本論文對於醫療市場的生產績效研究，係由兩篇獨立的學術研究報告所組成，研究重點在於利用非參數資料包絡分析方法 ( nonparametric DEA approach )，估計醫院的生產技術，以衡量醫院的技術效率及不同年度間之生產力變動，進而分析不同醫院間，生產績效差異的主要原因。本論文所採用的研究方法與探討的主題，不同於國內既有的相關文獻。 第一部分：生產不確定性與醫院效率 本部分主要探討在醫院面對不確定性時的效率評估。一般而言，醫院有兩種生產上的不確定性來源：醫師或醫院的診療結果所導致的生產不確定性；及消費者對醫療服務需求的不確定性 (Arrow, 1963)。當醫院面對生產不確定性時，醫院效率將與廠商如何處理不確定性問題有關，亦即，當廠商事前規劃愈縝密，未來可能的產出失靈水準愈低，則其生產效率表現愈佳。本文利用民國 82 及 83 年（準）醫學中心與（準）區域醫院資料，模擬醫院在面對生產不確定性時，各種可能的產出失靈水準，以chance constrained DEA 模式 (Land, Lovell and Thore, 1993) 估算醫院的隨機技術效率，並與傳統、確定性的DEA模式所得到之結果，做一比較。 Chance constrained DEA模式與傳統DEA模式的不同，在於前者估計出的生產前緣，並不總是包絡所有的樣本點，亦即，允許某廠商之產出超越生產前緣或說允許產出失靈可能性之存在，而後者則否。實證結果發現，在chance constrained DEA模式下，私立醫院的技術效率高於公立醫院，且呈現統計顯著性的差異，但兩者間的差異隨著醫院事前準備程度的提高而縮小；而傳統DEA模式也顯示，私立醫院的技術效率確實顯著地高於公立醫院。此外，若產出失靈水準夠低，則chance constrained DEA模式的效率值與傳統DEA模式的效率值，兩者間的分配會呈現統計顯著性差異。 在面對生產不確定性時，欲提升公立醫院的生產效率，應提高廠商事先規劃的程度，才能與私立醫院之生產效率並駕齊驅。一般而言，廠商事先準備的程度高低，與醫院本身的特性有關，因此，欲改善公立醫院緩衝產能的準備程度，以降低產出失靈水準，有必要進行體制層面的改革，亦即，從進行人事變革、財務之授權與彈性化等方向開始做起，如此應可提高公立醫院的生產效率。 第二部分：全民健康保險制度與醫院生產力變動 全民健保實施後，民眾對醫療服務的可近性提高，醫院間的市場結構改變，因此，醫院生產力與效率的提升，成為眾所關切的焦點。為瞭解醫院在全民健保實施後，資源是否有效配置，本部分利用民國 82 至 86 年醫學中心、區域醫院與地區醫院等大小型醫院資料，以範疇DEA模式估計Malmquist生產力變動指標，並將之分解為技術變動、純技術效率變動、及規模效率變動等三項變動來源。 實證結果發現，從82至86年醫院整體平均效率而言，CRS（VRS）生產技術下的平均效率為 66.00%（74.87%），表示不論大小型醫院，平均而言，皆存在技術不效率的情形。再者，在民國84年，亦即全民健保實施的年度，其效率水準明顯較其他年度為低，其餘年度的效率水準都相對較高，此一結果意謂，政策干擾對於醫院效率表現的影響，是短期性的。另外，小型醫院皆較大型醫院不效率，兩者的效率差異呈現統計顯著性；以權屬別而言，不論是大型醫院或小型醫院中的私立醫院，其生產效率均優於公立醫院，且兩者的效率差異呈現統計顯著性。而透過迴歸分析顯示，全民健保實施、權屬別之虛擬變數、佔床率、平均住院日、及以醫院產出衡量的集中度指標等，是影響醫院生產效率的重要因素。 從Malmquist生產力變動（　et al., 1994）來看，平均而言，82-86年間醫院生產力成長率約在 -3.06 % 左右。就生產力變動來源而言，技術成長率（-2.74 %）與整體效率成長率（-0.33 %）均為負，而技術變動則是阻礙生產力成長的主要原因。此外，若以醫院整體效率變動來源來看，平均而言，整體效率退步是由於規模效率變動所致（-0.74%）。 此外，本文著重在　et al.(1994)、Ray and Desli (1997) 及Grifell and Lovell (1998) 三種定義下的Malmquist生產力變動指標之比較。研究結果發現，Grifell and Lovell (1998) 的一般化Malmquist生產力指數，並沒有正確衡量廠商的生產力變動及其變動來源項。而利用Kruskal-Wallis檢定結果發現，三個模式中的生產力變動差異，並不具統計顯著性，而變動來源項（技術變動與規模效率變動）亦顯示相同的結果。 / This dissertation is focused on the efficiency and productivity studies of hospitals in Taiwan. It includes two independent academic papers. The primary intention is to introduce the newly developed ideas in the measurement of efficiency and productivity, rather than to create new ones. The utilization of these ideas has not, however, been discussion in print. And some of the arguments we used and brought together are new regarding to the literature of hospital efficiency and productivity measurement. Utilizing the non-parametric data envelopment analysis (DEA) approaches, efficiency scores and productivity change indexes were estimated. Efforts were made to explain the difference of productivity performance among individual hospitals. Nevertheless, the methods we used and the economic approach behind them distinguish this study from other empirical studies of the medical market. Part I　 Market Uncertainty and Hospital Efficiency This part of the dissertation is focused on the measurement of efficiency of hospitals, incorporating uncertainty. There are stochastic variations in production relationships for hospitals. Generally speaking, the uncertainty of hospitals comes from two major sources: the natural uncertainty of medical cares; and the uncertainty of demands for medical cares (Arrow, 1963). Given the uncertainty in the medical market, the efficiency of hospitals hinges on how decision-makers deal with it. Undoubtedly, an optimal planning of the output buffers improves the efficiency performance. Using the hospital survey data in 1993 and 1994, and employing the chance constrained DEA model (Land, Lovell and Thore, 1993), the stochastic efficiency indexes of public and private medical centers and regional hospitals were estimated. Compared with deterministic frontier enveloping a given set of sample observations all the time, the chance-constrained frontier envelops them most of the time. That is, the chance constrained DEA allows the possibilities of output failure. Imposing different values of output failure probability, the estimation results were compared with the traditional (deterministic) DEA models. The empirical evidences of the chance constrained DEA model showed that, on average, private hospitals performed significantly better than public hospitals. This result matches with the result of the traditional DEA model. With Mann-Whitney U test, we compared the distributions of efficiency indexes under chance constrained DEA and deterministic DEA models. The test results showed that the difference between these two different models is statistically significant given a higher probability of output failure. These results imply that the nature of risk and the manipulation for risk are different for public and private hospitals. We also find that that the efficiency performance of public hospitals could be improved by the increasing of its reserve capacity. Part II　 National Health Insurance and Hospital Productivity Change In this part of the dissertation, we examine the impact of NHI on hospitals, and trace the sources of hospital productivity growth in Taiwan. To pursue our goal, we employ a data consisting of 157 medical centers, regional hospitals and district hospitals over the period 1993 to 1997, and resort to the Malmquist productivity index to measure total factor productivity change. The index could be decomposed into three components: technical change, pure technical efficiency change and scale efficiency change. The estimation technique used in the study is the deterministic non-parametric DEA approach. The results we find are revealing and suggestive to the public and the government in order to promote and assure the efficient delivery of quality health care. The average efficiency scores are 66.00% (74.87%) for CRS (VRS) technology and it means that there are substantial efficiency losses for the sample hospitals during the study period. The efficiency score of the hospitals as a whole in 1995 (the beginning year of NHI) was much lower than the other 4 years' efficiency scores. A censored Tobit regression analysis is used and identifies that NHI policy, ownership, rate of bed occupancy, average length of stay and the output-specific concentration level were all the significant determinants of technical efficiency. Empirical results indicate that most medical care regions became more output-specific concentrated. Total factor productivity on average deteriorated at an annual rate of -3.1%, and it was dominated by substantial technical regresses at an annual rate of -2.74%. The small hospitals were severely affected by NHI. Furthermore, within large and small hospital groups, the difference in technical change was statistically significant, but the differences in TFP and the associated components between ownership were not. Special attention was paid to compare　 et al.(1994), Ray and Desli (1997) and Grifell and Lovell (1998) approaches to decomposing the Malmquist productivity index. Empirical results indicate that the first 2 approaches yield accurate productivity changes, while GL doesn't. However, they produce almost the same magnitude of average TFP. In addition, no significant differences in the measured technical change and efficiency change were found among the three approaches.

本論文探討的主要問題是，由於知識的欠缺、傳統僵化的因果關係及全有全無的賠償效果，民事醫療訴訟（醫療錯誤類型）之原告往往無法獲得賠償因而產生之不公平。為解決此問題，本論文擬先由上位的因果關係概念著手探討，蓋彈性的因果關係概念或許有助於患者目前之困境，而探討之具體主題則為「決定論」與「非決定論」之因果關係概念。在探討過上位之因果關係概念後，本文主張於依決定論觀點無法釐清因果關係問題時，引入非決定論或機率之觀點輔助認定因果關係；而機率觀點要如何具體適用於個案中，又可區分為兩個觀察側面，即風險面和機會面，升高風險事實上等於喪失或降低了機會。因此本文接下來針對「風險升高」及「機會喪失」兩個主題作詳盡探討，並觀察其對於因果關係及歸責問題所帶來的啟發。在風險升高部分，本文選定流行病學之因果關係作為研究對象，原因在於根據流行病學手法判斷因果關係時，風險升高概念扮演了舉足輕重的角色，而且流行病學與實證醫學有密切的關聯，醫療訴訟中二造所使用之研究結果或統計數據多半係依據流行病學之方法作成，研究流行病學的方法對於理解訴訟二造提出之證據應有相當的助益。在探討完風險面之後，本文將選定於美國醫療訴訟中逐漸被普及適用之機會喪失理論，作為機會面之研究主題，並比較風險與機會之間之關係，進而討論機會本身是否具有價值，是否可作為請求賠償之依據。完成上述風險面及機會面之探討後，本文將檢視此二課題於我國學說及法院實務上之發展情形，在判決方面主要檢討「輔大學生健康檢查事件」中一至三審之判決，以及可作為重要參考依據之日本最高法院平成十二年之「生存可能性」相關判決。 在上述探討後，最後則回歸本文關心的主要問題，亦即，由於知識的欠缺、傳統僵化的因果關係及全有全無的賠償效果，醫療訴訟之原告往往無法獲得賠償，因而產生之不公平。由上述探討可知，因果關係概念不一定是僵化的，非決定論或機率之原因概念並不會與決定論之必要及充分原因概念相衝突；因此當根據決定論之原因概念，或傳統因果關係理論無法釐清因果關係爭議，且由於事件之專業性，使事實錯誤之不利益無論歸予原告或被告皆不公平時，唯一適當之方式即為引入機率之觀點比例認定賠償數額。在解釋方法上，則可參考上述輔大學生事件中一二審判決之見解，使用存活機會之概念（未來是否能進而擴及「維持健康之機會」及「治癒機會」，則有賴實務及學說發展），將存在於醫師過失行為與患者死亡或傷害之間，難以舉證之因果關係問題轉化為患者喪失機會之損害評價問題。當將之轉化為損害評價問題之後，法院即可根據民事訴訟法第二二二條第二項，自由心證比例認定患者之損害數額，並判予患者財產上及非財產上之損害賠償；而當法院自由心證時，二造所提出關於機會之統計數據或流行病學研究自然是重要的參考依據，但法院不須受數據絕對地拘束。