult ICUs and one for paediatric/neonatal ICUs). Of 194 institutions participating in a nationwide QI initiative, 193 (99.4%) participated in these activities, totalling 850 healthcare professionals. From these, 641 participants responded to the survey (75.4%). The post-intervention survey showed that the participants responded positively to the educational activities. The participants perceived the various pedagogical strategies positively, which shows the value of a broad and diverse educational approach, customised to local settings and including game-based activities, to enhance learning among healthcare professionals.

The Association of Patient-Provider Language Concordance with Healthcare Comprehension Among Mexican-origin Latino/as in Oregon

Objective: To understand the local Mexican-origin Latino community’s preference for bilingual health care providers and understand the relationship between language concordant care and comprehension of a healthcare visit. Background: Language barriers in healthcare negatively impact quality of care. Language concordant care (where a health care provider speaks the patient’s language) has been shown to result in better patient comprehension than using interpreters/translators. Study Design: This is a cross sectional study in collaboration with the General Consulate of Mexico. We developed a 20-item survey that includes questions about socio-demographics, an acculturation scale, and questions regarding language concordant care experience and preferences. Participants were aged 18 or older who had a healthcare visit within the past 12 months. We used descriptive and multivariable statistics to describe our sample and test the association of language concordant care and comprehension of the healthcare visit. Results We recruited 505 participants who all completed the survey in Spanish. Overall, 93.5% reported preference for a language concordant provider and 32.9% reported receipt of such care. Of those who reported a concordant provider visit, 87% reported a regular source of care compared to 50% among those with a discordant provider (p<0.001). In multivariable analyses, patients with a concordant provider had higher odds of understanding everything their provider said (aOR = 10.53 (95% CI: 6.60-16.81). Conclusion There is a gap between the preference for language concordant providers and the receipt of this type of health care for Oregon Latinos. Increasing access to language concordant care can contribute to improving health care quality, equity, and outcomes for Latinos in Oregon.

The use of positive deviance approach to improve health service delivery and quality of care: a scoping review

BackgroundQuality has been a persistent challenge in the healthcare system, particularly in resource-limited settings. As a result, the utilization of innovative approaches is required to help countries in their efforts to enhance the quality of healthcare. The positive deviance (PD) approach is an innovative approach that can be utilized to improve healthcare quality. The approach assumes that solutions to problems are already available within the community and identifying and sharing those solutions can help others to resolve existing issues. Therefore, this scoping review aimed to synthesize the evidence regarding the use of the PD approach in healthcare system service delivery and quality improvement programs.MethodsArticles were retrieved from six international databases. The last date for article search was June 02, 2023, and no date restriction was applied. All articles were assessed for inclusion through a title and/or abstract read. Then, articles that passed the title and abstract review were screened by reading their full texts. In case of duplication, only the full-text published articles were retained. A descriptive mapping and evidence synthesis was done to present data with the guide of the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for Scoping Reviews checklist and the results are presented in text, table, and figure formats.ResultsA total of 125 articles were included in this scoping review. More than half, 66 (52.8%), of the articles were from the United States, 11(8.8%) from multinational studies, 10 (8%) from Canada, 8 (6.4%) from the United Kingdom and the remaining, 30 (24%) are from other nations around the world. The scoping review indicates that several types of study designs can be applied in utilizing the PD approach for healthcare service and quality improvement programs. However, although validated performance measures are utilized to identify positive deviants (PDs) in many of the articles, some of the selection criteria utilized by authors lack clarity and are subject to potential bias. In addition, several limitations have been mentioned in the articles including issues in operationalizing PD, focus on leaders and senior managers and limited staff involvement, bias, lack of comparison, limited setting, and issues in generalizability/transferability of results from prospects perspective. Nevertheless, the limitations identified are potentially manageable and can be contextually resolved depending on the nature of the study. Furthermore, PD has been successfully employed in healthcare service and quality improvement programs including in increasing surgical care quality, hand hygiene practice, and reducing healthcare-associated infections.ConclusionThe scoping review findings have indicated that healthcare systems have been able to enhance quality, reduce errors, and improve patient outcomes by identifying lessons from those who exhibit exceptional practices and implementing successful strategies in their practice. All the outcomes of PD-based research, however, are dependent on the first step of identifying true PDs. Hence, it is critical that PDs are identified using objective and validated measures of performance as failure to identify true PDs can subsequently lead to failure in identifying best practices for learning and dissemination to other contextually similar settings.

Patient satisfaction with preoperative nursing care and its associated factors in surgical procedures, 2023: a cross-sectional study

BackgroundTo enhance patient satisfaction, nurses engaged in preoperative care must possess a comprehensive understanding of the most up-to-date evidence. However, there is a notable dearth of relevant information regarding the current status of preoperative care satisfaction and its impact, despite a significant rise in the number of patients seeking surgical intervention with complex medical requirements.ObjectiveTo assess patient satisfaction with preoperative nursing care and its associated factors in surgical procedures of, 2023.MethodsA cross-sectional study was conducted, and the data was collected from the randomly selected 468 patients who had undergone surgery during the study period. The collected data was entered into Epidata version 3.1 and analyzed using SPSS version 25 software.ResultsThe complete participation and response of 468 participants resulted in a response rate of 100%. Overall patient satisfaction with preoperative nursing care was 79.5%. Sex (Adjusted odds ratio (AOR): 1.14 (95% confidence interval (CI): 0.21–2.91)), payment status for treatment (AOR: 1.45 (95% CI: 0.66–2.97)), preoperative fear and anxiety (AOR: 1.01, 95% CI: 0.49–2.13)), patient expectations (AOR: 3.39, 95% CI: 2.17–7.11)), and preoperative education (AOR: 1.148, 95% CI: 0.54–2.86)) exhibited significant associations with patient satisfaction with preoperative nursing care.ConclusionIt is important to exercise caution when interpreting the level of preoperative nursing care satisfaction in this study. The significance of preoperative nursing care satisfaction lies in its reflection of healthcare quality, as even minor deficiencies in preoperative care can potentially lead to life-threatening complications, including mortality. Therefore, prioritizing the improvement of healthcare quality is essential to enhance patient satisfaction.

Implementation challenges in preeclampsia care: perspectives from health care professionals in urban Uganda

BackgroundSub-Saharan Africa bears the burden of 70% of maternal deaths worldwide, of which ∼10% are attributable to hypertensive disorders of pregnancy, primarily complications of pre-eclampsia. In other global settings, outcomes of pregnancies affected by pre-eclampsia are improved with timely and effective medical care. Objectives(s)We aimed to explore the perspectives of local healthcare professionals on how pre-eclampsia care is currently delivered in the study setting and what challenges they experience in providing prompt and safe care. We identified specific objectives of exploring stakeholder perceptions of (i) recognising pre-eclampsia and (ii) timely intervention when pre-eclampsia is diagnosed. We also explored the wider system factors, e.g. cultural, financial, and logistic challenges that healthcare professionals perceived affected their ability to deliver optimal pre-eclampsia care. Study designIndividual semi-structured interviews were conducted with healthcare professionals and stakeholders. The findings were analysed using thematic analysis. ResultsThirty-three participants contributed to the study including doctors and midwives with varying degree of clinical experience and external stakeholders. Five key themes emerged; delayed patient presentation, recognising the unwell patient with pre-eclampsia, the challenges of the existing triage system, stakeholder disconnect, and ways of learning from each other Healthcare professionals referenced an important psychosocial perspective associated with pre-eclampsia in the study setting, which may influence the likelihood of seeking care through traditional healers rather than hospital-based routes. ConclusionsWe identify the key barriers to improving maternal and neonatal outcomes of pre-eclampsia, described at both institutional level and within the wider setting. The study provides invaluable contextual information that suggests a systems-based approach to healthcare quality improvement may be effective in reducing rates of maternal and neonatal morbidity and mortality.

Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.

Adverse effects of chemotherapy often require hospital admissions or treatment management. Identifying factors contributing to unplanned hospital utilization may improve health care quality and patients' well-being. This study aimed to assess if patient-reported outcome measures (PROMs) improve performance of machine learning (ML) models predicting hospital admissions, triage events (contacting helpline or attending hospital), and changes to chemotherapy. Clinical trial data were used and contained responses to three PROMs (European Organisation for Research and Treatment of Cancer Core Quality of Life Questionnaire [QLQ-C30], EuroQol Five-Dimensional Visual Analogue Scale [EQ-5D], and Functional Assessment of Cancer Therapy-General [FACT-G]) and clinical information on 508 participants undergoing chemotherapy. Six feature sets (with following variables: [1] all available; [2] clinical; [3] PROMs; [4] clinical and QLQ-C30; [5] clinical and EQ-5D; [6] clinical and FACT-G) were applied in six ML models (logistic regression [LR], decision tree, adaptive boosting, random forest [RF], support vector machines [SVMs], and neural network) to predict admissions, triage events, and chemotherapy changes. The comprehensive analysis of predictive performances of the six ML models for each feature set in three different methods for handling class imbalance indicated that PROMs improved predictions of all outcomes. RF and SVMs had the highest performance for predicting admissions and changes to chemotherapy in balanced data sets, and LR in imbalanced data set. Balancing data led to the best performance compared with imbalanced data set or data set with balanced train set only. These results endorsed the view that ML can be applied on PROM data to predict hospital utilization and chemotherapy management. If further explored, this study may contribute to health care planning and treatment personalization. Rigorous comparison of model performance affected by different imbalanced data handling methods shows best practice in ML research.

An Equity Agenda for the Field of Health Care Quality Improvement.

On the occasion of the 20th anniversary of the publication of To Err Is Human: Building a Safer Health System (IOM, 2000) and Crossing the Quality Chasm: A New Health System for the 21st Century (IOM, 2001), the National Academy of Medicine convened the leaders of seven prominent U.S. health care quality organizations to […]

Improving Healthcare Quality and Clinical Outcomes for Persons with Dementia in the Sub-Acute Hospital Through Person-Centered Care Practice.

Person-centered care is considered beneficial for persons with dementia. To evaluate the impact of a person-centered knowledge translation intervention on the quality of healthcare and outcomes for persons with dementia. Over nine months, sub-acute hospital nursing, allied health, and medical staff (n = 90) participated in online and/or face-to-face person-centered education and were supported by senior nursing, allied health, and medical staff champions (n = 8) to implement person-centered healthcare. The quality of healthcare service, ward climate and care delivery were evaluated pre/post study intervention. In the week following hospital admission (Time 1) and week of discharge (Time 3), agitation incidence (co-primary outcome) was assessed in participants with dementia (n = 80). Participant delirium (co-primary outcome), accidents/injuries, psychotropic medicines, length of stay, readmission and discharge destination (secondary outcomes) were compared with a retrospective group (n = 77) matched on demographics, cognition and function in activities of daily living. Improvements occurred post-intervention in service quality by 17.5% (p = 0.369, phi = 0.08), ward climate by 18.1% (p = 0.291, phi = 0.08), and care quality by 50% (p = 0.000, phi = 0.37). Participant agitation did not change from Time 1 to Time 3 (p = 0.223). Relative to the retrospective group, significant reductions occurred in participant delirium (p = 0.000, phi = 0.73), incidents/injuries (p = 0.000, phi = 0.99), psychotropic medicine use (p = 0.030, phi = 0.09), and hospital readmissions within 30 days (p = 0.002, phi = 0.25), but not in discharge to home (p = 0.171). When person-centered healthcare knowledge is translated through staff education and practice support, persons with dementia can experience improved healthcare services and clinical outcomes, while healthcare services can benefit through reductions in unplanned service use.

Quality improvement in public–private partnerships in low- and middle-income countries: a systematic review

BackgroundPublic–private partnerships (PPP) are often how health improvement programs are implemented in low-and-middle-income countries (LMICs). We therefore aimed to systematically review the literature about the aim and impacts of quality improvement (QI) approaches in PPP in LMICs.MethodsWe searched SCOPUS and grey literature for studies published before March 2022. One reviewer screened abstracts and full-text studies for inclusion. The study characteristics, setting, design, outcomes, and lessons learned were abstracted using a standard tool and reviewed in detail by a second author.ResultsWe identified 9,457 citations, of which 144 met the inclusion criteria and underwent full-text abstraction. We identified five key themes for successful QI projects in LMICs: 1) leadership support and alignment with overarching priorities, 2) local ownership and engagement of frontline teams, 3) shared authentic learning across teams, 4) resilience in managing external challenges, and 5) robust data and data visualization to track progress. We found great heterogeneity in QI tools, study designs, participants, and outcome measures. Most studies had diffuse aims and poor descriptions of the intervention components and their follow-up. Few papers formally reported on actual deployment of private-sector capital, and either provided insufficient information or did not follow the formal PPP model, which involves capital investment for a explicit return on investment. Few studies discussed the response to their findings and the organizational willingness to change.ConclusionsMany of the same factors that impact the success of QI in healthcare in high-income countries are relevant for PPP in LMICs. Vague descriptions of the structure and financial arrangements of the PPPs, and the roles of public and private entities made it difficult to draw meaningful conclusions about the impacts of the organizational governance on the outcomes of QI programs in LMICs. While we found many articles in the published literature on PPP-funded QI partnerships in LMICs, there is a dire need for research that more clearly describes the intervention details, implementation challenges, contextual factors, leadership and organizational structures. These details are needed to better align incentives to support the kinds of collaboration needed for guiding accountability in advancing global health. More ownership and power needs to be shifted to local leaders and researchers to improve research equity and sustainability.

Systematic evaluation of common natural language processing techniques to codify clinical notes.

Proper codification of medical diagnoses and procedures is essential for optimized health care management, quality improvement, research, and reimbursement tasks within large healthcare systems. Assignment of diagnostic or procedure codes is a tedious manual process, often prone to human error. Natural Language Processing (NLP) has been suggested to facilitate this manual codification process. Yet, little is known on best practices to utilize NLP for such applications. With Large Language Models (LLMs) becoming more ubiquitous in daily life, it is critical to remember, not every task requires that level of resource and effort. Here we comprehensively assessed the performance of common NLP techniques to predict current procedural terminology (CPT) from operative notes. CPT codes are commonly used to track surgical procedures and interventions and are the primary means for reimbursement. Our analysis of 100 most common musculoskeletal CPT codes suggest that traditional approaches can outperform more resource intensive approaches like BERT significantly (P-value = 4.4e-17) with average AUROC of 0.96 and accuracy of 0.97, in addition to providing interpretability which can be very helpful and even crucial in the clinical domain. We also proposed a complexity measure to quantify the complexity of a classification task and how this measure could influence the effect of dataset size on model's performance. Finally, we provide preliminary evidence that NLP can help minimize the codification error, including mislabeling due to human error.

INTISARI ANALISIS PERSIAPAN PELAKSANAAN PATIENT SAFETY DI RUANG RAWAT INAP (STUDY KASUS DI RSUD KABUPATEN BIMA)

The era of globalization impact very rapidly, in the development of advanced medical technology. These conditions have an impact on medical services is very complex and it is potentially causing the error, so that the necessary improvement of health care quality. Patient safety is one important component in the quality of health care. Improved patient safety is a necessary step to prevent and minimize unwanted incident (KTD). Nurses are one of the health workers who are at the forefront of health care and have a very important role in improving patient safety. To determine attitudes on the analysis of patient safety in the inpatient unit nurse Bima district hospital and in particular to identify the components of patient safety attitudes include climate of team work, working conditions, employment decisions, and some factors that support and hinder patient safety attitudes. The research is descriptive qualitative. Subjects were 3 nurses and 3 patients with interview techniques. Data collection techniques in this study used the analysis of qualitative data. A qualitative data analysis technique is using descriptive analysis techniques to describe the data in the field which were analyzed and concluded. Components of the patient safety attitudes is climate of teamwork that showed lower patient safety attitudes, job satisfaction showed high patient safety attitudes, working conditions showed lower patient safety attitudes, for logistical support in the form of medical equipment are laboratory equipments that still lacking. Components of the patient safety attitudes were high that consist job satisfaction, and the low patient safety attitudes components were the teamwork climate and conditions of employment, because they needed additional medical equipment such as laboratory equipments.

Quality Improvement In Healthcare: A Practical Guide For Providers (Part 3)

Quality Improvement In Healthcare: A Practical Guide For Providers (Part 3)

Design as a quality improvement strategy: The case for design expertise

Bad design in safety-critical environments like healthcare can lead to users being frustrated, excluded or injured. In contrast, good design can make it easier to use a service correctly, with impacts on both the safety and efficiency of healthcare delivery, as well as the experience of patients and staff. The participative dimension of design as an improvement strategy has recently gained traction in the healthcare quality improvement literature. However, the role of design expertise and professional design has been much less explored. Good design does not happen by accident: it takes expertise and the specific reasoning that expert designers develop through practical experience and training. Here, we define design, show why poor design can be disastrous and illustrate the benefits of good design. We argue for the recognition of distinctive design expertise and describe some of its characteristics. Finally, we discuss how design could be better promoted in healthcare improvement.