MEDICAL CARE UTILIZATION IN TIME AMONG ELDERLY MEDICARE ENROLLEES IN AN HMO

Investigation into the medical care utilization of elderly Medicare enrollees in an HMO (Kaiser - Portland, Oregon): The specific research topics are: (1) The utilization of medical care by selected determinants such as: place of service, type of service, type of appointment, physician status, physician specialty and number of associated morbidities. (2) The attended prevalence of 3 chronic diseases: hypertension, diabetes and arthritis in addition to pneumonias as an example of acute diseases. The selection of these examples was based on their importance in morbidity/or mortality results among the elderly. The share of these diseases in outpatient and inpatient contacts was examined as an example of the relation between morbidity and medical care utilization. (3) The tendency of individual utilization patterns to persist in subsequent time periods. The concept of contagion or proneness was studied in a period of 2 years. Fitting the negative binomial and the Poisson distributions was applied to the utilization in the 2nd year conditional on that in the 1st year as regards outpatient and inpatient contacts.^ The present research is based on a longitudinal study of 20% random sample of elderly Medicare enrollees. The sample size is 1683 individuals during the period from August 1980-December 1982.^ The results of the research were: (1) The distribution of contacts by selected determinants did not reveal a consistent pattern between sexes and age groups. (2) The attended prevalence of hypertension and arthritis showed excess prevalence among females. For diabetes and pneumonias no female excess was noticed. Consistent increased prevalence with increasing age was not detected.^ There were important findings pertaining to the relatively big share of the combined 3 chronic diseases in utilization. They accounted for 20% of male outpatient contacts vs. 25% of female outpatients. For inpatient contacts, they consumed 20% in case of males vs. 24% in case of females. (3) Finding that the negative binomial distribution fit the utilization experience supported the research hypothesis concerning the concept of contagion in utilization. This important finding can be helpful in estimating liability functions needed for forecasting future utilization according to previous experience. Such information has its relevance to organization, administration and planning for medical care in general. (Abstract shortened with permission of author.) ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

The effect of triage team on time-in-department in a pediatric emergency department

Objective. To determine whether the use of a triage team would reduce the average time-in-department in a pediatric emergency department by 25%.^ Methods. A triage team consisting of a physician, a nurse, and a nurse's assistant initiated work-ups and saw patients who required minimal lab work-up and were likely to be discharged. Study days were randomized. Our inclusion criteria were all children seen in the emergency center between 6p and 2a Monday-Friday. Our exclusion criteria included resuscitations, inpatient-inpatient transfers, left without being seen, leaving against medical advice, any child seen outside of 6p-2am Monday-Friday and on the weekends. A Pearson-Chi square was used for comparison of the two groups for heterogeneity. For the time-in-department analysis, we performed a 2 sided t-test with a set alpha of 0.05 using Mann Whitney U looking for differences in time-in-department based on acuity level, disposition, and acuity level stratified by disposition. ^ Results. Among urgent and non-urgent patients, we found a statistically significant decrease in time-in-department in a pediatric emergency department. Urgent patients had a time-in-department that was 51 minutes shorter than patients seen on non-triage team days (p=0.007), which represents a 14% decrease in time-in-department. Non-urgent patients seen on triage team days had a time-in-department that was 24 minutes shorter than non-urgent patients seen on non-triage team days (p=0.009). From the disposition perspective, discharged patients seen on triage team days had a shorter time-in-department of 28 minutes as compared to those seen on non-triage team days (p=0.012). ^ Conclusion. Overall, there was a trend towards decreased time-in-department of 19 minutes (5.9% decrease) during triage team times. There was a statistically significant decrease in the time-in-department among urgent patients of 51 minutes (13.9% decrease) and among discharged patients of 28 minutes (8.4% decrease). Urgent care patients make up nearly a quarter of the emergency patient population and decreasing their time-in-department would likely make a significant impact on overall emergency flow.^

The economic cost of type 2 diabetes attributable to physical inactivity in the United States and Texas in 2007

Type 2 diabetes has grown to epidemic proportions in the U.S., and its prevalence has been steadily increasing in Texas. The physical activity levels in the population have remained low despite it being one of the primary preventive strategies for type 2 diabetes. The objectives of this study were to estimate the direct medical costs of type 2 diabetes attributable to not meeting physical activity Guidelines and to physical inactivity in the U.S. and Texas in 2007. This was a cross sectional study that used physical activity prevalence data from the 2007 Behavioral Risk Factor Surveillance System (BRFSS) to estimate the population attributable risk percentage (PAR%) for type 2 diabetes. These data were combined with the prevalence and cost data of type 2 diabetes to estimate the cost of type 2 diabetes attributable to not meeting Guidelines and to inactivity in the U.S. and Texas in 2007.^ The cost of type 2 diabetes in the U.S. in 2007, attributable to not meeting physical activity Guidelines was estimated to be $13.29 billion, and that attributable to physical inactivity (no leisure time physical activity) was estimated to be $3.32 billion. Depending on various assumptions, these estimates ranged from $7.61 billion to $41.48 billion for not meeting Guidelines, and $1.90 billion to $13.20 billion for physical inactivity in the U.S. in 2007. The cost of type 2 diabetes in Texas in 2007 attributable to not meeting physical activity Guidelines was estimated to be $1.15 billion, and that attributable to physical inactivity (no leisure time physical activity) was estimated to be $325 million. Depending on various assumptions, these estimates ranged from $800 million to $3.47 billion for not meeting Guidelines, and $186 million to $1.28 billion for physical inactivity in Texas in 2007. These results illustrate how much money could be saved annually just in terms of type 2 diabetes cost in the U.S. and Texas, if the entire adult population was active enough to meet physical activity Guidelines. Physical activity promotion, particularly at the environmental and policy level should be a priority in the population. ^

Idiographic Self-Monitoring Instruments to Empower Client Participation and Evaluate Outcome in Intensive Family Preservation Services

Intensive Family Preservation Services seek to reflect the values of focusing on client strengths and viewing clients as colleagues. To promote those values, Intensive Family Preservation Programs should include a systematic form of client self monitoring in their packages of outcome measures. This paper presents a model of idiographic self-monitoring used in time series, single system research design developed for Family Partners, a family preservation program of the School for Contemporary Education in Annandale, Virginia. The evaluation model provides a means of empowering client families to utilize their strengths and promote their status as colleague in determining their own goals, participating in the change process, and measuring their own progress.