This paper investigates the growing popularity of computerized economic modeling as a computer-assisted instructional aid and the associated educational benefits. It also provides an overview of current modeling, simulation, and econometrics programs and attempts to identify trends in computer-assisted instruction in economics.
A macro model incorporating rational expectations in financial markets (the Murphy Model–MM) is used to endogenize the macroeconomic environment for a comprehensive general equilibrium model (ORANI). The interface exploits the existence of variables which are endogenous to both models, calibrating on a shock to government spending. Prospective benefits include: (1) to the numerous policy oriented users of ORANI, a facility allowing the macroeconomic environment to be determined by a macrodynamic model such as MM; (2) to these users, reassurance that ORANI's short‐run translates in calendar time to about two years; (3) to the clientele of a macro model, the possibility of much more detailed projections.
Electronic bulletin boards (EBBS) have become a mainstay in economic research by providing access to archived data, manuscripts, articles, and computational software. Most EBBS are free and open to the public, and many of them are accessible over the Internet. The proliferation of government agencies' involvement, individualized offerings, and interconnected resources have significantly enhanced the benefits and operations of EBBS. These recent developments have inspired numerous economics instructors to introduce EBBS into their electronic classrooms. Although the economics profession does heavily utilize EBBS, it has not taken full advantage of the available technology. Keywords electronic bulletin boards, Internet, economic research, computer-assisted instruction.Communication and access to information are vital ingredients to economic research. Researchers span the globe in search of economic data, research manuscripts, journal articles, and computational software. They also reach out to their colleagues for help and to discuss new ideas and concepts. Until the mid-1980s, most of these activities were conducted in the library, through the mail, and by phone. Today, researchers and students are turning to their personal computers to accomplish in a half day what used to take weeks. They use their Pcs to connect to the many electronic bulletin boards (EBBS) around the world either by using modems and phone lines or by use of the Internet.l I Realizing the importance of EBBS and electronic connectivity has recently gained the attention of many. The Clinton administration initiated a campaign to establish a national information network, and 28 firms joined to develop the information superhighway in America (Wired, 1993). In addition, many economics instructors are strengthening their learning-by-discovery teaching methods by providing students access to the Internet.2 Students are now able to access economic forecasts and outlooks, econometrics software, databases, and government documents and to participate in various electronic discussion groups contained on EBBS.
To improve quality of care and patient outcomes, and to reduce costs, hospitals in the United States are trying to mitigate readmissions that are potentially avoidable. By identifying high-risk patients, hospitals may be able to proactively adapt treatment and discharge planning to reduce the likelihood of readmission. Our objective in this study was to derive and validate a predictive model of 30-day readmissions for a small rural psychiatric hospital in the northeast. However, this model can be adapted by other rural psychiatric hospitals-a context that has been understudied in the literature. Our sample consisted of 1912 adult inpatients (1281 in the derivation cohort and 631 in the validation cohort), who were admitted between August 1, 2014, and July 31, 2016. We used deidentified data from the hospital's electronic medical record, including physician orders and discharge summaries. These data were merged with community-level variables that reflected the availability of care in the patients' zip codes. We first considered the correlates of 30-day readmission in a regression framework. We found that the probability of readmission increased with the number of previous admissions (vs. no readmissions). Moreover, the probability of readmission was much higher for patients with a depressive disorder (vs. no depressive disorder), with another mood disorder (vs. no other mood disorder), and/or with a psychotic disorder (vs. no psychotic disorder). We used these associations to derive a predictive model, in which we used the regression coefficients to construct a score for each patient. We then estimated the predicted probability of 30-day readmission on the basis of that score. After validating the model, we discuss the implications for clinical practice and the limitations of our approach.
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