Abstract The purpose of this work was to determine on the basis of the national hospital discharge register and cause-of-death statistics the extent of the hospital treatment required for chronic obstructive pulmonary disease (COPD) in Finland over the period 1972–2001, i.e. the use made of hospital services, factors affecting the length of stay in hospital and the correlation of length of stay with the prognosis. Different intervals within this period were taken for study according to the themes of the individual papers. The results suggest that the length of stay in hospital varies both geographically and seasonally in Finland, the shortest times being recorded in Northern Finland in summer. The main explanations for this would appear to lie in regional differences in health care resources and treatment practises and in climatic variations. The mean length of stay in hospital in the total material in 1987–1998 was nine days. The longest periods applied to cases with concurrent pneumonia or a cerebrovascular disorder. The duration of treatment for the exacerbation stage of COPD decreased by two days between 1993 and 2001, with the longest periods of treatment observed in the case of elderly women. One week of treatment with current modalities may be regarded as optimal, as this was associated with the longest interval before the next exacerbation, just over six months. About 3% of all emergency admissions ended in death, most commonly on a Friday in winter or spring. Patients admitted at a weekend died within the first 24 hours more frequently than did those admitted on a weekday. The mean duration of treatment and frequency of hospitalization increased towards the terminal stage. About one fourth of the patients had died within a year of the first admission for COPD and about a half within five years. Hospital treatment for COPD intensified in Finland during the 1990s as the numbers of hospital beds decreased. Treatment times became shorter and deaths in hospital during exacerbation became less frequent. It will be necessary from now onwards, however, to anticipate the ageing of the population and to develop treatment modalities to replace hospitalization, in order to reduce the costs accruing from this disease. Early diagnosis and outpatient rehabilitation should be developed, and special attention should be paid to appropriate treatment at the terminal stage. / Tiivistelmä Tutkimuksen tarkoituksena oli selvittää valtakunnallisen hoitoilmoitusrekisterin ja kuolemansyytilaston avulla keuhkoahtaumataudista (KAT) aiheutunutta sairaalahoitoa Suomessa 1972–2001: sairaalapalvelujen käyttöä, hoitojakson pituuteen vaikuttavia tekijöitä sekä hoitoajan yhteyttä ennusteeseen. Lähdeaineistosta valittiin erilaisia ajanjaksoja tutkimusasetelman mukaan. Tulokset viittaavat siihen, että hoitoajan pituus vaihtelee Suomessa maantieteellisesti ja vuodenaikojen mukaan: lyhyin hoitoaika on Pohjois-Suomessa kesällä. Ilmiötä selittänevät pääosin terveydenhuollon resurssien ja hoitokäytäntöjen alueelliset erot sekä ilmasto-olosuhteiden vaihtelu. Vuosina 1987–1998 keskimääräinen hoitoaika koko aineistossa oli yhdeksän vuorokautta. Jos potilaalla oli samanaikaisina sairauksina keuhkokuume tai aivoverenkiertohäiriö, nämä johtivat pisimpiin hoitoaikoihin. KAT:n pahenemisvaiheen hoitoaika lyheni kaksi vuorokautta vuodesta 1993 vuoteen 2001. Iäkkäitten naisten hoitoajat olivat pisimmät. Viikon pituinen hoitoaika nykyisillä hoitomuodoilla oli optimaalinen, sillä tällöin aika seuraavan pahenemisvaiheen hoitojakson alkuun oli pisin: vähän yli puoli vuotta. Kaikista päivystyshoitojaksoista potilaan kuolemaan päättyi kolmisen prosenttia. Yleisimmin tällainen hoitojakso päättyi potilaan kuolemaan perjantaisin ja todennäköisimmin talvella tai keväällä. Viikonloppuna sairaalaan tulleista potilaista kuoli ensimmäisen vuorokauden aikana enemmän kuin arkipäivinä tulleista. Keskimääräinen hoitoaika oli pisin ja sairaalahoito runsainta sairauden loppuvaiheessa kuoleman lähestyessä. Ensimmäisen KAT:n aiheuttaman hoitojakson jälkeen noin neljännes potilaista oli kuollut vuoden sisällä ja viiden vuoden kuluessa noin puolet. Keuhkoahtaumataudin sairaalahoito on tehostunut Suomessa 1990-luvulla sairaansijojen vähentyessä. Hoitoajat ovat lyhentyneet ja pahenemisvaiheiden sairaalakuolleisuus on vähäistä. Väestön ikääntyminen on kuitenkin ennakoitava ja sairaalaa korvaavia hoitomuotoja kehitettävä taudista aiheutuneiden kustannusten hillitsemiseksi. Varhaisdiagnostiikkaa ja avokuntoutusta on kehitettävä ja erityinen huomio kiinnitettävä sairauden loppuvaiheen asianmukaiseen hoitoon.

Focusing on the readmission agreement between Sweden and Afghanistan, this study aims to enhance our understanding of why and how states use readmission agreements and the discourse underpinning these practices. Based on interviews with key officials working in the Swedish deportation infrastructure, the findings show that the agreement is presented as a successful measure resulting in a more predictable process and increased forced returns. The agreement is a critical technique for minimizing disruptions in the deportation corridor to Afghanistan, however, not without interruptions due to the infrastructure’s reliance on many elements and the complexity of bilateral cooperation. The discursive practices, including “problem” representations and assumptions justifying the agreement, can be questioned considering that most Afghans abscond or travel to another Schengen country instead of returning. The absence of an agreement evaluation further necessitates calling the increased governmental focus on readmission agreements into question. The study contributes to deconstructing governmental rationalities through a novel methodology of studying deportation and readmission.

Background: Diabetes mellitus (DM) affects more than 29.1 million Americans. Standardized clinical practice guidelines recommended by regulatory healthcare agencies are the standard of care for diabetic patients and must be adhered to by healthcare professionals providing care. Purpose: The purpose of this quality improvement project was to identify Centers for Medicare and Medicaid Services’, Joint Commission on Accreditation of Healthcare Organization’s, and other professional healthcare organizations’ guidelines for nurses’ knowledge of evidence-based discharge practices; determine level of nurses’ knowledge on evidence-based discharge practice process; develop a quality improvement plan, including development of an evidence-based guideline for diabetic discharge instructions; present guideline to stakeholders; implement the guideline in fall of 2017; and evaluate nursing compliance with the guideline at a for-profit adult care hospital in South Florida. Theoretical Framework: The chronic care model was utilized as the framework. This model has been used for improving practice and preventing many chronic illnesses. Methods: Two quantitative nonparametric descriptive designs were used, the Wilcoxon signed- rank test and a paired t test. An online demographic survey and pre- and posttest surveys were administered to determine nurses’ knowledge of diabetes discharge guideline practices. The Appraisal of Guidelines for Research and Evaluation II (AGREE II) evaluation tool evaluated the guideline, and data were analyzed with Wilcoxon and paired t tests. Results: A statistically significant difference was found in the pre-posttest survey responses for question 5 (p=0.046 Wilcoxon; p=0.041t test), and question 13 (p= 0.022 Wilcoxon; p=0.018 t test), indicating improvement. With the AGREE II tool, the multidisciplinary team evaluated the guideline at 100%, and 76% of Advanced Practice Registered Nurses (APRNs) and Registered Nurses (RNs) demonstrated compliance with guideline use. Conclusion: A standardized diabetic discharge guideline incorporated into the hospital’s discharge process provided APRNs and RNs with tools for educating and providing diabetic patients for increase in quality of life after discharge. The guideline was recommended by the administrative team for continued use throughout the hospital. Implementation of an evidence-based standardized diabetic discharge guideline to promote nurses’ adherence results in effective nursing practices and an informed patient population.

Indiana University-Purdue University Indianapolis (IUPUI) / With the increasing availability of Electronic Health Records (EHRs) and advances in deep learning techniques, developing deep predictive models that use EHR data to solve healthcare problems has gained momentum in recent years. The majority of clinical predictive models benefit from structured data in EHR (e.g., lab measurements and medications). Still, learning clinical outcomes from all possible information sources is one of the main challenges when building predictive models. This work focuses mainly on two sources of information that have been underused by researchers; unstructured data (e.g., clinical notes) and a patient network. We propose a novel hybrid deep learning model, DeepNote-GNN, that integrates clinical notes information and patient network topological structure to improve 30-day hospital readmission prediction. DeepNote-GNN is a robust deep learning framework consisting of two modules: DeepNote and patient network. DeepNote extracts deep representations of clinical notes using a feature aggregation unit on top of a state-of-the-art Natural Language Processing (NLP) technique - BERT. By exploiting these deep representations, a patient network is built, and Graph Neural Network (GNN) is used to train the network for hospital readmission predictions. Performance evaluation on the MIMIC-III dataset demonstrates that DeepNote-GNN achieves superior results compared to the state-of-the-art baselines on the 30-day hospital readmission task. We extensively analyze the DeepNote-GNN model to illustrate the effectiveness and contribution of each component of it. The model analysis shows that patient network has a significant contribution to the overall performance, and DeepNote-GNN is robust and can consistently perform well on the 30-day readmission prediction task. To evaluate the generalization of DeepNote and patient network modules on new prediction tasks, we create a multimodal model and train it on structured and unstructured data of MIMIC-III dataset to predict patient mortality and Length of Stay (LOS). Our proposed multimodal model consists of four components: DeepNote, patient network, DeepTemporal, and score aggregation. While DeepNote keeps its functionality and extracts representations of clinical notes, we build a DeepTemporal module using a fully connected layer stacked on top of a one-layer Gated Recurrent Unit (GRU) to extract the deep representations of temporal signals. Independent to DeepTemporal, we extract feature vectors of temporal signals and use them to build a patient network. Finally, the DeepNote, DeepTemporal, and patient network scores are linearly aggregated to fit the multimodal model on downstream prediction tasks. Our results are very competitive to the baseline model. The multimodal model analysis reveals that unstructured text data better help to estimate predictions than temporal signals. Moreover, there is no limitation in applying a patient network on structured data. In comparison to other modules, the patient network makes a more significant contribution to prediction tasks. We believe that our efforts in this work have opened up a new study area that can be used to enhance the performance of clinical predictive models.

Mood disorders are often linked with concurrent partner relational distress. The present study compared the cost effectiveness of treating mood disorder alone versus when the condition is comorbid with partner relational distress. Cigna, a leading health insurance management company in the US, provided outpatient data. Participants included patients with solely a mood disorder diagnosis (n = 72,712) and those with both a mood disorder and a comorbid partner relational distress diagnosis (n = 113, including 69 females and 44 males). These participants were treated in outpatient settings throughout the US. These numbers are surprisingly low considering the extensive literature showing a strong relationship between mood disorder and partner relational distress. A multivariate general linear model and binary logistic regressions were used to analyze the data. Results indicate that having a mood disorder present with a partner relational distress disorder significantly increased the average cost of care by about $471 per person compared to having solely a mood disorder. For mood disorders alone, there were also differences in cost effectiveness and readmission for mood disorders by professional license type, age, and gender with counselors being the most cost effective and medical doctors being the least (60% more costly). The treatment modality used impacted readmission rates, with family therapy having the lowest (8.54%) and mixed therapy having the highest (33.54%). Due to the small sample size, we were unable to determine the significance of subsequent analyses for comorbid disorders. Clinical implications and future directions for research will be discussed.

Generalized anxiety disorder (GAD) is frequently found in primary care settings and is highly prevalent among adolescents. The purpose of this study was to examine the cost effectiveness by provider type and therapy modality in treating adolescents (ages 13-17) with a GAD diagnosis (DSM-IV 300.02). A national insurance company in the United States provided outpatient and unidentifiable data for adolescent GAD cases (n = 2,932). These cases were used to analyze the cost effectiveness, total cost, treatment length, dropout, and readmission rates for the treatment of adolescents with GAD. Descriptive statistics signify that the mean cost of treatment for GAD in the first episode of care across all provider types is $439.28. Results revealed significant differences in cost effectiveness, total cost, treatment length, and readmission rates by provider type and therapy modality. MFTs and counselors were most cost effective, had the lowest total cost and number of sessions, as well as the lowest readmission rate among the provider types. In contrast, MSWs and psychologists were the least cost effective, had the highest number of sessions and the highest readmission rate. Therapy modality comparisons indicated that family therapy is most cost effective followed by individual, then mixed therapy modalities. Significantly fewer sessions were found when conducting family therapy upon treating adolescents with GAD. There were no significant differences in dropout by provider type, therapy modality or age group. The results of total cost by gender were also insignificant. Professional and clinical implications and future directions for research will be discussed.

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No description available.

No / There are few data on the associations between anaemia, allogeneic blood transfusion (ABT), patient blood management, and outcome after arthroplasty in the UK. National agencies nevertheless instruct NHS Trusts to implement blood conservation measures including preoperative anaemia management. Internationally, blood management programmes show encouraging results. METHODS: We retrospectively audited 717 primary hip or knee arthroplasties in a UK general hospital and conducted regression analyses to identify outcome predictors. We used these data to modify previously published algorithms for UK practice and audited its introduction prospectively. The retrospective audit group served as a control. RESULTS: Preoperative haemoglobin (Hb) concentration predicted ABT (odds ratio 0.25 per 1 g dl(-1), P<0.001). It also predicted the length of stay (LOS, effect size -0.7 days per 1 g dl(-1), P=0.004) independently of ABT, including in non-anaemic patients. Patient blood management implementation was associated with lower ABT rates for hip (23-7%, P<0.001) and knee (7-0%, P=0.001) arthroplasty. LOS for total hip replacement and total knee replacement decreased from 6 (5-8) days to 5 (3-7) and 4 (3-6) days, respectively, after algorithm implementation (P<0.001). The all-cause re-admission rate within 90 days decreased from 13.5% (97/717) before to 8.2% (23/281) after algorithm implementation (P=0.02). CONCLUSIONS: We conclude that preoperative Hb predicts markers of arthroplasty outcome in UK practice. A systematic approach to optimize Hb mass before arthroplasty and limit Hb loss perioperatively was associated with improved outcome up to 90 days after discharge.

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