An overview of orthodontic malpractice liability based on a survey and case assessment review

The purpose of this survey study and case review was to identify 1) the common causes related to filing a malpractice claim against an orthodontist and, 2) the factors mitigating against a potential malpractice claim in the United States (U.S). The objec

Application of the Delphi Method to Identify Risks in an Acute Healthcare Setting

In efforts to mitigate risks and reduce incidences and medical legal claims, risk management programs in acute healthcare settings analyze historical data to determine root causes, improve care delivery processes and ultimately mitigate further harm. In order to maintain highly coordinated, strategic and systemic frameworks required to identify risks, risk management teams must fundamentally expand beyond grounded, compartmentalized and decentralized issues management. As a strategy to identify an applicable solution, this Major Research Project (MRP) trials foresight methods to identify risks in acute care settings. Using a foresight technique called horizon scanning, seventeen risks were identified to form a draft futures risk registry. A delphi study was conducted whereby the identified risks were rated upon by a panel of healthcare experts. Consensus was reached on eleven risks over two rounds of polling, which formed the 2025 futures risk registry; a registry that can be applicable to any comparable acute care hospital. The study concludes with three operational strategies to imbed the trailed foresight methods into routine hospital risk identification processes

Unsupervised learning for anomaly detection in Australian medical payment data

Fraudulent or wasteful medical insurance claims made by health care providers are costly for insurers. Typically, OECD healthcare organisations lose 3-8% of total expenditure due to fraud. As Australia’s universal public health insurer, Medicare Australia, spends approximately $A 34 billion per annum on the Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme, wasted spending of$A1–2.7 billion could be expected.However, fewer than 1% of claims to Medicare Australia are detected as fraudulent, below international benchmarks. Variation is common in medicine, and health conditions, along with their presentation and treatment, are heterogenous by nature. Increasing volumes of data and rapidly changing patterns bring challenges which require novel solutions. Machine learning and data mining are becoming commonplace in this field, but no gold standard is yet available. In this project, requirements are developed for real-world application to compliance analytics at the Australian Government Department of Health and Aged Care (DoH), covering: unsupervised learning; problem generalisation; human interpretability; context discovery; and cost prediction. Three novel methods are presented which rank providers by potentially recoverable costs. These methods used association analysis, topic modelling, and sequential pattern mining to provide interpretable, expert-editable models of typical provider claims. Anomalous providers are identified through comparison to the typical models, using metrics based on costs of excess or upgraded services. Domain knowledge is incorporated in a machine-friendly way in two of the methods through the use of the MBS as an ontology. Validation by subject-matter experts and comparison to existing techniques shows that the methods perform well. The methods are implemented in a software framework which enables rapid prototyping and quality assurance. The code is implemented at the DoH, and further applications as decision-support systems are in progress. The developed requirements will apply to future work in this fiel

IT Tools and Performance Indicators: A Qualitative Overview of Managerial, Organizational, Financial Strategies within Healthcare Sector

open1The work examines the different healthcare contexts in which innovation has been applied, or could be applied, resulting in cost containment and increased quality and efficiency of medical care services. In addition, the different factors influencing the adoption of information technologies in the national healthcare systems of the European Union are discussed, in particular as regards the existence of structural barriers. Innovation is defined as the creation of something still not existing, to be uses for new products and services or for more efficient processes and is therefore linked to change, because innovation requires change. Information technology (IT) is described as the acquisition, processing and storage of data by a computing product. This work qualitatively analyses use cases, which are in turn based on quantitative research methodologies (i.e. performance indicators), commonly based on the manipulation of independent variables to generate statistically analyzable data, which guarantees objectivity and provides greater data reliability. Studies have been conducted to observe current trends in access to information technology across different age groups, to detect the existence of correlations between Internet users and online healthcare information searches. In this work, several Italian initiatives for the diffusion of IT applications in the healthcare sector have been analyzed. Some of the ongoing pilot projects include the collaboration of the Politecnico di Milano, through the establishment of the Laboratory of Biomedical Technologies (TBMLab), and the Scuola Superiore Sant'Anna of Pisa, to carry out research on eHealth activities and to promote the development of home automation systems for patients with disabilities. The HHC-MOTES model should also be noted, which aims to analyze the implementation of IT in the healthcare (HHC) sector from the point of view of sustainability in the management, organizational, technological, environmental and social fields (MOTES).openRemondino, MarcoRemondino, Marc

Digital Habit Evidence

This Article explores how “habit evidence” will become a catalyst for a new form of digital proof based on the explosive growth of smart homes, smart cars, smart devices, and the Internet of Things. Habit evidence is the rule that certain sorts of semiautomatic, regularized responses to particular stimuli are trustworthy and thus admissible under the Federal Rules of Evidence (“FRE”) 406 “Habit; Routine Practice” and state equivalents. While well established since the common law, “habit” has made only an inconsistent appearance in reported cases and has been underutilized in trial practice. But intriguingly, once applied to the world of digital trails and the Internet of Things, this long dormant rule could transform our “quantified lives” into a significant new evidentiary power. In fact, habit evidence as quantified fact may become weaponized to reimagine trial practice in the digital age

Phenomenological Assessment of Integrative Medicine Decision-making and the Utility of Predictive and Prescriptive Analytics Tools

The U.S. Healthcare system is struggling to manage the burden of chronic disease, racial and socio-economic disparities, and the debilitating impact of the current global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). More patients need alternatives to allopathic or “Western” medicine focused on fighting disease with mechanism, pharmaceuticals, and invasive measures. They are seeking Integrative Medicine which focuses on health and healing, emphasizing the centrality of the patient-physician relationship. In addition to providing the best conventional care, IM focuses on preventive maintenance, wellness, improved behaviors, and a holistic care plan. This qualitative research assessed whether predictive and prescriptive analytics (artificial intelligence tools that predict patient outcomes and recommend treatments, interventions, and medications) supports the decision-making processes of IM practitioners who treat patients suffering from chronic pain. PPA was used in a few U.S. hospitals but was not widely available for IM practitioners at the time of this research. Phenomenological interviews showed doctors benefit from technology that aggregates data, providing a clear patient snapshot. PPA exposed historical information that doctors often miss. However, current systems lacked the design to manage individualized, holistic care focused on the mind, body, and spirit. Using the Future-Focused Task-Technology Fit theory, the research suggested PPA could actually do more harm than good in its current state. Future technology must be patient-focused and designed with a better understanding of the IM task and group characteristics (e.g., the unique way providers practice medicine) to reduce algorithm aversion and increase adoption. In the ideal future state, PPA will surface healthcare Big Data from multiple sources, support communication and collaboration across the patient’s support system and community of care, and track the various objective and subjective factors contributing to the path to wellness