Market power can reduce the symptoms of adverse selection. To see the relationship, consider the incentive for a firm to offer a product that appeals to low-risk consumers and leads high-risk consumers to purchase insurance elsewhere. This incentive problem can be addressed through regulation but is also absent in a monopoly. This paper develops a model of welfare to explicitly characterize the substitutability between adverse selection regulation and market power. Market concentration has welfare benefits by reducing inefficient sorting of consumers among available plan options, a symptom of adverse selection. However, since market concentration also carries the welfare cost of higher markups, the magnitude and net direction of the effects are an empirical question. The model is estimated for the non-group market using novel choice data from a private online broker and a risk prediction model to relate preferences to marginal cost. The analysis focuses on two policies that target different dimensions of adverse selection: risk adjustment and the individual mandate. A simulation of a proposed merger of two insurance firms shows that, in the absence of a risk adjustment policy, the merger improves consumer welfare in markets that are not already highly concentrated. While the risk adjustment policy does not optimally price the sorting externality, it is successful in reducing the welfare cost of inefficient sorting and also eliminating the potential benefit to consumers from additional market power. The individual mandate is successful in increasing the insurance rate and lowering prices in the least concentrated markets, but leads to higher prices in the most concentrated markets. These results suggest that selection regulation is advantageous in competitive insurance markets, and less necessary and potentially harmful in very concentrated markets.
{"title":"Market Power in the Presence of Adverse Selection","authors":"Conor Ryan","doi":"10.2139/ssrn.3570241","DOIUrl":"https://doi.org/10.2139/ssrn.3570241","url":null,"abstract":"Market power can reduce the symptoms of adverse selection. To see the relationship, consider the incentive for a firm to offer a product that appeals to low-risk consumers and leads high-risk consumers to purchase insurance elsewhere. This incentive problem can be addressed through regulation but is also absent in a monopoly. This paper develops a model of welfare to explicitly characterize the substitutability between adverse selection regulation and market power. Market concentration has welfare benefits by reducing inefficient sorting of consumers among available plan options, a symptom of adverse selection. However, since market concentration also carries the welfare cost of higher markups, the magnitude and net direction of the effects are an empirical question. The model is estimated for the non-group market using novel choice data from a private online broker and a risk prediction model to relate preferences to marginal cost. The analysis focuses on two policies that target different dimensions of adverse selection: risk adjustment and the individual mandate. A simulation of a proposed merger of two insurance firms shows that, in the absence of a risk adjustment policy, the merger improves consumer welfare in markets that are not already highly concentrated. While the risk adjustment policy does not optimally price the sorting externality, it is successful in reducing the welfare cost of inefficient sorting and also eliminating the potential benefit to consumers from additional market power. The individual mandate is successful in increasing the insurance rate and lowering prices in the least concentrated markets, but leads to higher prices in the most concentrated markets. These results suggest that selection regulation is advantageous in competitive insurance markets, and less necessary and potentially harmful in very concentrated markets.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80755651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is taken as given by many policy makers that Direct-to-Consumer Advertising of prescription drugs drives inappropriate patients to treatment. Alternatively, advertising may provide useful information that causes appropriate patients to seek treatment. I study this dynamic in the context of antidepressants. Leveraging variation driven by the borders of television markets, I find that a 10% increase in antidepressant advertising leads to a 0.3% ($32 million) increase in new prescriptions followed by reductions in workplace absenteeism worth about $770 million. I find no effect of advertising on prices, generic penetration, drug switches, adverse effects, non-adherence rates or therapist visits.
{"title":"Promoting Wellness or Waste? Evidence from Antidepressant Advertising","authors":"Bradley T. Shapiro","doi":"10.2139/ssrn.3130327","DOIUrl":"https://doi.org/10.2139/ssrn.3130327","url":null,"abstract":"It is taken as given by many policy makers that Direct-to-Consumer Advertising of prescription drugs drives inappropriate patients to treatment. Alternatively, advertising may provide useful information that causes appropriate patients to seek treatment. I study this dynamic in the context of antidepressants. Leveraging variation driven by the borders of television markets, I find that a 10% increase in antidepressant advertising leads to a 0.3% ($32 million) increase in new prescriptions followed by reductions in workplace absenteeism worth about $770 million. I find no effect of advertising on prices, generic penetration, drug switches, adverse effects, non-adherence rates or therapist visits.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"291 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78111548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, I estimate the association between weak labor market conditions and the quantity of office-based physician services received by children enrolled in Medicaid. I find that children use more services in areas with higher unemployment during the Great Recession, and the result is not influenced by changes in sample composition. The association could reflect either demand factors such as worsening health or supply factors such as changes in the number of physicians willing to accept Medicaid patients. I provide several pieces of evidence supporting a supply-side mechanism: higher unemployment reduces the demand for physician services by privately-insured patients. Physicians respond to the demand shock by serving more Medicaid enrollees.
{"title":"Do Children on Medicaid Benefit from a Weak Labor Market? Evidence from the Great Recession","authors":"Jiajia Chen","doi":"10.2139/ssrn.3484320","DOIUrl":"https://doi.org/10.2139/ssrn.3484320","url":null,"abstract":"In this article, I estimate the association between weak labor market conditions and the quantity of office-based physician services received by children enrolled in Medicaid. I find that children use more services in areas with higher unemployment during the Great Recession, and the result is not influenced by changes in sample composition. The association could reflect either demand factors such as worsening health or supply factors such as changes in the number of physicians willing to accept Medicaid patients. I provide several pieces of evidence supporting a supply-side mechanism: higher unemployment reduces the demand for physician services by privately-insured patients. Physicians respond to the demand shock by serving more Medicaid enrollees.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"121 16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84099311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Holston, Laubach and Williams' (2017) estimates of the natural rate of interest are driven by the downward trending behaviour of `other factor' $z_{t}$. I show that their implementation of Stock and Watson's (1998) Median Unbiased Estimation (MUE) to determine the size of $lambda_{z}$ is unsound. It cannot recover the ratio of interest $lambda _{z}=a_{r}sigma _{z}/sigma _{tilde{y}}$ from MUE required for the estimation of the full structural model. This failure is due to their Stage 2 model being incorrectly specified. More importantly, the MUE procedure that they implement spuriously amplifies the estimate of $lambda _{z}$. Using a simulation experiment, I show that their MUE procedure generates excessively large estimates of $lambda _{z}$ when applied to data simulated from a model where the true $lambda _{z}$ is equal to zero. Correcting their Stage 2 MUE procedure leads to a substantially smaller estimate of $lambda _{z}$, and a more subdued downward trending influence of `other factor' $z_{t}$ on the natural rate. This correction is quantitatively important. With everything else remaining the same in the model, the natural rate of interest is estimated to be 1.5% at the end of 2019:Q2; that is, three times the 0.5% estimate obtained from Holston et al.'s (2017) original Stage 2 MUE implementation. I also discuss various other issues that arise in their model of the natural rate that make it unsuitable for policy analysis.
Holston, Laubach和Williams(2017)对自然利率的估计是由“其他因素”$z_{t}$的下降趋势行为驱动的。我表明，他们的实现股票和沃森(1998)的中位数无偏估计(MUE)，以确定$lambda_{z}$的大小是不健全的。它不能从估计全结构模型所需的MUE中恢复利息比$lambda _{z}=a_{r}sigma _{z}/sigma _{tilde{y}}$。这种失败是由于他们的阶段2模型被错误地指定。更重要的是，它们所执行的最大利用效率程序虚假地放大了$lambda _{z}$的估计值。通过模拟实验，我表明，当应用于从真实的$lambda _{z}$等于零的模型模拟的数据时，他们的MUE过程产生了过大的$lambda _{z}$估计值。修正他们的第2阶段最大利用效率程序导致对$lambda _{z}$的估计值大大减小，并且“其他因素”$z_{t}$对自然率的下降趋势影响更加减弱。这种修正在数量上很重要。在模型中其他因素保持不变的情况下，自然利率估计为1.5% at the end of 2019:Q2; that is, three times the 0.5% estimate obtained from Holston et al.'s (2017) original Stage 2 MUE implementation. I also discuss various other issues that arise in their model of the natural rate that make it unsuitable for policy analysis.
{"title":"Econometric Issues with Laubach and Williams’ Estimates of the Natural Rate of Interest","authors":"Daniel Bunčić","doi":"10.2139/ssrn.3541959","DOIUrl":"https://doi.org/10.2139/ssrn.3541959","url":null,"abstract":"Holston, Laubach and Williams' (2017) estimates of the natural rate of interest are driven by the downward trending behaviour of `other factor' $z_{t}$. I show that their implementation of Stock and Watson's (1998) Median Unbiased Estimation (MUE) to determine the size of $lambda_{z}$ is unsound. It cannot recover the ratio of interest $lambda _{z}=a_{r}sigma _{z}/sigma _{tilde{y}}$ from MUE required for the estimation of the full structural model. This failure is due to their Stage 2 model being incorrectly specified. More importantly, the MUE procedure that they implement spuriously amplifies the estimate of $lambda _{z}$. Using a simulation experiment, I show that their MUE procedure generates excessively large estimates of $lambda _{z}$ when applied to data simulated from a model where the true $lambda _{z}$ is equal to zero. Correcting their Stage 2 MUE procedure leads to a substantially smaller estimate of $lambda _{z}$, and a more subdued downward trending influence of `other factor' $z_{t}$ on the natural rate. This correction is quantitatively important. With everything else remaining the same in the model, the natural rate of interest is estimated to be 1.5% at the end of 2019:Q2; that is, three times the 0.5% estimate obtained from Holston et al.'s (2017) original Stage 2 MUE implementation. I also discuss various other issues that arise in their model of the natural rate that make it unsuitable for policy analysis.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86395505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We formalize an econometric model for two-sided matching mechanisms in a school choice context, where exogenous variation is generated by using lotteries as a tie-breaking mechanism. Our model accommodates a wide range of matching algorithms studied in the theoretical market design literature. We propose a Horvitz–Thompson estimator for the average treatment effect that is exactly unbiased, compatible with multiple treatments, and compatible with heterogeneous treatment effects. We present theoretical properties of the estimator and inference procedures. Our work clarifies the econometric model used in Abdulkadiroglu et al. (2017) and provides a robustness check on their results.
{"title":"Causal Inference in Matching Markets: Simulable Mechanisms","authors":"Jiafeng Chen","doi":"10.2139/ssrn.3510903","DOIUrl":"https://doi.org/10.2139/ssrn.3510903","url":null,"abstract":"We formalize an econometric model for two-sided matching mechanisms in a school choice context, where exogenous variation is generated by using lotteries as a tie-breaking mechanism. Our model accommodates a wide range of matching algorithms studied in the theoretical market design literature. We propose a Horvitz–Thompson estimator for the average treatment effect that is exactly unbiased, compatible with multiple treatments, and compatible with heterogeneous treatment effects. We present theoretical properties of the estimator and inference procedures. Our work clarifies the econometric model used in Abdulkadiroglu et al. (2017) and provides a robustness check on their results.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83764814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We estimate a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data. We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under a general approximate factor model. The key application is to estimate counterfactual outcomes in causal inference from panel data. The unobserved control group is modeled as missing values, which are inferred from the latent factor model. The inferential theory for the imputed values allows us to test for individual treatment effects at any time. We apply our method to portfolio investment strategies and find that around 14% of their average returns are significantly reduced by the academic publication of these strategies.
{"title":"Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference","authors":"Ruoxuan Xiong, Markus Pelger","doi":"10.2139/ssrn.3465357","DOIUrl":"https://doi.org/10.2139/ssrn.3465357","url":null,"abstract":"This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We estimate a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data. We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under a general approximate factor model. The key application is to estimate counterfactual outcomes in causal inference from panel data. The unobserved control group is modeled as missing values, which are inferred from the latent factor model. The inferential theory for the imputed values allows us to test for individual treatment effects at any time. We apply our method to portfolio investment strategies and find that around 14% of their average returns are significantly reduced by the academic publication of these strategies.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78272499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Roll, M. Grinstein‐Weiss, Emily Gallagher, Cynthia Cryder
This paper presents the results of an experiment testing the roles of a savings pre-commitment and different savings-focused choice architectures on the savings deposit decisions of 845,786 low- and moderate-income (LMI) tax filers. Results suggest that pre-committing to save at the start of the tax filing process can, among certain populations, dramatically increase savings rates. Among early tax filers, pre-commitment is associated with a 20.6 percentage point increase in savings deposits and a $418.86 increase in the amount deposited to savings. We observe more modest effects of pre-commitment on a general sample of tax filers. We also see strong evidence that choice architectures emphasizing savings strongly impact the deposit decisions of tax filers. The experiment also revealed cautionary evidence that the structure of pre-commitment can solidify decisions, making it then harder to later nudge those who opt-out of savings to change their minds. These findings may be broadly applicable to settings beyond the tax time moment – such as to applications that seek to encourage particular behaviors (like work or exercise) on the part of its participants.
{"title":"Can Pre-Commitment Increase Savings Deposits? Evidence from a Tax Time Field Experiment","authors":"S. Roll, M. Grinstein‐Weiss, Emily Gallagher, Cynthia Cryder","doi":"10.2139/ssrn.3464634","DOIUrl":"https://doi.org/10.2139/ssrn.3464634","url":null,"abstract":"This paper presents the results of an experiment testing the roles of a savings pre-commitment and different savings-focused choice architectures on the savings deposit decisions of 845,786 low- and moderate-income (LMI) tax filers. Results suggest that pre-committing to save at the start of the tax filing process can, among certain populations, dramatically increase savings rates. Among early tax filers, pre-commitment is associated with a 20.6 percentage point increase in savings deposits and a $418.86 increase in the amount deposited to savings. We observe more modest effects of pre-commitment on a general sample of tax filers. We also see strong evidence that choice architectures emphasizing savings strongly impact the deposit decisions of tax filers. The experiment also revealed cautionary evidence that the structure of pre-commitment can solidify decisions, making it then harder to later nudge those who opt-out of savings to change their minds. These findings may be broadly applicable to settings beyond the tax time moment – such as to applications that seek to encourage particular behaviors (like work or exercise) on the part of its participants.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74138797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Physicians’ relationships with the pharmaceutical industry have recently come under public scrutiny, particularly in the context of opioid drug prescribing. This study examines the effect of doctor-industry marketing interactions on subsequent prescribing patterns of opioids using linked Medicare Part D and Open Payments data for the years 2014-2017. Results indicate that both the number and the dollar- value of marketing visits increase physicians’ patented opioid claims. Furthermore, direct-to-physician marketing of safer abuse-deterrent formulations of opioids is the primary driver of positive and persistent spillovers on the prescribing of less safe generic opioids - a result that may be driven by insurance coverage policies. These findings suggest that pharmaceutical marketing efforts may have unintended public health implications.
{"title":"Pharmaceutical Opioid Marketing and Physician Prescribing Behavior","authors":"Svetlana Beilfuss","doi":"10.2139/ssrn.3379855","DOIUrl":"https://doi.org/10.2139/ssrn.3379855","url":null,"abstract":"Physicians’ relationships with the pharmaceutical industry have recently come under public scrutiny, particularly in the context of opioid drug prescribing. This study examines the effect of doctor-industry marketing interactions on subsequent prescribing patterns of opioids using linked Medicare Part D and Open Payments data for the years 2014-2017. Results indicate that both the number and the dollar- value of marketing visits increase physicians’ patented opioid claims. Furthermore, direct-to-physician marketing of safer abuse-deterrent formulations of opioids is the primary driver of positive and persistent spillovers on the prescribing of less safe generic opioids - a result that may be driven by insurance coverage policies. These findings suggest that pharmaceutical marketing efforts may have unintended public health implications.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88808968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Treatment practices vary widely across hospitals, often with little connection to patients’ medical needs. We assess impacts of these differences in delivery practices at childbirth. We find that infants quasi-randomly delivered at hospitals with higher C-section rates are born in better shape and are less likely to be readmitted, with suggestive evidence of improved survival. These benefits are driven by avoidance of prolonged labors that pose risks to infant health. In contrast, these infants are more likely to visit the emergency department for respiratory-related problems, consistent with a large observational literature linking C-section to chronic reductions in respiratory health. (JEL I11, I12, J13, J16)
{"title":"The Health Impacts of Hospital Delivery Practices","authors":"David Card, Alessandra Fenizia, D. Silver","doi":"10.3386/W25986","DOIUrl":"https://doi.org/10.3386/W25986","url":null,"abstract":"Treatment practices vary widely across hospitals, often with little connection to patients’ medical needs. We assess impacts of these differences in delivery practices at childbirth. We find that infants quasi-randomly delivered at hospitals with higher C-section rates are born in better shape and are less likely to be readmitted, with suggestive evidence of improved survival. These benefits are driven by avoidance of prolonged labors that pose risks to infant health. In contrast, these infants are more likely to visit the emergency department for respiratory-related problems, consistent with a large observational literature linking C-section to chronic reductions in respiratory health. (JEL I11, I12, J13, J16)","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72764664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predictive analytics looks forward trying to divine unknown future trials or actions based on data mining, statistics, modeling, deep learning and artificial intelligence, and machine learning. Business Intelligence, its forerunner in analytics, is a look backward. Predictive models are useful to business activities to well understand the customers, with the goal of forecasting buying patterns, potential risks, and its possible prospects. Healthcare industry organizes predictive analytics in different ways to improve operations and minimize risk. This article will explain the understanding of predictive analytics and predictive modeling, how the healthcare industry adopted predictive analytics and modeling and the importance of data mining in healthcare.
{"title":"Predictive Analytics and Predictive Modeling in Healthcare","authors":"Sourav Mukherjee","doi":"10.2139/ssrn.3403900","DOIUrl":"https://doi.org/10.2139/ssrn.3403900","url":null,"abstract":"Predictive analytics looks forward trying to divine unknown future trials or actions based on data mining, statistics, modeling, deep learning and artificial intelligence, and machine learning. Business Intelligence, its forerunner in analytics, is a look backward. Predictive models are useful to business activities to well understand the customers, with the goal of forecasting buying patterns, potential risks, and its possible prospects. Healthcare industry organizes predictive analytics in different ways to improve operations and minimize risk. This article will explain the understanding of predictive analytics and predictive modeling, how the healthcare industry adopted predictive analytics and modeling and the importance of data mining in healthcare.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85659695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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