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1.
Artigo em Inglês | MEDLINE | ID: mdl-33082082

RESUMO

DESCRIPTION OF PROBLEM: Streamlining communication between radiology and referring services is vital to ensure appropriate care with minimal delays. Increased subspecialization has led to compartmentalization of the radiology department with many physicians working in disparate areas. At our hospital, we anecdotally noted that a significant portion of incoming phone calls were misdirected to the wrong workstations. This resulted in wasted time, unnecessary interruptions, and delays in care because the referring clinicians could not efficiently navigate the radiology department staffing structure. Our quality improvement project involved developing a web-based tool allowing the emergency department (ED) to more efficiently contact the appropriate radiology desk and reduce misdirected phone calls. INSTITUTIONAL APPROACH EMPLOYED TO ADDRESS THE PROBLEM: Surveys were sent to radiology residents and ED providers (attendings, residents, physician assistants) to assess how often phone calls were misdirected to the wrong radiology station. Radiology residents were asked which stations received the most misdirected phone calls, and what station the caller was often looking for. ED providers were asked which stations they intended when they were told they called the wrong station, and a series of questions in the survey assessed their knowledge of commonly called radiology station (Plain Film, CT Body, Ultrasound, Neuoradiology, Pediatrics, and Overnight Desk). ED and radiology physicians worked together to design a simple, easily accessed web-based tool that allowed the ED clinicians to determine which station should be called during for each hour of the day, which integrated differences in staffing by radiology throughout the day. After the tool had been implemented for 8 months, surveys were again sent to radiology residents and ED clinicians asking the same questions as before to assess for any significant change in response. Additional questions were added to the ED survey to assess awareness of the new tool. DESCRIPTION OF OUTCOMES IN CHANGE OF PRACTICE: An interactive, easily updated schedule with optimal contact numbers was made available through the ED intranet. The design allowed for easy modification of contact numbers over time to accommodate changes in coverage location or staffing models. Prior to implementation contact information was presented on a static screen, which was unable to be changed and included multiple incorrect and defunct numbers. Additionally, contact defaulted to a general radiology pager, which was carried by a resident only responsible for plain films for most of the day. Numbers included in the new intranet tool were all pertinent reading room stations, all scheduling desks, and all technologist workspaces. Different schedules were provided for weekdays and weekends. Initial survey results showed that prior to the intervention, 74% of radiology residents said they received misdirected phone calls at least twice a day, and 57.9% of ED respondents reached the wrong recipient at least once per day. Frequencies of misdirected calls dropped to 58.4% of radiology residents (P = 0.37) and 17.9% of ED respondents (P < 0.01) on follow-up surveys 8 months after the tool was established. After establishing the new tool, 82.1% of ED respondents were aware of the new intranet contact tool and were using it to contact radiology. On the series of questions assessing ED respondents' knowledge of radiology numbers, over 50% of respondents knew the correct answer or answered using the call sheet after implementation; this resulted in statistically significant increases in accuracy for Body, Neuroradiology, and Pediatric radiology stations. Furthermore, with the exception of ED plain films, there was a statistically significant reduction in number of responses who said the general radiology pager should be called for reads. Fifty percent of radiology residents believed there was a reduction in the number of misdirected phone calls from the ED with this tool. CONCLUSION, LIMITATIONS, AND DESCRIPTIONS OF FUTURE DIRECTIONS: Our tool was successful in accomplishing multiple goals. First, over 80% of ED respondents adopted the new tool. Second, the number of misdirected phone calls based on the subjective perception of ED respondents and radiology residents was reduced. Third, we objectively improved the ED respondents' behavior pattern in contacting the radiology department by either calling the correct number using the call tool, and by reducing the number of respondents who use the pager. Going forward, we hope to be able to expand use of this tool throughout the hospital in order to provide more timely and efficient care with other services by streamlining access between referring services and the appropriate radiology recipients.

2.
JAMA Intern Med ; 180(10): 1328-1333, 2020 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-32744612

RESUMO

Importance: As coronavirus disease 2019 (COVID-19) spread throughout the US in the early months of 2020, acute care delivery changed to accommodate an influx of patients with a highly contagious infection about which little was known. Objective: To examine trends in emergency department (ED) visits and visits that led to hospitalizations covering a 4-month period leading up to and during the COVID-19 outbreak in the US. Design, Setting, and Participants: This retrospective, observational, cross-sectional study of 24 EDs in 5 large health care systems in Colorado (n = 4), Connecticut (n = 5), Massachusetts (n = 5), New York (n = 5), and North Carolina (n = 5) examined daily ED visit and hospital admission rates from January 1 to April 30, 2020, in relation to national and the 5 states' COVID-19 case counts. Exposures: Time (day) as a continuous variable. Main Outcomes and Measures: Daily counts of ED visits, hospital admissions, and COVID-19 cases. Results: A total of 24 EDs were studied. The annual ED volume before the COVID-19 pandemic ranged from 13 000 to 115 000 visits per year; the decrease in ED visits ranged from 41.5% in Colorado to 63.5% in New York. The weeks with the most rapid rates of decrease in visits were in March 2020, which corresponded with national public health messaging about COVID-19. Hospital admission rates from the ED were stable until new COVID-19 case rates began to increase locally; the largest relative increase in admission rates was 149.0% in New York, followed by 51.7% in Massachusetts, 36.2% in Connecticut, 29.4% in Colorado, and 22.0% in North Carolina. Conclusions and Relevance: From January through April 2020, as the COVID-19 pandemic intensified in the US, temporal associations were observed with a decrease in ED visits and an increase in hospital admission rates in 5 health care systems in 5 states. These findings suggest that practitioners and public health officials should emphasize the importance of visiting the ED during the COVID-19 pandemic for serious symptoms, illnesses, and injuries that cannot be managed in other settings.


Assuntos
Infecções por Coronavirus , Assistência à Saúde/tendências , Serviço Hospitalar de Emergência , Hospitalização/estatística & dados numéricos , Controle de Infecções , Pandemias , Pneumonia Viral , Adulto , Betacoronavirus , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/terapia , Estudos Transversais , Serviço Hospitalar de Emergência/organização & administração , Serviço Hospitalar de Emergência/tendências , Feminino , Humanos , Controle de Infecções/métodos , Controle de Infecções/organização & administração , Masculino , Inovação Organizacional , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Pneumonia Viral/terapia , Estudos Retrospectivos , Estados Unidos/epidemiologia
3.
Sci Rep ; 9(1): 4460, 2019 03 14.
Artigo em Inglês | MEDLINE | ID: mdl-30872757

RESUMO

Lyme disease (LD) is the most common tick-borne illness in the United States. Although appropriate antibiotic treatment is effective for most cases, up to 20% of patients develop post-treatment Lyme disease syndrome (PTLDS). There is an urgent need to improve clinical management of LD using precise understanding of disease and patient stratification. We applied machine-learning to electronic medical records to better characterize the heterogeneity of LD and developed predictive models for identifying medications that are associated with risks of subsequent comorbidities. For broad disease categories, we identified 3, 16, and 17 comorbidities within 2, 5, and 10 years of diagnosis, respectively. At a higher resolution of ICD-9 codes, we identified known associations with LD including chronic pain and cognitive disorders, as well as particular comorbidities on a timescale that matched PTLDS symptomology. We identified 7, 30, and 35 medications associated with risks of these comorbidities within 2, 5, and 10 years, respectively. For instance, the first-line antibiotic doxycycline exhibited a consistently protective association for typical symptoms of LD, including backache. Our approach and findings may suggest new hypotheses for more personalized treatments regimens for LD patients.


Assuntos
Antibacterianos/efeitos adversos , Doença de Lyme/complicações , Doença de Lyme/tratamento farmacológico , Doença de Lyme/epidemiologia , Adulto , Idoso , Antibacterianos/uso terapêutico , Comorbidade , Doxiciclina/efeitos adversos , Doxiciclina/uso terapêutico , Registros Eletrônicos de Saúde , Feminino , Fluticasona/efeitos adversos , Humanos , Modelos Logísticos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Furoato de Mometasona/efeitos adversos , Cidade de Nova Iorque/epidemiologia , Fatores de Risco , Análise de Sobrevida , Deficiência de Vitamina D/etiologia
4.
AJR Am J Roentgenol ; 212(4): 859-866, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30779671

RESUMO

OBJECTIVE: Clinical decision support (CDS) tools have been shown to reduce inappropriate imaging orders. We hypothesized that CDS may be especially effective for house staff physicians who are prone to overuse of resources. MATERIALS AND METHODS: Our hospital implemented CDS for CT and MRI orders in the emergency department with scores based on the American College of Radiology's Appropriateness Criteria (range, 1-9; higher scores represent more-appropriate orders). Data on CT and MRI orders from April 2013 through June 2016 were categorized as pre-CDS or baseline, post-CDS period 1 (i.e., intervention with active feedback for scores of ≤ 4), and post-CDS period 2 (i.e., intervention with active feedback for scores of ≤ 6). Segmented regression analysis with interrupted time series data estimated changes in scores stratified by house staff and non-house staff. Generalized linear models further estimated the modifying effect of the house staff variable. RESULTS: Mean scores were 6.2, 6.2, and 6.7 in the pre-CDS, post-CDS 1, and post-CDS 2 periods, respectively (p < 0.05). In the segmented regression analysis, mean scores significantly (p < 0.05) increased when comparing pre-CDS versus post-CDS 2 periods for both house staff (baseline increase, 0.41; 95% CI, 0.17-0.64) and non-house staff (baseline increase, 0.58; 95% CI, 0.34-0.81), showing no differences in effect between the cohorts. The generalized linear model showed significantly higher scores, particularly in the post-CDS 2 period compared with the pre-CDS period (0.44 increase in scores; p < 0.05). The house staff variable did not significantly change estimates in the post-CDS 2 period. CONCLUSION: Implementation of active CDS increased overall scores of CT and MRI orders. However, there was no significant difference in effect on scores between house staff and non-house staff.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Imagem por Ressonância Magnética/estatística & dados numéricos , Corpo Clínico Hospitalar/estatística & dados numéricos , Padrões de Prática Médica/estatística & dados numéricos , Tomografia Computadorizada por Raios X/estatística & dados numéricos , Feedback Formativo , Humanos , Sistemas de Registro de Ordens Médicas , Pessoa de Meia-Idade , Estudos Retrospectivos
5.
BioData Min ; 12: 3, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30728857

RESUMO

Background: The opioid epidemic in the United States is averaging over 100 deaths per day due to overdose. The effectiveness of opioids as pain treatments, and the drug-seeking behavior of opioid addicts, leads physicians in the United States to issue over 200 million opioid prescriptions every year. To better understand the biomedical profile of opioid-dependent patients, we analyzed information from electronic health records (EHR) including lab tests, vital signs, medical procedures, prescriptions, and other data from millions of patients to predict opioid substance dependence. Results: We trained a machine learning model to classify patients by likelihood of having a diagnosis of substance dependence using EHR data from patients diagnosed with substance dependence, along with control patients with no history of substance-related conditions, matched by age, gender, and status of HIV, hepatitis C, and sickle cell disease. The top machine learning classifier using all features achieved a mean area under the receiver operating characteristic (AUROC) curve of ~ 92%, and analysis of the model uncovered associations between basic clinical factors and substance dependence. Additionally, diagnoses, prescriptions, and procedures prior to the diagnoses of substance dependence were analyzed to elucidate the clinical profile of substance-dependent patients, relative to controls. Conclusions: The predictive model may hold utility for identifying patients at risk of developing dependence, risk of overdose, and opioid-seeking patients that report other symptoms in their visits to the emergency room.

6.
NPJ Digit Med ; 1: 23, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-31304305

RESUMO

Patient-generated health data (PGHD), collected from mobile apps and devices, represents an opportunity for remote patient monitoring and timely interventions to prevent acute exacerbations of chronic illness-if data are seen and shared by care teams. This case report describes the technical aspects of integrating data from a popular smartphone platform to a commonly used EHR vendor and explores the challenges and potential of this approach for disease management. Consented subjects using the Asthma Health app (built on Apple's ResearchKit platform) were able to share data on inhaler usage and peak expiratory flow rate (PEFR) with a local pulmonologist who ordered this data on Epic's EHR. For users who had installed and activated Epic's patient portal (MyChart) on their iPhone and enabled sharing of health data between apps via HealthKit, the pulmonologist could review PGHD and, if necessary, make recommendations. Four patients agreed to share data with their pulmonologist, though only two patients submitted more than one data point across the 4.5-month trial period. One of these patients submitted 101 PEFR readings across 65 days; another submitted 24 PEFR and inhaler usage readings across 66 days. PEFR for both patients fell within predefined physiologic parameters, except once where a low threshold notification was sent to the pulmonologist, who responded with a telephone discussion and new e-prescription to address symptoms. This research describes the technical considerations and implementation challenges of using commonly available frameworks for sharing PGHD, for the purpose of remote monitoring to support timely care interventions.

7.
Emerg Med Pract ; (Suppl 2017A): 1-11, 2017 10 25.
Artigo em Inglês | MEDLINE | ID: mdl-29068629

RESUMO

With the proliferation of smartphones over the past several years, apps now play a prominent role in many social and work contexts, including medicine. This is enough of a phenomenon to have inspired the abbreviation "mHealth," short for mobile health. The number of app-driven clinical calculators, checklists, and risk scores in common use in the emergency department (ED) has significantly increased and shows no sign of slowing. Thanks to this digital development and innovation, clinical decision support is now just a finger-tap away. As of 2016, there were approximately 20,000 apps in the "Medical" category of Apple's app store. There are at least 300 apps specifically targeted to emergency clinicians, and given the variety of patient presentations, general-purpose apps and apps from other specialties likely merit usage in the ED. Despite the abundance of apps and their potential to improve patient care, the decision of which apps to choose and use is left largely to each clinician, with little guidance on best practices or potential risks. The purpose of this report is to educate emergency clinicians about medical apps that can be utilized to improve patient care during a shift.

8.
J Med Toxicol ; 13(4): 278-286, 2017 12.
Artigo em Inglês | MEDLINE | ID: mdl-28831738

RESUMO

BACKGROUND: The misuse of prescription opioids (MUPO) is a leading public health concern. Social media are playing an expanded role in public health research, but there are few methods for estimating established epidemiological metrics from social media. The purpose of this study was to demonstrate that the geographic variation of social media posts mentioning prescription opioid misuse strongly correlates with government estimates of MUPO in the last month. METHODS: We wrote software to acquire publicly available tweets from Twitter from 2012 to 2014 that contained at least one keyword related to prescription opioid use (n = 3,611,528). A medical toxicologist and emergency physician curated the list of keywords. We used the semantic distance (SemD) to automatically quantify the similarity of meaning between tweets and identify tweets that mentioned MUPO. We defined the SemD between two words as the shortest distance between the two corresponding word-centroids. Each word-centroid represented all recognized meanings of a word. We validated this automatic identification with manual curation. We used Twitter metadata to estimate the location of each tweet. We compared our estimated geographic distribution with the 2013-2015 National Surveys on Drug Usage and Health (NSDUH). RESULTS: Tweets that mentioned MUPO formed a distinct cluster far away from semantically unrelated tweets. The state-by-state correlation between Twitter and NSDUH was highly significant across all NSDUH survey years. The correlation was strongest between Twitter and NSDUH data from those aged 18-25 (r = 0.94, p < 0.01 for 2012; r = 0.94, p < 0.01 for 2013; r = 0.71, p = 0.02 for 2014). The correlation was driven by discussions of opioid use, even after controlling for geographic variation in Twitter usage. CONCLUSIONS: Mentions of MUPO on Twitter correlate strongly with state-by-state NSDUH estimates of MUPO. We have also demonstrated that a natural language processing can be used to analyze social media to provide insights for syndromic toxicosurveillance.


Assuntos
Transtornos Relacionados ao Uso de Opioides/epidemiologia , Uso Indevido de Medicamentos sob Prescrição/estatística & dados numéricos , Mídias Sociais/estatística & dados numéricos , Transtornos Relacionados ao Uso de Substâncias/epidemiologia , Mineração de Dados/métodos , Inquéritos Epidemiológicos , Humanos , Processamento de Linguagem Natural , Transtornos Relacionados ao Uso de Opioides/diagnóstico , Prevalência , Análise de Componente Principal , Semântica , Design de Software , Transtornos Relacionados ao Uso de Substâncias/diagnóstico , Fatores de Tempo , Estados Unidos/epidemiologia
9.
Nat Biotechnol ; 35(4): 354-362, 2017 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-28288104

RESUMO

The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma. Our platform enabled prospective collection of longitudinal, multidimensional data (e.g., surveys, devices, geolocation, and air quality) in a subset of users over the 6-month study period. Consistent trending and correlation of interrelated variables support the quality of data obtained via this method. We detected increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires. Potential challenges with this technology include selection bias, low retention rates, reporting bias, and data security. These issues require attention to realize the full potential of mobile platforms in research and patient care.


Assuntos
Asma/epidemiologia , Pesquisa sobre Serviços de Saúde/organização & administração , Inquéritos Epidemiológicos/estatística & dados numéricos , Vigilância da População/métodos , Projetos de Pesquisa , Telemedicina/estatística & dados numéricos , Adolescente , Adulto , Idoso , Asma/diagnóstico , Feminino , Inquéritos Epidemiológicos/métodos , Humanos , Masculino , Pessoa de Meia-Idade , New York/epidemiologia , Estudos Observacionais como Assunto/métodos , Seleção de Pacientes , Prevalência , Fatores de Risco , Adulto Jovem
10.
Pac Symp Biocomput ; 22: 300-311, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-27896984

RESUMO

In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices. The ability to identify patient groups or patterns of symptoms that might predict adverse outcomes such as asthma exacerbations or hospitalizations from these types of large, prospectively collected data sets, would be of significant general interest. However, conventional clustering methods cannot be applied to these types of longitudinally collected data, especially survey data actively collected from app users, given heterogeneous patterns of missing values due to: 1) varying survey response rates among different users, 2) varying survey response rates over time of each user, and 3) non-overlapping periods of enrollment among different users. To handle such complicated missing data structure, we proposed a probability imputation model to infer missing data. We also employed a consensus clustering strategy in tandem with the multiple imputation procedure. Through simulation studies under a range of scenarios reflecting real data conditions, we identified favorable performance of the proposed method over other strategies that impute the missing value through low-rank matrix completion. When applying the proposed new method to study asthma triggers and symptoms collected as part of the AMHS, we identified several patient groups with distinct phenotype patterns. Further validation of the methods described in this paper might be used to identify clinically important patterns in large data sets with complicated missing data structure, improving the ability to use such data sets to identify at-risk populations for potential intervention.


Assuntos
Aplicativos Móveis , Telemedicina , Asma/classificação , Asma/diagnóstico , Asma/terapia , Telefone Celular , Análise por Conglomerados , Biologia Computacional/métodos , Simulação por Computador , Coleta de Dados , Humanos , Inquéritos e Questionários , Fatores de Tempo
11.
Appl Clin Inform ; 7(1): 128-42, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27081412

RESUMO

BACKGROUND: Older adults are at risk for inadequate emergency department (ED) pain care. Unrelieved acute pain is associated with poor outcomes. Clinical decision support systems (CDSS) hold promise to improve patient care, but CDSS quality varies widely, particularly when usability evaluation is not employed. OBJECTIVE: To conduct an iterative usability and redesign process of a novel geriatric abdominal pain care CDSS. We hypothesized this process would result in the creation of more usable and favorable pain care interventions. METHODS: Thirteen emergency physicians familiar with the Electronic Health Record (EHR) in use at the study site were recruited. Over a 10-week period, 17 1-hour usability test sessions were conducted across 3 rounds of testing. Participants were given 3 patient scenarios and provided simulated clinical care using the EHR, while interacting with the CDSS interventions. Quantitative System Usability Scores (SUS), favorability scores and qualitative narrative feedback were collected for each session. Using a multi-step review process by an interdisciplinary team, positive and negative usability issues in effectiveness, efficiency, and satisfaction were considered, prioritized and incorporated in the iterative redesign process of the CDSS. Video analysis was used to determine the appropriateness of the CDS appearances during simulated clinical care. RESULTS: Over the 3 rounds of usability evaluations and subsequent redesign processes, mean SUS progressively improved from 74.8 to 81.2 to 88.9; mean favorability scores improved from 3.23 to 4.29 (1 worst, 5 best). Video analysis revealed that, in the course of the iterative redesign processes, rates of physicians' acknowledgment of CDS interventions increased, however most rates of desired actions by physicians (such as more frequent pain score updates) decreased. CONCLUSION: The iterative usability redesign process was instrumental in improving the usability of the CDSS; if implemented in practice, it could improve geriatric pain care. The usability evaluation process led to improved acknowledgement and favorability. Incorporating usability testing when designing CDSS interventions for studies may be effective to enhance clinician use.


Assuntos
Sistemas de Apoio a Decisões Clínicas/estatística & dados numéricos , Serviço Hospitalar de Emergência , Geriatria , Manejo da Dor , Idoso , Humanos , Médicos/estatística & dados numéricos
12.
Acad Emerg Med ; 23(5): 645-9, 2016 05.
Artigo em Inglês | MEDLINE | ID: mdl-26932394

RESUMO

OBJECTIVES: Emergency departments (EDs) commonly analyze cases of patients returning within 72 hours of initial ED discharge as potential opportunities for quality improvement. In this study, we tested the use of a health information exchange (HIE) to improve identification of 72-hour return visits compared to individual hospitals' site-specific data. METHODS: We collected deidentified patient data over a 5-year study period from Healthix, an HIE in the New York metropolitan area. We measured site-specific 72-hour ED returns and compared these data to those obtained from a regional 31-site HIE (Healthix) and to those from a smaller, antecedent 11-site HIE. Although only ED visits were counted as index visits, either ED or inpatient revisits within 72 hours of the index visit were considered as early returns. RESULTS: A total of 12,669,657 patient encounters were analyzed across the 31 HIE EDs, including 6,352,829 encounters from the antecedent 11-site HIE. Site-specific 72-hour return visit rates ranged from 1.1% to 15.2% (median = 5.8%) among the individual 31 sites. When the larger HIE was used to identify return visits to any site, individual EDs had a 72-hour return frequency of 1.8% to 15.5% (median = 6.8%). HIE increased the identification ability of 72-hour ED return analyses by a mean of 11.16% (95% confidence interval = 11.10% to 11.22%) compared with site-specific (no HIE) analyses. CONCLUSION: This analysis demonstrates incremental improvements in our ability to identify early ED returns using increasing levels of HIE data aggregation. Although intuitive, this has not been previously described using HIE. ED quality measurement and patient safety efforts may be aided by using HIE in 72-hour return analyses.


Assuntos
Continuidade da Assistência ao Paciente , Serviço Hospitalar de Emergência/estatística & dados numéricos , Troca de Informação em Saúde/estatística & dados numéricos , Sistemas de Informação em Saúde/estatística & dados numéricos , Sistemas de Informação Hospitalar/estatística & dados numéricos , Alta do Paciente/estatística & dados numéricos , Adulto , Feminino , Humanos , Masculino , Cidade de Nova Iorque , Segurança do Paciente , Melhoria de Qualidade
15.
J Clin Ethics ; 26(1): 68-72, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-25794296

RESUMO

When physicians search the web for personal information about their patients, others have argued that this undermines patients' trust, and the physician-patient relationship in general. We add that this practice also places other relationships at risk, and could jeopardize a physician's career. Yet there are also reports of web searches that have unambiguously helped in the care of patients, suggesting circumstances in which a routine search of the web could be beneficial. We advance the notion that, just as nonverbal cues and unsolicited information can be useful in clinical decision making, so too can online information from patients. As electronic records grow more voluminous and span more types of data, searching these resources will become a clinical skill, to be used judiciously and with care--just as evaluating the literature is, today. But to proscribe web searches of patients' information altogether is as nonsensical as disregarding findings from physical exams-instead, what's needed are guidelines for when to look and how to evaluate what's uncovered, online.


Assuntos
Competência Clínica , Internet , Relações Médico-Paciente/ética , Médicos/ética , Tomada de Decisões/ética , Registros Eletrônicos de Saúde , Humanos
16.
Am J Emerg Med ; 33(1): 104-7, 2015 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-25303847

RESUMO

For more than 25 years, emergency medicine researchers have examined 72-hour return visits as a marker for high-risk patient visits and as a surrogate measure for quality of care. Individual emergency departments frequently use 72-hour returns as a screening tool to identify deficits in care, although comprehensive departmental reviews of this nature may consume considerable resources. We discuss the lack of published data supporting the use of 72-hour return frequency as an overall performance measure and examine why this is not a valid use, describe a conceptual framework for reviewing 72-hour return cases as a screening tool, and call for future studies to test various models for conducting such quality assurance reviews of patients who return to the emergency department within 72 hours.


Assuntos
Serviço Hospitalar de Emergência/estatística & dados numéricos , Cuidado Periódico , Readmissão do Paciente/estatística & dados numéricos , Garantia da Qualidade dos Cuidados de Saúde , Registros Eletrônicos de Saúde , Humanos
17.
Med 2 0 ; 3(1): e1, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25075245

RESUMO

BACKGROUND: Twitter is a social network where users read, send, and share snippets of text ("tweets"). Tweets can be disseminated through multiple means; on desktop computers, laptops, and mobile devices, over ethernet, Wi-Fi or cellular networks. This redundancy positions Twitter as a useful tool for disseminating information to the public during emergencies or disasters. Previous research on dissemination of information using Twitter has mostly investigated the characteristics of tweets that are most effective in raising consumer awareness about a new product or event. In particular, they describe characteristics that increase the chance the messages will be shared ("retweeted") by users. In comparison, little has been published on how information from municipal or state government agencies spreads on Twitter during emergency situations. Retweeting these messages is a way to enhance public awareness of potentially important instructions from public officials in a disaster. OBJECTIVE: The aim of this study is to (1) describe the tweets of select New York State and New York City agencies by public officials surrounding two notable recent winter storms that required a large-scale emergency response, and (2) identify the characteristics of the tweets of public officials that were most disseminated (retweeted). METHODS: For one week surrounding Superstorm Sandy (October 2012) and the winter blizzard Nemo (February 2013), we collected (1) tweets from the official accounts for six New York governmental agencies, and (2) all tweets containing the hashtags #sandy (or #nemo) and #nyc. From these data we calculated how many times a tweet was retweeted, controlling for differences in baseline activity in each account. We observed how many hashtags and links each tweet contained. We also calculated the lexical diversity of each tweet, a measure of the range of vocabulary used. RESULTS: During the Sandy storm, 3242 shared (retweeted) messages from public officials were collected. The lexical diversity of official tweets was similar (2.25-2.49) and well below the average for non-official tweets mentioning #sandy and #nyc (3.82). Most official tweets were with substantial retweets including a link for further reading. Of the 448 tweets analyzed from six official city and state Twitter accounts from the Nemo blizzard, 271 were related to the storm, and 174 had actionable information for the public. Actionable storm messages were retweeted approximately 24x per message, compared to 31x per message for general storm information. CONCLUSIONS: During two weather emergencies, New York public officials were able to convey storm-related information that was shared widely beyond existing follower bases, potentially improving situational awareness and disaster response. Official Sandy tweets, characterized by a lower lexical diversity score than other city- and Sandy-related tweets, were likely easier to understand, and often linked to further information and resources. Actionable information in the Nemo blizzard, such as specific instructions and cancellation notices, was not shared as often as more general warnings and "fun facts," suggesting agencies mix important instructions with more general news and trivia, as a way of reaching the broadest audience during a disaster.

18.
PLoS One ; 9(2): e82452, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-24533044

RESUMO

Detailed data on the recreational use of drugs are difficult to obtain through traditional means, especially for substances like Dextromethorphan (DXM) which are available over-the-counter for medicinal purposes. In this study, we show that information provided by commenters on YouTube is useful for uncovering the toxicologic effects of DXM. Using methods of computational linguistics, we were able to recreate many of the clinically described signs and symptoms of DXM ingestion at various doses, using information extracted from YouTube comments. Our study shows how social networks can enhance our understanding of recreational drug effects.


Assuntos
Dextrometorfano/efeitos adversos , Antagonistas de Aminoácidos Excitatórios/efeitos adversos , Internet , Transtornos Relacionados ao Uso de Substâncias/diagnóstico , Análise por Conglomerados , Humanos , Drogas Ilícitas , Linguística , Reconhecimento Automatizado de Padrão , Mídias Sociais , Transtornos Relacionados ao Uso de Substâncias/epidemiologia , Avaliação de Sintomas
19.
EGEMS (Wash DC) ; 2(3): 1095, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25848622

RESUMO

PURPOSE: In a health care system where patients often have numerous providers and multiple chronic medical conditions, interoperability of health information technology (HIT) is of paramount importance. Regional health information organizations (RHIO) often provide a health information exchange (HIE) as a solution, which gives stakeholders access to clinical data that they otherwise would not otherwise have. A secondary use of preexisting HIE infrastructure is clinical event notification (CEN) services, which send automated notifications to stakeholders. This paper describes the development and implementation of a CEN service enabled by a RHIO in the New York metropolitan area to improve care coordination for patients enrolled in a geriatric emergency department care coordination program. INNOVATION: This operational CEN system incorporates several innovations that to our knowledge have not been implemented previously. They include the near real-time notifications and the delivery of notifications via multiple pathways: electronic health record (EHR) "in-baskets," email, text message to internet protocol-based "zone" phones, and automated encounter entry into the EHR. Based on these alerts the geriatric care coordination team contacts the facility where the patient is being seen and offers additional information or assistance with disposition planning with the goal of decreasing potentially avoidable admissions and duplicate testing. FINDINGS: During the nearly one-year study period, the CEN program enrolled 5722 patients and sent 497 unique notifications regarding 206 patients. Of these notifications, 219 (44%) were for emergency department (ED) visits; 121 (55%) of those notifications were received during normal business hours when the care coordination team was available to contact the ED where the patient was receiving care. Hospital admissions resulted from 45% of ED visits 17.8% of these admissions lasted 48 hours or less, suggesting some might potentially be avoidable. CONCLUSIONS AND DISCUSSION: This study demonstrates the potential of CEN systems to improve care coordination by notifying providers of the occurrence of specific events. Although it could not directly be demonstrated here, we believe that widespread use of CEN systems have potential to reduce potentially avoidable admissions and duplicate testing, likely leading to decreased costs.

20.
AMIA Annu Symp Proc ; 2014: 573-9, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25954362

RESUMO

Health information exchange (HIE) provides an essential enhancement to electronic health records (EHR), allowing information to follow patients across provider organizations. There is also an opportunity to improve public health surveillance, quality measurement, and research through secondary use of HIE data, but data quality presents potential barriers. Our objective was to validate the secondary use of HIE data for two emergency department (ED) quality measures: identification of frequent ED users and early (72-hour) ED returns. We compared concordance of various demographic and encounter data from an HIE for four hospitals to data provided by the hospitals from their EHRs over a two year period, and then compared measurement of our two quality measures using both HIE and EHR data. We found that, following data cleaning, there was no significant difference in the total counts for frequent ED users or early ED returns for any of the four hospitals (p<0.001).


Assuntos
Registros Eletrônicos de Saúde/organização & administração , Serviço Hospitalar de Emergência/normas , Troca de Informação em Saúde , Registro Médico Coordenado , Registros Eletrônicos de Saúde/normas , Troca de Informação em Saúde/normas , New York , Qualidade da Assistência à Saúde
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