개인정보 보호를 위한 의료영상 발급 표준 업무절차 개발연구 (Development of Standard Process for Private Information Protection of Medical Imaging Issuance)
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- 대한방사선기술학회지:방사선기술과학
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- 제32권3호
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- pp.335-341
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- 2009
목 적 : 기존 필름으로 발급되었던 의료영상은 IT기술의 발달로 디지털화 되어 CD로 발급되고 있다. 그러나 발급 시 신분확인을 하고 있는 의무기록과는 달리 필름을 사용하던 시절부터 의료영상은 별다른 신분확인을 하지 않는 의료기관이 많다. 이에 신청자의 개인의료정보 보호에 대한 인식 실태를 조사하고 여러 의료기관의 CD 또는 DVD 등의 매체를 통한 의료영상 복사 현황을 조사, 정보보안에 관련된 국, 내외 법률 및 권고안을 분석하여 국내 환경에 부합하는 의료영상 복사 발급과 절차를 마련하는 기준을 제시하고자 한다. 대상 및 방법 : 첫째, 2008년 5월 1일부터 7월 31일까지 수도권에 있는 33개 종합병원을 대상으로 의료영상복사 신청 시 구비서류, 발급절차 등을 전화를 통한 유선 조사를 시행하였다. 신청자에 따른 구비서류를 의료법 제 21조 2항에 의거 (1) 본인일 경우 신분증 확인, (2) 가족일 경우 신청자 신분증, 가족관계 서류(건강보험증, 가족관계증명서, 등본 등), (3) 제 3자 대리인일 경우 신분증, 위임장, 인감증명서로 기준을 마련하여 조사하였다. 둘째, 연구기간 동안 위의 기준에 따라 의료영상을 발급해 주고 있는 K 의료원에 복사를 신청하는 신청자들이 준비해온 구비서류 여부를 파악하였다. 셋째, 구비서류의 확인 및 미비 시 조치 등에 대한 발급절차의 기준을 정립하여 프로세스를 개발하였다. 결 과 : 수도권 33개 의료영상 발급현황을 조사한 결과 모든 조건을 충족한 병원은 16곳(49%), 신분증만 있으면 가능한 병원은 4곳(12%), 누구나 신청 가능한 병원 4곳(12%)이었으며 의료영상을 발급하는 부서가 아닌 진료과에서 신청하는 곳이 9곳(27%)으로 구비서류 조건여부는 알 수 없었다. 또한 신청자들이 복사 신청시 준비해온 구비서류가 조건에 충족한지 3개월간의 조사 결과 모두 준비한 경우(완비)는 629건(49%), 일부만 준비한 경우(일부 미비) 416건(33%), 모두 준비하지 않은 경우(미비) 226건(18%)이였다. 위의 연구결과를 근거로 의료영상 복사 신청 절차에 대한 프로세스를 정립하여 객관적인 응대를 할 수 있도록 하고, 환자와의 마찰을 줄이고 불편을 최소화 하면서 환자의 편의를 도모하고자 세분화된 발급절차 모형도를 작성하였다. 결 론 : 다른 전산 시스템과 달리 의료영상 시스템인 PACS가 의료기기로 분류되어 있는 것은 그만큼 의료정보의 중요성이 크다는 의미이다. 또한 의료영상의 학문적 성격으로 의학교육 및 연구에 많이 쓰이는데 이러한 이유로 쉽게 인용되고 남용 될 수 있다. 따라서 의료영상은 전문적인 교육을 받은 의료영상 관리자에 의해 적절한 발급 기준으로 발급, 관리되어야 할 것이며 이에 관한 개인정보보호와 의료영상에 대한 적극적인 홍보가 필요할 것이다.
경북대학교박물관에서 소장하고 있는 봉화 북지리 석조반가상은 국립중앙박물관 특별전 '고대불교조각대전 <불상, 간다라에서 서라벌까지>'의 전시를 위해 운송과정을 거쳐 보존처리를 수행하였다. 석조반가상에는 표면에 오염물, 입상분해, 균열 등이 발생한 상태였으며 특히, 하단부가 사선으로 파손되어 단독으로 세울 수 없었으므로 전시 시 직립 안정성을 확보하기 위한 받침대를 제작하였다. 또한 암석의 구성광물을 동정하고 오염물을 확인하기 위해 편광현미경 및 실체현미경 관찰, SEM-EDS분석을 실시하였다. 그 결과, 봉화 북지리 석조반가상은 흑운모 화강암으로 구성된 것을 확인하였다. 새로 제작한 받침대에 원형의 우레탄수지로 만든 봉을 여러 군데 박아 넣었고 이에 대한 마찰력 실험으로 받침대 안정성을 평가하였다. 우레탄수지 및 에폭시수지의 마찰력 비교실험에서는 에폭시수지보다 우레탄수지가 높은 마찰계수 결과를 나타냈다. 따라서 봉화 북지리 석조반가상은 우레탄봉을 사용한 받침대를 받침으로써 보다 안정적인 전시가 가능하였다.
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.