A productivity dashboard for hospitals: An empirical study

Health units are institutions which require accurate, updated information to support managerial decisions for thriving in such a critical industry. Thus, health information systems have been developed to help hospital managers steer daily operations. These systems provide not only operational support, but also key performance indicators (KPI’s) to monitor relevant areas at a time-aggregated basis. Despite the recognized value of dashboards in helping decision-makers, the literature shows a lack of proposals of productivity dashboards to assist Hospitals stakeholders. The thesis focuses on two problems: Hospital organizations need access to production and productivity information to improve access to services. Managers need production and productivity information to optimize resource allocation. The importance of addressing these issues lies in the fact that to monitor production and productivity information, is it possible to improve resource allocation. This dissertation consists of the development of dashboards to monitor information obtained from a hospital organization at the level of production and productivity, with the mission of supporting decision makers in the decision process. To properly develop the productivity dashboard, the Design Science Research (DSR) methodology was adopted to build and evaluate the artefact. It was ascertained that the production and productivity segment need more study and that the dashboards on these themes is an asset at the level of monitoring and analysis and subsequent decision-making process. The expected contribution of this research is to develop a dashboard recognized by health stakeholders as capable of better assisting them during their management duties.As unidades de saúde são instituições que requerem informações atualizadas e precisas para apoiar as decisões de gestão a fim de prosperarem numa indústria tão crítica. Assim, os sistemas de informação de saúde foram desenvolvidos para ajudar os gestores hospitalares a dirigir as operações diárias. Esses sistemas não fornecem só suporte operacional, mas também indicadores de desempenho chave (KPI’s) para monitorizar áreas relevantes numa base agregada no tempo. A tese concentra-se em dois problemas: As organizações hospitalares precisam de informações sobre produção e produtividade para melhorar o acesso aos serviços. Os gestores precisam de informações de produção e produtividade para otimizar a alocação de recursos. A importância da resolução destas questões prende-se com o facto de que ao monitorizar a informação de produção e produtividade é possível melhorar a alocação de recursos. A pesquisa consiste no desenvolvimento de painel de controlo para monitorar as informações obtidas numa organização hospitalar ao nível da produção e produtividade, com a missão de apoiar os decisores no processo de decisão. Para desenvolver adequadamente o painel de controlo de produtividade, adotou-se a metodologia Design Science Research (DSR) para construir e avaliar o artefato. Verificou-se que o segmento de produção e produtividade necessita de mais estudo e que o painel de controlo sobre estas temáticas é uma mais-valia ao nível da monotorização e análise e posterior processo de tomada de decisão. O contributo esperado é melhorar o processo de tomada de decisão nas Organizações de saúde, podendo ser útil para alertar de factos que a própria organização possa ainda desconhecer relativamente à sua operacionalidade

Informatics for Health 2017 : advancing both science and practice

Conference report, The Informatics for Health congress, 24-26 April 2017, in Manchester, UK.Introduction : The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress. Discussion : The twin programmes of “Big Data” and “Digital Health” are not always joined up by coherent policy and investment priorities. Substantial global investment in health IT and data science has led to sound progress but highly variable outcomes. Society needs an approach that brings together the science and the practice of health informatics. The goal is multi-level Learning Health Systems that consume and intelligently act upon both patient data and organizational intervention outcomes. Conclusions : Informatics for Health demonstrated the art of the possible, seen in the breadth and depth of our contributions. We call upon policy makers, research funders and programme leaders to learn from this joined-up approach.Publisher PDFPeer reviewe

Artificial Intelligence and Healthcare in India

Artificial Intelligence (AI), also referred to as the new electricity, is the emerging focus area in India. AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making. Most of the AI systems rely on historical large datasets for predicting future trends and outcomes at a pace which humans would not be able to match. The development of AI in India is in the initial stages and there is no regulatory body focused solely on AI. However, recently, Government of India has taken various initiatives related to AI such as establishment of Artificial Intelligence Task Force, formulation of NITI Aayog's National Strategy for Artificial Intelligence #AIFORALL, setting up of four Committees for AI under Ministry of Electronics and Information technology etc. Some of India’s state governments have also taken few initiatives, such as establishment of Centre of Excellence for Data Science and Artificial Intelligence (CoE-DS&AI) by Karnataka, Safe and Ethical Artificial Intelligence Policy 2020 and Face Recognition Attendance System by Tamil Nadu, AI-Powered System for monitoring driving behaviour by West Bengal, AI System to fight agricultural risks by Maharashtra etc. As with any other technology, AI brings with it a span of opportunities and challenges. In healthcare, AI could be beneficial in mining medical records; designing treatment plans; forecasting health events; assisting repetitive jobs; doing online consultations; assisting in clinical decision making; medication management; drug creation; making healthier choices and decisions; and solving public health problems etc. AI could be very helpful in areas where there is scarcity of human resources, such as rural and remote areas. AI technology has been helpful in dealing with COVID-19 in India. It has helped in preliminary screening of COVID-19 cases, containment of coronavirus, contact tracing, enforcing quarantine and social distancing, tracking of suspects, tracking the pandemic, treatment and remote monitoring of COVID-19 patients, vaccine and drug development etc. The path for adoption of AI driven healthcare in India is filled with a lot of challenges. The unstructured data sets, interoperability issues, lack of open sets of medical data, inadequate analytics solutions which could work with big data, limited funds, inadequate infrastructure, lack of manpower skilled in AI, regulatory weaknesses, inadequate framework and issues related to data protection are some of the key challenges for AI-driven healthcare. It is recommended that government should support companies to invest in AI; encourage public private partnerships in the domain of AI and Health; enact and effectively enforce laws and legislation related to AI and Health; frame policies addressing issues related to confidentiality and privacy in the AI-driven healthcare; and establish a certification system for AI-based healthcare solutions. To adopt AI-based healthcare, it is important to train workforce in AI so that they can carefully handle sensitive health information, protect data against theft and use AI systems effectively. It is also crucial that healthcare decisions based on AI solutions should have a rationale and are explainable

Complex Care Management Program Overview

This report includes brief updates on various forms of complex care management including: Aetna - Medicare Advantage Embedded Case Management ProgramBrigham and Women's Hospital - Care Management ProgramIndependent Health - Care PartnersIntermountain Healthcare and Oregon Health and Science University - Care Management PlusJohns Hopkins University - Hospital at HomeMount Sinai Medical Center -- New York - Mount Sinai Visiting Doctors Program/ Chelsea-Village House Calls ProgramsPartners in Care Foundation - HomeMeds ProgramPrinceton HealthCare System - Partnerships for PIECEQuality Improvement for Complex Chronic Conditions - CarePartner ProgramSenior Services - Project Enhance/EnhanceWellnessSenior Whole Health - Complex Care Management ProgramSumma Health/Ohio Department of Aging - PASSPORT Medicaid Waiver ProgramSutter Health - Sutter Care Coordination ProgramUniversity of Washington School of Medicine - TEAMcar

Predictive analysis in healthcare

The Emergency departments (ED) are the major entry point to the healthcare system. With the growing demand due to the increase of life expectancy and the greater number of diseases, it is mandatory for the ED’s to have a more efficient resource management in order to try and provide the best experience possible to its patients. If the resource demand is greater than the resources available, then ED crowding occurs. This phenomenon leads to several problems that affect the patient experience, like longer waiting times, lack of beds, patients in hallways, etc. One of the ways to improve patient satisfaction is through patient waiting time prediction, since it would allow for a better resource management in the ED and providing patients with a waiting time estimation on the triage increases patient satisfaction. The author collaborated with a Portuguese hospital near Lisbon using real ED data and built a prototype to predict the ED waiting time. The researcher complemented the ED original dataset with external data like weather information, DGS Announcements and number of football games, to try to find the most accurate model. To perform the prediction, the Naïve Bayes (NB) and Random Forest (RF) algorithms were applied in three different scenarios: the first one only with data from the original dataset, the second one where the number of football games and DGS announcements attributes were added and finally, a third one with the same dataset as the previous scenario but added weather information (temperature, wind, humidity and precipitation). The RF algorithm was the one with the best performance, especially in the third scenario. For this reason, the author used the RF algorithm with the variable inputs from the third scenario to perform the predictions on the prototype. The author concluded that the external data attributes added in both second and third scenarios were not the most important attributes for the waiting times, being the most important variables, the triage colors, disease category.As urgências dos hospitais são o maior ponto de entrada para o sistema de saúde. Com o aumento da esperança média de vida e o aumento do número de doenças, aumentou a necessidade e a procura dos serviços de saúde, levando a que seja importante que as urgências dos hospitais consigam fazer uma gestão eficiente dos seus recursos de forma a proporcionar a melhor experiência possível aos seus utentes. Se a procura por recursos nas urgências dos hospitais for superior aos recursos disponíveis, ocorre um fenómeno de concentração excessiva de pessoas nas urgências, o que pode causar vários problemas como por exemplo tempos de espera mais longos, falta de camas, utentes nos corredores, o que acaba por afetar a satisfação dos utentes. Uma forma de aumentar a satisfação dos utentes é através da previsão do tempo de espera nas urgências do hospital, visto que ajuda a administração do hospital a fazer uma melhor gestão dos recursos disponíveis e oferecer uma previsão do tempo de espera aos utentes leva a maior satisfação. O autor desenvolveu em conjunto com um hospital Português perto de Lisboa, usando dados reais, um protótipo que permite fazer a previsão do tempo de espera nas urgências do hospital. Para complementar os dados providenciados pelo hospital, o autor adicionou alguns atributos como informação do estado meteorológico por dia (temperatura, humidade, precipitação e vento), anúncios da Direção-Geral de Saúde (DGS) e o número de jogos de futebol das duas principais equipas de Lisboa (Sporting CP e SL Benfica) por dia. O autor aplicou os algoritmos Naive Bayes e Random Forest em três cenários diferentes: o primeiro em que apenas se utilizam os dados originais providenciados pelo hospital, o segundo em que se adicionam os atributos dos anúncios da DGS e o número de jogos de futebol e o terceiro em que para além dos atributos do cenário anterior, se adicionou os atributos relativos ao estado meteorológico do dia mencionados anteriormente. O algoritmo com melhor performance foi o Random Forest, principalmente no terceiro cenário, fator que levou a que este tenha sido o modelo escolhido para ser utilizado no protótipo. Depois de fazer as previsões do tempo de espera e analisar os resultados, podese concluir que para além do algoritmo Random Forest apresentar melhores resultados para a previsão do tempo de espera nas urgências, tendo em conta o tipo de dados fornecido, os atributos externos adicionados posteriormente e que não pertenciam ao conjunto de dados original providenciado pelo hospital, não são dos atributos que mais afetam os tempos de espera, sendo que os atributos que têm mais importância para os tempos de espera das urgências são a cor de triagem e a categoria da doença