Spencer S. Jones

The incidence of fibromyalgia and its associated comorbidities: a population-based retrospective cohort study based on International Classification of Diseases, 9th Revision codes.

Background:The epidemiology of fibromyalgia is poorly defined. The incidence of fibromyalgia has not been determined using a large population base. Previous studies based on prevalence data demonstrated that females are 7 times more likely to have fibromyalgia than males and that the peak age for females is during the childbearing years. Objective:We have calculated the incidence rate of fibromyalgia in a large, stable population and determined the strength of association between fibromyalgia and 7 comorbid conditions. Methods:We conducted a retrospective cohort study of a large, stable health insurance claims database (62,000 nationwide enrollees per year). Claims from 1997 to 2002 were examined using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes to identify fibromyalgia cases (ICD code 729.1) and 7 predetermined comorbid conditions. Results:A total of 2595 incident cases of fibromyalgia were identified between 1997 and 2002. Age-adjusted incidence rates were 6.88 cases per 1000 person-years for males and 11.28 cases per 1000 person-years for females. Females were 1.64 times (95% confidence interval = 1.59–1.69) more likely than males to have fibromyalgia. Patients with fibromyalgia were 2.14 to 7.05 times more likely to have one or more of the following comorbid conditions: depression, anxiety, headache, irritable bowel syndrome, chronic fatigue syndrome, systemic lupus erythematosus, and rheumatoid arthritis. Conclusion:Females are more likely to be diagnosed with fibromyalgia than males, although to a substantially smaller degree than previously reported, and there are strong associations for comorbid conditions that are commonly thought to be associated with fibromyalgia.

Forecasting daily patient volumes in the emergency department.

BACKGROUND Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. OBJECTIVES The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. METHODS Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. RESULTS All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. CONCLUSIONS This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

Use of health-related, quality-of-life metrics to predict mortality and hospitalizations in community-dwelling seniors

OBJECTIVES: To investigate whether health‐related quality‐of‐life (HRQoL) scores in a primary care population can be used as a predictor of future hospital utilization and mortality.

A multivariate time series approach to modeling and forecasting demand in the emergency department

STUDY OBJECTIVE The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. METHODS Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. RESULTS Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. CONCLUSION Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

A framework for information system usage in collaborative care

UNLABELLED Clinical information systems (CIS) can affect the quality of patient care. In this paper, we focus on CIS use in the collaborative treatment of chronic diseases. We have developed a framework to determine which CIS functions have general usefulness for improving patient outcomes. METHODS We reviewed the use of clinical information systems within a collaborative care environment, identifying CIS functions important in chronic disease care. We grouped the functions into categories of access, best practices, and communication (ABC). Three independent raters selected the most important collaborative care related functions from the HL7 Electronic Health Record Systems functional model, and mapped the HL7 functions against the ABC categories. We then built a model of CIS use and tested it on data from a cohort of patients with chronic illnesses. RESULTS Of the 133 HL7 elements in the ABC model, 60 (45%) were ranked as important for collaborative care by two reviewers. Agreement was moderate for importance (kappa=.20) but high for ABC categorization (kappa=.67). In our data tests, for the 1105 patients, access 4.4+/-6.5, best practices 0.8+/-1.6, and communication 2.9+/-4.5. CIS functions were used per episode of care. We were able to identify several key functions that may affect patient care. For example, certain CIS functions related to best practices were associated with higher clinician adherence to testing guidelines. DISCUSSION This framework may be useful to assess and compare CIS systems for collaborative care. Future refinements of the model are discussed.

Forecasting Emergency Department Crowding: An External, Multicenter Evaluation

STUDY OBJECTIVE We apply a previously described tool to forecast emergency department (ED) crowding at multiple institutions and assess its generalizability for predicting the near-future waiting count, occupancy level, and boarding count. METHODS The ForecastED tool was validated with historical data from 5 institutions external to the development site. A sliding-window design separated the data for parameter estimation and forecast validation. Observations were sampled at consecutive 10-minute intervals during 12 months (n=52,560) at 4 sites and 10 months (n=44,064) at the fifth. Three outcome measures-the waiting count, occupancy level, and boarding count-were forecast 2, 4, 6, and 8 hours beyond each observation, and forecasts were compared with observed data at corresponding times. The reliability and calibration were measured following previously described methods. After linear calibration, the forecasting accuracy was measured with the median absolute error. RESULTS The tool was successfully used for 5 different sites. Its forecasts were more reliable, better calibrated, and more accurate at 2 hours than at 8 hours. The reliability and calibration of the tool were similar between the original development site and external sites; the boarding count was an exception, which was less reliable at 4 of 5 sites. Some variability in accuracy existed among institutions; when forecasting 4 hours into the future, the median absolute error of the waiting count ranged between 0.6 and 3.1 patients, the median absolute error of the occupancy level ranged between 9.0% and 14.5% of beds, and the median absolute error of the boarding count ranged between 0.9 and 2.8 patients. CONCLUSION The ForecastED tool generated potentially useful forecasts of input and throughput measures of ED crowding at 5 external sites, without modifying the underlying assumptions. Noting the limitation that this was not a real-time validation, ongoing research will focus on integrating the tool with ED information systems.

Physician perspectives of nurse care management located in primary care clinics

Care management has been suggested as a method to improve management of chronic disease, but its success can depend on the involvement of primary care physicians, especially with referral to care management. Our objective was to identify and characterize physicians’ perspectives of care management in order to gain insight into the rationale for referral to care management. The study took place in primary care clinics within an integrated delivery system. Nineteen primary care physicians with varying levels of involvement with care management participated in the study. We performed a qualitative and quantitative analysis of semistructured interviews. Four referral patterns emerged that were related to physicians’ recognition of care managers’ abilities and how care managers were connected to their practice. Results from this study can be used to more effectively implement similar models of chronic disease management, where physician participation is a critical component for successful implementation.