Algorithmic vulnerabilities and the datalogical: Early motherhood and tracking-as-care regimes

2019 ◽  
Vol 25 (2) ◽  
pp. 171-185 ◽  
Author(s):  
Helen Thornham
Keyword(s):  
Healthcare Systems ◽  
Sociotechnical Systems ◽  
Young Mothers ◽  
Maternal Subjectivity ◽  
Early Motherhood ◽  
Female Embodiment ◽  
Care Regimes

This article draws on work from a 6-month project with 12 young mothers in which we mapped and tracked ourselves and our infants. The project employed a range of methods including digital ethnographies, walk-along methods, hacking and playful experimentations. We explored, broke and tested a range of wearables and phone-based tracking apps, meeting regularly to discuss and compare our experiences and interrogate the sociotechnical systems of postnatal healthcare alongside the particular politics of certain apps and their connective affordances. In this article, I use the project as a springboard to explore what I call algorithmic vulnerabilities: the ways that the contemporary datalogical anthropocene is exposing and positioning subjects in ways that not only rarely match their own lived senses of identity but are also increasingly difficult to interrupt or disrupt. While this is not necessarily a new phenomenon (see Clough et al., 2015; Hayles, 2017), I argue that the particular algorithmic vulnerabilities within this context, which are forged in part through the ideological enmeshing of the long-running atomization of maternal and infant bodies within the healthcare systems (Crowe, 1987; Shaw, 2012; Wajcman, 1991) and the new and emergent tracking apps (Greenfield, 2016; Lupton, 2016; O’Riordan, 2017) create momentary stabilizations of sociotechnical systems in which maternal subjectivity and female embodiment become algorithmically vulnerable in affective and profound ways. These stabilizations become increasingly and problematically normative, partly because they feed and perpetuate a wider ‘taken-for granted’ sensibility of gendered neoliberalism (Gill, 2017: 609) which, as I argue, is coming to encapsulate the contemporary datalogical anthropocene. Secondly, the sociotechnical politics of the apps and the healthcare systems are revealed as co-dependent, raising a number of questions about long-term algorithmic vulnerabilities and normativities which predate the contemporary datalogical ‘turn’ and impact both practices and methods.

Quality in long-term care homes for people with dementia: an assessment of specialist provision

2006 ◽  
Vol 26 (4) ◽  
pp. 649-668 ◽  
Author(s):  
SIOBHAN REILLY ◽  
MICHELE ABENDSTERN ◽  
JANE HUGHES ◽  
DAVID CHALLIS ◽  
DAN VENABLES ◽  
...  
Keyword(s):  
Social Services ◽  
Research Evidence ◽  
Long Term Care ◽  
Term Care ◽  
North West ◽  
People With Dementia ◽  
National Service Framework ◽  
Specialist Services ◽  
Care Regimes

There has been debate for some years as to whether the best model of care for people with dementia emphasises specialist facilities or integrated service provision. Although the United Kingdom National Service Framework for Older People recommended that local authority social services departments encourage the development of specialist residential care for people with dementia, uncertainty continues as to the benefits of particular care regimes, partly because research evidence is limited. This paper examines a large number of ‘performance measures’ from long-term care facilities in North West England that have residents with dementia. Of the 287 in the survey, 56 per cent described themselves as specialist services for elderly people with mental ill-health problems (known familiarly as ‘EMI homes’). It was envisaged that EMI homes would score higher than non-EMI homes on several measures of service quality for people with dementia that were developed from research evidence and policy documents. The analysis, however, found that EMI homes performed better than non-EMI homes on only a few measures. While both home types achieved good results on some standards, on others both performed poorly. Overall, EMI and non-EMI homes offered a similar service.

scholarly journals Sharing hospital-based health technology assessments internationally

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
M Marchetti ◽  
S Daugbjerg
Keyword(s):  
New Technologies ◽  
Health Technology ◽  
Healthcare Systems ◽  
Decision Makers ◽  
Fiscal Sustainability ◽  
Specific Information ◽  
Long Term Effects ◽  
Short And Long Term ◽  
Sustainability Challenges

Abstract Issue/problem National healthcare systems worldwide are at a critical point due to the fiscal sustainability challenges faced. At the same time, healthcare systems are under pressure to meet the global demand for adaptation of medical innovations arriving into the market persistently. Description of the problem Hospitals often serve as the entry point for new technologies to the healthcare system. It is therefore extremely important that Health Technology Assessments (HTA) are available in timely order to accurately inform decision-makers on both short- and long-term effects of a health technology to avoid inappropriate investments. Hospital based HTA (HB-HTA) was developed to accommodate the need for evidence-based hospital-specific information in a timely manner. A substantial increase in the use of HB-HTA has been observed in the last years. However, only few reports are being published. A database for the structured collection of HB-HTA reports could help the dissemination and collaboration between hospitals. Effects/changes A survey answered by an international group of experts knowledgeable in HB-HTA from eighteen different countries has showed that there is an interest to realize the collection and dissemination of HB-HTA reports on an international scale. However, confidentiality and resources for a database are barriers for the dissemination of HB-HTA reports. The challenge will therefore be to overcome these barriers and design a database containing high quality, comparable and complete HB-HTA reports with proper data security, regular maintenance and user support. Lessons International collaboration in HB-HTA is the key to timely inform decision-makers without compromising the quality of the data or the methodology.

scholarly journals An algorithm to identify medication nonpersistence using electronic pharmacy databases

2015 ◽  
Vol 22 (5) ◽  
pp. 957-961 ◽  
Author(s):  
Melissa M Parker ◽  
Howard H Moffet ◽  
Alyce Adams ◽  
Andrew J Karter
Keyword(s):  
Healthcare Systems ◽  
Healthcare Operations ◽  
Timely Manner ◽  
Pharmacy Records ◽  
Grace Period ◽  
Adult Diabetes ◽  
Primary Nonadherence ◽  
Medication Taking Behavior ◽  
Novel Algorithm

Abstract Objective Identifying patients who are medication nonpersistent (fail to refill in a timely manner) is important for healthcare operations and research. However, consistent methods to detect nonpersistence using electronic pharmacy records are presently lacking. We developed and validated a nonpersistence algorithm for chronically used medications. Materials and Methods Refill patterns of adult diabetes patients (n = 14,349) prescribed cardiometabolic therapies were studied. We evaluated various grace periods (30-300 days) to identify medication nonpersistence, which is defined as a gap between refills that exceeds a threshold equal to the last days’ supply dispensed plus a grace period plus days of stockpiled medication. Since data on medication stockpiles are typically unavailable for ongoing users, we compared nonpersistence to rates calculated using algorithms that ignored stockpiles. Results When using grace periods equal to or greater than the number of days’ supply dispensed (i.e., at least 100 days), this novel algorithm for medication nonpersistence gave consistent results whether or not it accounted for days of stockpiled medication. The agreement (Kappa coefficients) between nonpersistence rates using algorithms with versus without stockpiling improved with longer grace periods and ranged from 0.63 (for 30 days) to 0.98 (for a 300-day grace period). Conclusions Our method has utility for health care operations and research in prevalent (ongoing) and new user cohorts. The algorithm detects a subset of patients with inadequate medication-taking behavior not identified as primary nonadherent or secondary nonadherent. Healthcare systems can most comprehensively identify patients with short- or long-term medication underutilization by identifying primary nonadherence, secondary nonadherence, and nonpersistence.

scholarly journals Long-term home visiting with vulnerable young mothers: an interpretive description of the impact on public health nurses

BMC Nursing ◽  
2015 ◽  
Vol 14 (1) ◽  
Author(s):  
Anne L Dmytryshyn ◽  
Susan M Jack ◽  
Marilyn Ballantyne ◽  
Olive Wahoush ◽  
Harriet L MacMillan
Keyword(s):  
Public Health ◽  
Home Visiting ◽  
Public Health Nurses ◽  
Young Mothers ◽  
Interpretive Description ◽  
The Impact

scholarly journals A Multivariate Forecasting Model for the COVID-19 Hospital Census Based on Local Infection Incidence

2021 ◽  
Author(s):  
Hieu M. Nguyen ◽  
Philip Turk ◽  
Andrew McWilliams
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Healthcare Systems ◽  
Leading Indicators ◽  
Short Term ◽  
Correction Model ◽  
Vector Error Correction ◽  
Infection Incidence ◽  
Vector Error

AbstractCOVID-19 has been one of the most serious global health crises in world history. During the pandemic, healthcare systems require accurate forecasts for key resources to guide preparation for patient surges. Fore-casting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. In the literature, only a few papers have approached this problem from a multivariate time-series approach incorporating leading indicators for the hospital census. In this paper, we propose to use a leading indicator, the local COVID-19 infection incidence, together with the COVID-19 hospital census in a multivariate framework using a Vector Error Correction model (VECM) and aim to forecast the COVID-19 hospital census for the next 7 days. The model is also applied to produce scenario-based 60-day forecasts based on different trajectories of the pandemic. With several hypothesis tests and model diagnostics, we confirm that the two time-series have a cointegration relationship, which serves as an important predictor. Other diagnostics demonstrate the goodness-of-fit of the model. Using time-series cross-validation, we can estimate the out-of-sample Mean Absolute Percentage Error (MAPE). The model has a median MAPE of 5.9%, which is lower than the 6.6% median MAPE from a univariate Autoregressive Integrated Moving Average model. In the application of scenario-based long-term forecasting, future census exhibits concave trajectories with peaks lagging 2-3 weeks later than the peak infection incidence. Our findings show that the local COVID-19 infection incidence can be successfully in-corporated into a VECM with the COVID-19 hospital census to improve upon existing forecast models, and to deliver accurate short-term forecasts and realistic scenario-based long-term trajectories to help healthcare systems leaders in their decision making.Author summaryDuring the COVID-19 pandemic, healthcare systems need to have adequate resources to accommodate demand from COVID-19 cases. One of the most important metrics for planning is the COVID-19 hospital census. Only a few papers make use of leading indicators within multivariate time-series models for this problem. We incorporated a leading indicator, the local COVID-19 infection incidence, together with the COVID-19 hospital census in a multivariate framework called the Vector Error Correction model to make 7-day-ahead forecasts. This model is also applied to produce 60-day scenario forecasts based on different trajectories of the pandemic. We find that the two time-series have a stable long-run relationship. The model has a good fit to the data and good forecast performance in comparison with a more traditional model using the census data alone. When applied to different 60-day scenarios of the pandemic, the census forecasts show concave trajectories that peak 2-3 weeks later than the infection incidence. Our paper presents this new model for accurate short-term forecasts and realistic scenario-based long-term forecasts of the COVID-19 hospital census to help healthcare systems in their decision making. Our findings suggest using the local COVID-19 infection incidence data can improve and extend more traditional forecasting models.

scholarly journals The clinical application of glibenclamide: the problem of safety and efficacy

2012 ◽  
Vol 58 (3) ◽  
pp. 65-69
Author(s):  
G K Reshed'ko ◽  
E V Khaĭkina
Keyword(s):  
Healthcare Systems ◽  
Hypoglycemic Agents ◽  
Public Healthcare ◽  
Challenging Problem ◽  
Oral Hypoglycemic Agents ◽  
Close Attention ◽  
Safety And Efficacy ◽  
The World

Well-timed, effective, and safe therapy of type 2 diabetes mellitus (DM2) is a challenging problem for public healthcare systems all over the world. Glibenclamide is currently the most popular sulfanylurea derivative prescribed to the patients presenting with DM2. High requirements to the safety profile of modern medications including oral hypoglycemic agents imply the necessity of close attention to the potential problems associated with the long-term application of glibennclamide. The present review is focused on the topical problems of safety and efficacy of glibenclamide arising from its use in the clinical practice.

scholarly journals Perinatal outcomes of newborns born from young mothers with epidural analgesia

2021 ◽  
Vol 66 (3) ◽  
pp. 118-124
Author(s):  
R. T. Shakirov ◽  
S. V. Kinzhalova ◽  
R. A. Makarov ◽  
S. V. Bychkova ◽  
N. V. Putilova ◽  
...  
Keyword(s):  
Epidural Analgesia ◽  
Cord Blood ◽  
Young Women ◽  
Neonatal Period ◽  
Young Mothers ◽  
Acid Base ◽  
Term Infants ◽  
Early Neonatal Period ◽  
Arterial Blood

Objective. To evaluate the features of the course of the early neonatal period of newborns born from young women under conditions of epidural analgesia.Material and methods. The authors conducted a prospective, comparative, randomized, longitudinal, monocenter study. Patients of Group 1 (n=25) were anesthetized with a narcotic analgesic (2% Тrimeperedine 1,0 ml intramuscularly). Patients of Group 2 (n=30) received long-term epidural analgesia (EA) with 0,2% Ropivacaine (10,0 ml/hour). All patients delivered full-term infants. The course of labor, clinical and laboratory characteristics of newborns in the early neonatal period were evaluated.Results. There were no significant differences in the duration of labor, volume of blood loss, and other characteristics between the groups. There were no clinical differences between the groups of newborns. We did not find a negative effect of epidural analgesia on the Apgar score at the 1st (p=0,166) and 5th (p=0,217) minutes of life and the neuropsychiatric status of the newborn (p=0,322). At the same time, in the group of long-term epidural analgesia, there was a tendency to a higher incidence of moderate and mild asphyxia (19,2% versus 9,5%; p=0,436). When comparing the acid-base state of umbilical cord arterial blood, significant differences were found in the following indicators: lower pH (p=0,042) and pO2 level (p=0,007) and higher pCO2 level (p=0,031) in arterial cord blood.Conclusion. Epidural analgesia during labor in young women is accompanied by a lower level of pH and pO2 and a higher level of pCO2 in the arterial cord blood as compared to a Group of Тrimeperedine, which indicates a more pronounced shift in the acid-base state of the fetal blood. When analyzing neurological outcomes in newborns, there were no statistically significant differences. However, further follow-up is required for children born from young mothers who have received long-term epidural analgesia in labor.

scholarly journals Long-Term Care and Gender Equality: Fuzzy-Set Ideal Types of Care Regimes in Europe

2020 ◽  
Vol 8 (4) ◽  
pp. 92-102 ◽  
Author(s):  
Attila Bartha ◽  
Violetta Zentai
Keyword(s):  
Gender Equality ◽  
Fuzzy Set ◽  
Long Term Care ◽  
Ideal Type ◽  
Term Care ◽  
Ideal Types ◽  
Intergenerational Ties ◽  
And Gender ◽  
Care Regimes

Recent changes in the organization of long-term care have had controversial effects on gender inequality in Europe. In response to the challenges of ageing populations, almost all countries have adopted reform measures to secure the increasing resource needs for care, to ensure care services by different providers, to regulate the quality of services, and overall to recalibrate the work-life balance for men and women. These reforms are embedded in different family ideals of intergenerational ties and dependencies, divisions of responsibilities between state, market, family, and community actors, and backed by wider societal support to families to care for their elderly and disabled members. This article disentangles the different components of the notion of ‘(de)familialization’ which has become a crucial concept of care scholarship. We use a fuzzy-set ideal type analysis to investigate care policies and work-family reconciliation policies shaping long-term care regimes. We are making steps to reveal aggregate gender equality impacts of intermingling policy dynamics and also to relate the analysis to migrant care work effects. The results are explained in a four-pronged ideal type scheme to which European countries belong. While only Nordic and some West European continental countries are close to the double earner, supported carer ideal type, positive outliers prove that transformative gender relations in care can be construed not only in the richest and most generous welfare countries in Europe.

A Metalearning Approach on Sociotechnical Systems Toward Improving Organizational Effectiveness

2019 ◽  
pp. 1-14
Author(s):  
Carlos Raul Navarro Gonzalez ◽  
Mildrend Ivett Montoya Reyes ◽  
Gabriela Jacobo Galicia ◽  
Ismael Mendoza Muñoz
Keyword(s):  
Organizational Development ◽  
Organizational Effectiveness ◽  
Social Systems ◽  
Sociotechnical Systems ◽  
Synergistic Action ◽  
Organizational Improvement ◽  
Organization Effectiveness ◽  
Technical Systems ◽  
Learning And Change

Sociotechnical systems optimize social and technical systems, but joint optimization should involve autonomy, adaptability, meaningfulness, and feedback as underlying principles. A metalearning approach in the organizational development could affect the process of managing the change inside the organization where innovation, learning, and change produce resistance amount members. A systemic approach in measuring organizational effectiveness is presented emphasizing differences with short-term and long-term measures. Differences between validating and evaluating any sociotechnical interventions is done, proposing that evaluating could help detecting strengths and weaknesses in socio-technical methodologies and provide a guidance to the organizational improvement. This chapter proposes a tool that can join multiple points of view and help to promote a synergistic action toward technical and social systems looking to impact organization effectiveness.

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