1,250 research outputs found
Problematic Interactions between AI and Health Privacy
No full textProblematic Interactions Between AI and Health Privacy Nicholson Price, University of Michigan Law SchoolFollow Abstract The interaction of artificial intelligence (AI) and health privacy is a two-way street. Both directions are problematic. This Essay makes two main points. First, the advent of artificial intelligence weakens the legal protections for health privacy by rendering deidentification less reliable and by inferring health information from unprotected data sources. Second, the legal rules that protect health privacy nonetheless detrimentally impact the development of AI used in the health system by introducing multiple sources of bias: collection and sharing of data by a small set of entities, the process of data collection while following privacy rules, and the use of non-health data to infer health information. The result is an unfortunate anti-synergy: privacy protections are weak and illusory, but rules meant to protect privacy hinder other socially valuable goals. The state of affairs creates biases in health AI, privileges commercial research over academic research, and is ill-suited to either improve health care or protect patients. The health system deeply needs a new bargain between patients and the health system about the uses of patient data
Market Stability Switches in a Continuous-Time Financial Market with Heterogeneous Beliefs
No full textBy considering a financial market of fundamentalists and trend followers in which the price trend of the trend followers is formed as a weighted average of historical prices, we establish a continuous-time financial market model with time delay and examines the impact of time delay on market price dynamics. Conditions for the stability of the fundamental price in terms of agents' behavior parameters and time delay are obtained. In particular, it is found that an increase in time delay can not only destabilize the market price but also stabilize an otherwise unstable market price, leading to stability switching as delay increases. This interesting phenomena shed new light in understanding of mechanism on the market stability. When the fundamental price becomes unstable through Hopf bifurcations, suffcient conditions on the stability and global existence of the periodic solution are obtained.asset price; fundamentalists; trend followers; delay differential equations; stability; bifurcations
Price adjustment and market structure
No full textThe present thesis is concerned with the relationship between price
adjustments in response to changes in economic conditions and industrial
market structure. Its point of departure consists of abandoning the time-honoured
assumption that firms in industrial markets act as if they were
price takers. Instead, attention is focused on the determinants of price
adjustment in a more realistic industrial setting.
Following the introductory analysis, a synthesis is proposed
between the long-standing "administered prices" hypothesis, and the recent
theories associated with the "new view" of Keynes. It is suggested that
both approaches have common theoretical underpinnings which are themselves
closely related to this thesis.
The main body of analysis consists of a theoretical and an empirical
investigation. In the theoretical section, two distinct aspects of the
price adjustment decision are examined. The first concerns the comparative
statics of adjustment and involves an analysis of the factors which determine
the magnitude of price adjustments following changes in cost and demand.
Moreover, the influence of market structure on the adjustment process is
examined through its impact on the costs of search which are associated with
the pricing decision. The second, and no less important aspect of the
theoretical investigation concerns the dynamics of price adjustment. The
object of this analysis is to assess the impact of market structure on the
rate of price adjustment over time.
The two hypotheses developed in the theoretical section are put to
extensive empirical testing. The quantitative analysis involves mainly
time-series and cross-section regressions, but other statistical techniques
such as rank correlation and covariance tests are also employed.
The first of these hypotheses is that price adjustments in response
to short-run changes in demand could be attenuated relative to those occasioned by changes in marginal costs. The rationale for this asymmetry
is based on the unequal impact of search costs. The empirical findings,
whilst by no means conclusive, do not contradict this view.
The second hypothesis suggests that a high degree of industrial
concentration will be associated with high rates of price adjustment. This
is because concentration facilitates the process of dynamic co-ordination
amongst firms by reducing the costs of search. The empirical results come
out strongly in favour of this hypothesis. The consequential implications
regarding "administered prices" and the management of inflation are explored
in the concluding chapter of this thesis
The Social Cost-of-Living: Welfare Foundations and Estimation
No full textWe present a new class of social cost-of-living indices and a nonparametric framework for estimating these and other social cost-of- living indices. Common social cost-of-living indices can be understood as aggregator functions of approximations of individual cost-of-living indices. The Consumer Price Index (CPI) is the expenditure-weighted average of first-order approximations of each individual’s cost-of-living index. This is troubling for three reasons. First, it has not been shown to have a welfare economic foundation for the case where agents are heterogeneous (as they clearly are.) Second, it uses an expenditure-weighted average which downweights the experience of poor households relative to rich households. Finally, it uses only first-order approximations of each individual’s cost-of-living index, and thus ignores substitution effects. We propose a “common-scaling” social cost-of-living index, which is defined as the single scaling to everyone’s expenditure which holds social welfare constant across a price change. Our approach has an explicit social welfare foundation and allows us to choose the weights on the costs of rich and poor households. We also give a unique solution for the welfare function for the case where the weights are independent of household expenditure. A first order approximation of our social cost-of- living index nests as special cases commonly used indices such as the CPI. We also provide a nonparametric method for estimating second- order approximations (which account for substitution effects).Inflation, Social cost-of-living, Demand, Average Derivatives
Price Indexes for Drugs: A Review of the Issues
No full textPrice indexes provide a way to summarize changes in prices of individual goods and services using an aggregate statistic. An important use of these indexes is to decompose changes in spending into price and quantity components. Price indexes have roles in many areas, including in the National Income and Product Accounts and National Health Expenditure Accounts. Health economists have also used similar decompositions to inform policy debates about which levers may be used to contain cost growth. There are three particular issues that arise when discussing price and quality change. The first is deciding which particular formula and weights is most appropriate in constructing the index. Secondly, some price changes are accompanied by changes in the quality of goods. And lastly, price indexes for medical care do not have a clear link to patients’ welfare. Therefore, this paper focuses on the measurement issues, how the indexes are constructed, and how they may be used to decompose the growth in spending into price and quantity components.
The Social Cost-of-Living: Welfare Foundations and Estimation
No full textWe present a new class of social cost-of-living indices and a nonparametric framework for estimating these and other social cost-of-living indices. Common social cost-of-living indices can be understood as aggregator functions of approximations of individual cost-of-living indices. The Consumer Price Index (CPI) is the expenditure-weighted average of first-order approximations of each individual’s cost-of-living index. This is troubling for three reasons. First, it has not been shown to have a welfare economic foundation for the case where agents are heterogeneous (as they clearly are.) Second, it uses an expenditure-weighted average which downweights the experience of poor households relative to rich households. Finally, it uses only first-order approximations of each individual’s cost-of-living index, and thus ignores substitution effects. We propose a “common-scaling” social cost-of-living index, which is defined as the single scaling to everyone’s expenditure which holds social welfare constant across a price change. Our approach has an explicit social welfare foundation and allows us to choose the weights on the costs of rich and poor households. We also give a unique solution for the welfare function for the case where the weights are independent of household expenditure. A first order approximation of our social cost-of-living index nests as special cases commonly used indices such as the CPI. We also provide a nonparametric method for estimating second-order approximations (which account for substitution effects).Inflation, Social cost-of-living, Demand, Average derivatives
Death audits and reviews for reducing maternal, perinatal and child mortality (Protocol)
No full textBackground: The United Nations' Sustainable Development Goals (SDGs) include reducing the global maternal mortality rate to less than 70 per 100,000 live births and ending preventable deaths of newborns and children under five years of age, in every country, by 2030. Maternal and perinatal death audit and review is widely recommended as an intervention to reduce maternal and perinatal mortality, and to improve quality of care, and could be key to attaining the SDGs. However, there is uncertainty over the most cost-effective way of auditing and reviewing deaths: community-based audit (verbal and social autopsy), facility-based audits (significant event analysis (SEA)) or a combination of both (confidential enquiry).Objectives: To assess the impact and cost-effectiveness of different types of death audits and reviews in reducing maternal, perinatal and child mortality.Search methods: We searched the following from inception to 16 January 2019: CENTRAL, Ovid MEDLINE, Embase OvidSP, and five other databases. We identified ongoing studies using ClinicalTrials.gov and the World Health Organization (WHO) International Clinical Trials Registry Platform, and searched reference lists of included articles.Selection criteria: Cluster-randomised trials, cluster non-randomised trials, controlled before-and-after studies and interrupted time series studies of any form of death audit or review that involved reviewing individual cases of maternal, perinatal or child deaths, identifying avoidable factors, and making recommendations. To be included in the review, a study needed to report at least one of the following outcomes: perinatal mortality rate; stillbirth rate; neonatal mortality rate; mortality rate in children under five years of age or maternal mortality rate.Data collection and analysis: We used standard Cochrane Effective Practice and Organisation of Care (EPOC) group methodological procedures. Two review authors independently extracted data, assessed risk of bias and assessed the certainty of the evidence using GRADE. We planned to perform a meta-analysis using a random-effects model but included studies were not homogeneous enough to make pooling their results meaningful.Main results: We included two cluster-randomised trials. Both introduced death review and audit as part of a multicomponent intervention, and compared this to current care. The QUARITE study (QUAlity of care, RIsk management, and TEchnology) concerned maternal death reviews in hospitals in West Africa, which had very high maternal and perinatal mortality rates. In contrast, the OPERA trial studied perinatal morbidity/mortality conferences (MMCs) in maternity units in France, which already had very low perinatal mortality rates at baseline.The OPERA intervention in France started with an outreach visit to brief obstetricians, midwives and anaesthetists on the national guidelines on morbidity/mortality case management, and was followed by a series of perinatal MMCs. Half of the intervention units were randomised to receive additional support from a clinical psychologist during these meetings. The OPERA intervention may make little or no difference to overall perinatal mortality (low certainty evidence), however we are uncertain about the effect of the intervention on perinatal mortality related to suboptimal care (very low certainty evidence).The intervention probably reduces perinatal morbidity related to suboptimal care (unadjusted odds ratio (OR) 0.62, 95% confidence interval (CI) 0.40 to 0.95; 165,353 births; moderate-certainty evidence).The effect of the intervention on stillbirth rate, neonatal mortality, mortality rate in children under five years of age, maternal mortality or adverse effects was not reported.The QUARITE intervention in West Africa focused on training leaders of hospital obstetric teams using the ALARM (Advances in Labour And Risk Management) course, which included one day of training about conducting maternal death reviews. The leaders returned to their hospitals, established a multidisciplinary committee and started auditing maternal deaths, with the support of external facilitators. The intervention probably reduces inpatient maternal deaths (adjusted OR 0.85, 95% CI 0.73 to 0.98; 191,167 deliveries; moderate certainty evidence) and probably also reduces inpatient neonatal mortality within 24 hours following birth (adjusted OR 0.74, 95% CI 0.61 to 0.90;moderate certainty evidence). However, QUARITE probably makes little or no difference to the inpatient stillbirth rate (moderate certainty evidence) and may make little or no difference to the inpatient neonatal mortality rate after 24 hours, although the 95% confidence interval includes both benefit and harm (low certainty evidence). The QUARITE intervention probably increases the percent of women receiving high quality of care (OR 1.87, 95% CI 1.35 - 2.57, moderate-certainty evidence). The effect of the intervention on perinatal mortality,mortality rate in children under five years of age, or adverse effects was not reported.We did not find any studies that evaluated child death audit and review or community-based death reviews or costs.Author's conclusions: A complex intervention including maternal death audit and review, as well as development of local leadership and training, probably reduces inpatient maternal mortality in low-income country district hospitals, and probably slightly improves quality of care. Perinatal death audit and review, as part of a complex intervention with training, probably improves quality of care, as measured by perinatal morbidity related to suboptimal care, in a high-income setting where mortality was already very low.The WHO recommends that maternal and perinatal death reviews should be conducted in all hospitals globally. However, conducting death reviews in isolation may not be sufficient to achieve the reductions in mortality observed in the QUARITE trial. This review suggests that maternal death audit and review may need to be implemented as part of an intervention package which also includes elements such as training of a leading doctor and midwife in each hospital, annual recertification, and quarterly outreach visits by external facilitators to provide supervision and mentorship. The same may also apply to perinatal and child death reviews. More operational research is needed on the most cost-effective ways of implementing maternal, perinatal and paediatric death reviews in low- and middle-income countries
Are Trade Secrets Delaying Biosimilars?
No full textOn 6 March 2015, the United States Food and Drug Administration (FDA) approved, under the Biologics Price Competition and Innovation Act (BPCIA), a biosimilar of filgrastim (Neupogen), for treating chemotherapy-caused neutropenia (1). Although this action represents a step toward cheaper medical treatments, it masks systemic problems. Not only has it taken 5 years since the BPCIA\u27s passage (2), but economists estimate that even by 2020, biosimilar competition will reduce consumer prices only modestly (3). Why will price competition be so lacking? One key reason is the barrier to competitive entry created by trade secrecy in biologics manufacturing
Distributed Governance of Medical AI
No full textArtificial intelligence (AI) promises to bring substantial benefits to medicine. In addition to pushing the frontiers of what is humanly possible, like predicting kidney failure or sepsis before any human can notice, it can democratize expertise beyond the circle of highly specialized practitioners, like letting generalists diagnose diabetic degeneration of the retina. But AI doesn’t always work, and it doesn’t always work for everyone, and it doesn’t always work in every context. AI is likely to behave differently in well-resourced hospitals where it is developed than in poorly resourced frontline health environments where it might well make the biggest difference for patient care. To make the situation even more complicated, AI is unlikely to go through the centralized review and validation process that other medical technologies undergo, like drugs and most medical devices. Even if it did go through those centralized processes, ensuring high-quality performance across a wide variety of settings, including poorly resourced settings, is especially challenging for such centralized mechanisms. What are policymakers to do? This short Essay argues that the diffusion of medical AI, with its many potential benefits, will require policy support for a process of distributed governance, where quality evaluation and oversight take place in the settings of application—but with policy assistance in developing capacities and making that oversight more straightforward to undertake. Getting governance right will not be easy (it never is), but ignoring the issue is likely to leave benefits on the table and patients at risk
Risk and Resilience in Health Data Infrastructure
No full textToday’s health system runs on data. However, for a system that generates and requires so much data, the health care system is surprisingly bad at maintaining, connecting, and using those data. In the easy cases of coordinated care and stationary patients, the system works — sometimes. But when care is fragmented, fragmented data often result. Fragmented data create risks both to individual patients and to the system. For patients, fragmentation creates risks in care based on incomplete or incorrect information, and may also lead to privacy risks from a patched-together system. For the system, data fragmentation hinders efforts to improve efficiency and quality, and to drive health innovation based on collected data. Efforts to combat data fragmentation would benefit by considering the idea of health data infrastructure. Most obviously, that would be infrastructure for health data — that is, infrastructure on which health data can be stored and transmitted. But it should also be an infrastructure of health data — that is, a platform of shared data on which to base further efforts to increase the efficiency or quality of care
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