QM6640 TROY Medical Debt Critical Thinking Assessment Business Statistics Help

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qm6640

Troy University

Description

Read the case from the QM 6640 Medical Debt PDF document, and review the data from the QM 6640 Assessment Patient Data Excel file. Then, write a concise report answering questions 1-6 from the end of the case, using the data to assist in justifying your answers. Using past due amount as the dependent variable and all other variables as predictors (independent variables), run appropriate statistical analyses in Excel to mine the data. Insert tables and graphs from Excel in your report as appropriate. Label sections of your report to correspond to the questions. The completed report is the deliverable for this assignment.

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Graduate Critical Thinking Rubric Rubric ID: GradCT NOTE: This rubric is for program assessment, not grading. If you will be grading the assignment, use a separate rubric. This will keep your grade for each student as well as any comments you make to them private as we will only download results for program assessment rubrics specifically. Program assessment rubrics must be completed and submitted in Blackboard for each student. Assessment results will be aggregated by program, location, and delivery mode, so individual students or instructors are not identified. 3 2 1 Exceeds Expectations Meets Expectations Below Expectations Explanation of Issue/problem to be considered critically Issue/problem to be considered critically is Issue/problem to be considered issues is stated clearly and described stated but description leaves some terms critically is stated without clarification comprehensively, delivering all relevant undefined, ambiguities unexplored, or description. information necessary for full boundaries undetermined, and/or understanding. backgrounds unknown. Evidence Selecting and using information to investigate a point of view or conclusion Information is taken from source(s) with enough interpretation/ evaluation, to develop a comprehensive analysis or synthesis. Viewpoints of experts are questioned thoroughly. Information is taken from source(s) with some interpretation/ evaluation, but not enough to develop a coherent analysis or synthesis. Viewpoints of experts are taken as mostly fact, with little questioning. Information is taken from source(s) without any interpretation/ evaluation. Viewpoints of experts are taken as fact, without question. Influence of context and assumptions Thoroughly (systematically and methodically) analyzes own and others' assumptions and carefully evaluates the relevance of contexts when presenting a position. Questions some assumptions. Identifies several relevant contexts when presenting a position. May be more aware of others' assumptions than one's own (or vice versa). Shows an emerging awareness of present assumptions (sometimes labels assertions as assumptions). Begins to identify some contexts when presenting a position. Specific position (perspective, thesis/ hypothesis) acknowledges different sides of an issue. Specific position (perspective, thesis/ hypothesis) is stated, but is simplistic and obvious. Student's position Specific position (perspective, thesis/ (perspective, hypothesis) is imaginative, taking into thesis/hypothesis) account the complexities of an issue. Limits of position (perspective, thesis/ hypothesis) are acknowledged. Others' points of view are synthesized within position (perspective, thesis/ hypothesis). Conclusions and related outcomes (implications and consequences Conclusions and related outcomes (consequences and implications) are logical and reflect student’s informed evaluation and ability to place evidence and perspectives discussed in priority order. Directions for program assessment: Conclusion is logically tied to information (because information is chosen to fit the desired conclusion); some related outcomes (consequences and implications) are identified clearly. Conclusion is inconsistently tied to some of the information discussed; related outcomes (consequences and implications) are oversimplified. For student number ______ 1. Read the essay/paragraphs/answers. 2. Using the program assessment rubric in Blackboard, check the box with the description that best describes the student’s contribution in relation to expectations: exceeds=3; meets=2; below expectations=1. 3. A student’s total score will be the total of the values of the checked boxes. 4. A student earns a satisfactory score if the total value is 8 or higher. Exceeds Expectations Meets Expectations Below Expectations = 12-15 points = 8-11 points = 5-7 points Income Past Due Amount 9268 0 12099 0 96397 0 63027 327 273245 0 58756 0 139832 0 19930 0 37615 149 7686 3616 24125 197 28650 58 15340 0 20274 4506 20249 0 33959 171 177266 267 25020 266 163109 168 16650 1544 25664 155 14896 428 76008 0 174119 307 29516 324 41614 0 7016 1277 29586 0 21671 346 170421 0 114948 0 9267 137 144920 0 68926 0 124873 0 129358 0 186134 145 8926 0 8441 1174 20214 0 43988 410 172494 0 289543 0 51294 360 29621 960 192763 0 Insurance Medicaid Medicare Private Private Private Private Private Medicare Medicaid Private Private Medicaid Private Private Medicare Private Private Medicaid Private Medicaid Medicaid Medicaid Private Private Private Private Medicaid Medicaid Medicaid Private Medicare Private Private Private Private Medicare Medicare Private None Private + Medicare Private Private Private Private Medicaid Private Years Since Last Visit 0 1 0 1 0 0 3 0 2 1 0 2 1 2 1 3 0 2 0 0 1 2 0 0 0 1 0 2 0 1 0 0 3 0 1 0 0 0 2 0 0 0 0 1 1 0 Year Born 1955 1934 1973 1958 1963 1975 1992 1945 1969 1989 1947 1968 1956 1952 1938 1978 1989 1957 1979 1980 1981 1970 1970 1962 1981 1967 1989 1985 1959 1987 1940 1965 1985 1983 1989 1942 1941 1965 1991 1933 1971 1985 1956 1967 1987 1948 Gender Female Male Female Female Female Male Male Female Female Male Male Male Female Male Male Male Female Female Male Female Male Female Female Male Male Male Female Male Female Female Female Male Male Male Female Female Male Male Female Female Female Male Female Male Male Male 77959 8859 256103 48289 111048 19274 25629 12940 102797 9153 26884 58016 7519 25948 147116 17582 29101 25892 64146 69688 12739 221574 89151 21651 49988 58244 6004 9402 100088 115420 15321 218481 221217 215248 135772 81499 138647 12476 78631 23234 108879 19262 21747 200536 21973 9900 9970 173 368 208 475 0 63 0 0 500 0 0 0 429 484 59 69 364 0 0 0 1452 0 0 332 1802 360 0 278 0 0 0 291 0 0 0 0 0 4239 168 0 0 0 0 0 154 0 2091 Private Private Private Private Medicare None Private + Medicare Private + Medicare Medicare Private Medicare Private Private Medicare Private Private Medicare Medicaid Private Private + Medicare Medicaid Private Private Medicaid Private Private Private Medicaid None None Medicare Private Medicare Private Private Private Private Private Private Private + Medicare Private Private Private Medicare Medicaid Private Private 0 3 0 0 2 2 2 0 0 0 1 1 3 1 0 1 0 0 0 3 0 0 2 1 3 2 1 0 0 0 0 4 0 0 0 3 0 0 4 0 0 3 0 1 0 0 0 1986 1980 1984 1965 1939 1947 1943 1946 1934 1947 1942 1988 1976 1939 1979 1947 1935 1968 1979 1934 1983 1963 1963 1969 1969 1978 1955 1975 1955 1963 1939 1975 1938 1988 1992 1950 1964 1953 1988 1933 1982 1980 1986 1938 1977 1960 1982 Female Female Male Male Male Female Female Male Male Male Male Female Female Male Female Female Male Male Male Male Male Male Female Male Male Female Male Male Female Female Female Male Female Female Male Male Male Male Female Male 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Private + Medicare Private + Medicare Private + Medicare Medicare Private + Medicare Private Private Private None Medicaid Private Medicaid Private Private Private Private Medicaid Private Medicaid Private Medicare Private Medicaid Private Medicare Private Medicare Private Private Medicare 0 0 0 3 0 0 0 0 0 0 0 2 0 0 3 0 0 0 2 0 2 0 0 0 3 0 0 0 0 2 1 3 0 1 0 3 0 1 1 0 0 1 2 0 0 3 0 1990 1942 1944 1972 1964 1948 1954 1962 1953 1969 1976 1969 1969 1950 1986 1974 1950 1936 1940 1941 1938 1937 1951 1973 1958 1952 1983 1963 1961 1977 1972 1968 1967 1985 1987 1981 1953 1942 1977 1960 1966 1937 1985 1932 1961 1953 1946 Female Male Male Female Male Female Female Female Female Male Female Male Male Male Female Male Male Female Female Male Male Female Male Male Male Female Female Male Female Female Female Male Female Male Female Male Male Male Male Male Male Female Male Male Female Female Male 6919 106850 45194 7684 180617 26418 336113 29742 29029 110198 7967 118943 58683 7312 136053 28792 14768 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Medicare Private Medicare None Private Private Private Private Private Private Private + Medicare Private Medicaid Private Private Private Medicare Medicaid Private + Medicare Private Medicaid Private Private + Medicare Medicaid Private Private Medicaid Private Private Private Medicare Medicaid Private Private Medicare Private Medicare Private Medicare Medicare Private Private 0 1 1 4 4 2 1 0 0 0 0 2 0 2 0 0 0 0 2 1 1 0 3 0 2 1 0 2 2 0 0 0 0 0 2 1 0 0 3 1 0 0 2 0 0 3 0 1986 1949 1964 1985 1988 1933 1976 1933 1990 1956 1983 1964 1977 1989 1971 1940 1970 1967 1964 1949 1974 1933 1967 1933 1975 1986 1991 1940 1992 1974 1989 1981 1953 1976 1971 1934 1966 1978 1986 1943 1983 1939 1980 1939 1937 1966 1951 Male Male Male Female Male Male Female Female Male Male Male Male Female Male Female Female Male Female Female Male Female Male Female Female Female Male Female Male Male Female Male Female Female Male Male Male Male Male Female Female Female Male Female Male Male Male Male 8217 9912 96701 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1955 1947 1944 1957 1936 1937 1986 1959 1947 1976 1960 1942 1953 1990 1984 1966 1982 1941 1990 1960 1944 1985 1979 1973 1977 1953 1966 1953 1975 1967 1973 1952 1932 1933 1961 1961 1983 1984 1937 1991 1947 1934 Female Male Male Female Male Male Male Female Female Female Female Male Female Male Male Female Female Female Female Female Male Male Male Male Male Female Male Female Male Female Male Female Male Female Male Female Female Female Male Female Female Female Male Female Female Female Male 68674 8246 195868 87530 138470 15953 182832 12523 18866 110458 9065 168570 181751 175689 165994 8852 104521 104711 20807 25332 360674 156925 6512 53626 101138 10101 16627 19658 94243 48113 7415 223013 29582 11539 310509 255763 124088 252071 323478 25415 187157 9516 38187 76822 217310 60882 19685 0 2935 2212 0 0 196 0 92 486 0 0 0 0 0 0 3868 0 0 0 0 0 0 443 0 0 0 3866 0 0 0 0 0 3373 1157 123 0 0 0 0 0 0 0 0 0 0 0 0 Private Private Private Private Private Private + Medicare Private Private Private Private Medicare Medicare Private Private Medicare Medicare Private Private Medicare Medicare Private Private Private + Medicare Medicare Medicare Private Medicare Medicaid Private Private Medicaid Private Medicaid Private Private Private Private Private Private Medicare Private Medicare Medicaid Private Private Private Medicare 2 2 0 0 0 1 4 0 0 0 0 0 2 0 0 0 0 0 0 0 2 0 0 0 0 2 0 4 0 0 1 3 0 2 2 3 2 0 1 0 4 0 0 0 0 3 2 1959 1961 1983 1991 1990 1938 1981 1964 1961 1977 1933 1933 1950 1950 1934 1934 1967 1982 1942 1934 1979 1962 1938 1935 1938 1966 1936 1968 1964 1951 1949 1990 1981 1980 1970 1987 1986 1992 1970 1939 1976 1944 1949 1956 1957 1958 1943 Female Female Male Female Male Male Female Female Male Male Male Female Male Female Female Male Female Male Female Male Female Female Male Male Female Female Male Female Female Female Female Male Male Female Female Male Female Male Female Male Female Male Male Female Male Female Male 9723 102904 103645 80470 150324 23143 42829 29128 25800 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1973 1941 1954 1950 1991 1982 1965 1958 1960 1992 1989 1973 1979 1977 1943 1936 1950 1976 1982 1972 1989 1983 1977 1985 1959 Male Female Female Male Male Male Male Male Male Female Male Male Female Male Male Male Female Male Female Female Male Female Female Female Female Female Female Female Female Male Female Female Female Male Male Male Female Female Female Female Male Female Female Male Female Female Female 174171 11752 101920 64932 39844 27597 47557 31455 174821 19519 7477 30122 62421 8578 29187 27072 24180 85387 13305 280614 136403 184772 19308 73627 9368 96974 15177 178358 11340 102025 91549 105884 29031 42364 152546 8426 8456 59705 186937 52046 106445 167903 103624 14975 89410 23133 48556 0 0 0 0 236 0 268 443 1190 0 463 467 459 166 365 0 2280 0 4405 0 0 0 0 34 0 0 86 431 0 0 0 297 0 0 185 0 0 0 0 2731 0 0 0 0 0 492 0 Private Medicaid Private + Medicare Medicare Medicaid Medicare Private Medicaid Private Medicaid Medicaid Private Private Medicaid Medicaid Private Medicaid Private None Private Private Private Medicare Private Medicare Private Medicaid Private Private Medicare Private Private Private None Private Medicaid Private Private Private + Medicare Private + Medicare Private Private Medicare Private Private Private Private 0 0 2 0 2 0 1 2 0 0 0 2 2 1 3 0 0 0 2 0 2 0 0 2 0 3 2 0 4 0 0 0 0 0 3 0 2 3 0 3 0 0 1 3 2 2 2 1950 1962 1934 1939 1967 1933 1958 1952 1992 1965 1969 1985 1980 1954 1966 1975 1980 1947 1958 1982 1955 1988 1941 1980 1938 1982 1987 1965 1973 1944 1947 1967 1978 1947 1976 1948 1977 1951 1946 1937 1981 1964 1936 1969 1980 1965 1977 Female Male Male Male Female Male Male Female Male Male Male Female Male Female Male Male Female Male Male Male Male Female Male Male Male Female Male Male Female Female Male Male Female Female Male Male Male Female Female Male Female Male Female Female Female Male Male Hospitalizations 1 8 2 6 3 2 1 6 4 2 0 1 5 5 7 3 2 1 3 1 2 1 2 4 2 0 1 2 5 2 2 0 2 2 1 9 2 5 1 4 2 1 0 1 2 6 Ethnicity Caucasian Caucasian 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Caucasian African American Caucasian Asian Caucasian African American Caucasian African American Caucasian Caucasian Caucasian Caucasian Asian Caucasian Caucasian Asian Caucasian Caucasian Hispanic Caucasian Other Caucasian Caucasian Caucasian Asian African American Caucasian Caucasian Other Other Caucasian Hispanic Caucasian African American Married Married Married Single Divorced Divorced Married Married Divorced Married Separated Single Divorced Married Married Married Married Married Married Divorced Divorced Separated Divorced Separated Divorced Divorced Divorced Married Married Married Divorced Divorced Married Divorced Widowed Divorced Married Married Divorced Single Married Married Married Married Separated Divorced Divorced 1 3 5 2 13 4 1 2 8 1 1 1 1 2 3 3 2 2 4 1 5 1 2 1 1 0 6 1 1 3 1 9 6 9 10 0 3 3 1 1 5 7 0 3 1 1 6 African American Caucasian Hispanic Asian Other Hispanic Hispanic Hispanic Caucasian African American Caucasian Hispanic Hispanic Hispanic Caucasian Hispanic Caucasian Caucasian Asian Caucasian Hispanic Caucasian Caucasian Caucasian Caucasian Asian Caucasian Caucasian Caucasian Hispanic Caucasian Caucasian Asian Caucasian Caucasian Hispanic Caucasian Caucasian Caucasian Hispanic African American Caucasian Caucasian Caucasian Hispanic Other Caucasian Married Single Divorced Divorced Married Married Married Married Married Married Married Single Separated Separated Married Married Married Divorced Widowed Married Married Married Divorced Single Married Divorced Single Divorced Widowed Married Divorced Widowed Single Widowed Married Divorced Widowed Married Divorced Married Divorced Divorced Separated Divorced Married Married Widowed 0 0 5 6 4 8 6 3 1 3 0 5 3 0 1 3 0 2 2 4 7 1 5 1 4 1 1 4 6 4 2 1 4 2 11 2 3 3 4 7 2 0 3 1 7 3 1 Caucasian Hispanic African American Caucasian Caucasian Asian Caucasian Caucasian Caucasian African American Caucasian Other Caucasian Hispanic Caucasian Caucasian Caucasian Caucasian Caucasian Asian Caucasian Asian Hispanic Caucasian Hispanic Caucasian Caucasian African American Caucasian African American Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Asian Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Caucasian Married Married Married Widowed Separated Separated Married Married Widowed Divorced Divorced Married Married Married Divorced Married Married Married Married Separated Divorced Divorced Married Separated Married Separated Married Divorced Divorced Divorced Married Separated Married Divorced Married Separated Separated Married Widowed Married Single Married Divorced Divorced Married Separated Married 2 5 5 3 5 5 6 5 4 2 4 2 1 4 0 0 4 10 2 5 7 6 5 4 3 5 3 3 0 0 1 2 4 2 1 1 4 3 1 5 2 3 1 17 2 6 2 Caucasian Caucasian Caucasian Other Hispanic African American Caucasian Other Caucasian Caucasian Caucasian Caucasian Other Caucasian African American Hispanic Caucasian Caucasian Hispanic Asian Caucasian Caucasian Caucasian Caucasian Asian Caucasian Caucasian Caucasian African American Caucasian Caucasian Hispanic Caucasian African American Caucasian Caucasian African American Asian Asian Caucasian African American Caucasian Hispanic African American Hispanic African American African American Divorced Married Married Divorced Divorced Married Widowed Married Married Widowed Married Married Divorced Divorced Married Single Divorced Single Married Married Married Single Divorced Divorced Divorced Single Separated Widowed Single Divorced Separated Married Divorced Divorced Separated Married Married Divorced Divorced Widowed Married Widowed Divorced Married Married Married Married 2 2 1 5 1 1 1 4 0 2 11 8 7 6 0 5 2 11 1 1 2 2 6 2 2 4 5 1 2 6 2 6 2 0 4 2 2 5 4 5 4 2 8 3 1 7 1 Asian Caucasian Asian Caucasian Hispanic Caucasian Caucasian Caucasian Caucasian Caucasian Asian Hispanic Caucasian Asian Caucasian Hispanic Hispanic Asian Caucasian Other Hispanic Caucasian Caucasian Caucasian Caucasian African American Caucasian African American Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Caucasian Asian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Divorced Single Widowed Widowed Married Widowed Single Married Married Married Married Single Separated Married Married Divorced Divorced Married Single Divorced Divorced Married Married Widowed Married Single Separated Divorced Divorced Widowed Married Married Married Married Divorced Married Married Married Divorced Widowed Married Divorced Married Married Married Married Married 1 2 9 4 6 3 1 2 1 2 6 4 0 1 1 1 1 2 10 3 2 5 3 4 3 2 8 3 4 3 0 6 5 3 1 1 5 3 7 15 0 2 0 2 0 1 2 Caucasian Caucasian Asian Caucasian Other Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Caucasian African American Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Caucasian African American Caucasian Caucasian Caucasian Asian Hispanic Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Other African American Caucasian Asian African American Asian Caucasian African American Married Separated Married Separated Married Married Widowed Single Divorced Divorced Married Divorced Divorced Divorced Divorced Married Married Divorced Widowed Divorced Married Divorced Divorced Married Married Divorced Married Divorced Married Married Married Divorced Separated Married Single Divorced Married Single Single Separated Married Divorced Divorced Divorced Separated Divorced Married 3 1 7 4 1 1 1 1 3 1 1 10 5 3 4 3 3 8 0 3 1 2 2 5 2 2 2 3 2 1 3 2 2 0 4 2 2 5 3 0 3 5 9 7 5 0 5 Hispanic Asian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Caucasian Caucasian Caucasian Hispanic African American Hispanic Caucasian Caucasian Hispanic Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Hispanic Caucasian Caucasian Caucasian Hispanic African American Caucasian Caucasian African American Caucasian Asian African American African American Caucasian Caucasian Caucasian Caucasian Hispanic Widowed Divorced Married Married Married Married Divorced Divorced Separated Married Married Separated Divorced Divorced Married Single Married Divorced Divorced Divorced Divorced Married Single Married Widowed Married Separated Widowed Married Married Divorced Married Divorced Separated Married Married Divorced Married Separated Married Separated Widowed Married Divorced Married Married Divorced 8 3 5 6 0 1 0 3 1 9 2 5 3 1 3 5 8 2 3 7 2 0 1 1 2 3 3 0 5 0 0 5 6 2 4 3 0 6 0 5 1 1 9 2 0 7 1 African American Caucasian Caucasian Caucasian Caucasian Asian Hispanic Caucasian African American Caucasian Caucasian Caucasian Hispanic Caucasian African American African American African American Caucasian Hispanic Caucasian Hispanic Caucasian Caucasian Hispanic Hispanic Hispanic Caucasian Other Caucasian Caucasian Caucasian Other Caucasian Caucasian Caucasian Caucasian Asian African American Hispanic Caucasian Caucasian Caucasian African American Hispanic Hispanic Hispanic Caucasian Widowed Divorced Married Married Married Married Divorced Separated Divorced Married Single Married Divorced Widowed Married Separated Divorced Married Married Married Married Divorced Divorced Married Divorced Divorced Married Single Widowed Married Married Single Married Divorced Married Divorced Divorced Married Divorced Married Married Married Married Married Separated Divorced Married 0 1 5 1 2 2 7 1 3 5 8 8 2 0 1 1 4 6 2 2 4 2 5 3 3 1 1 1 4 7 1 2 3 2 3 1 5 0 1 3 2 1 1 0 3 2 1 Caucasian Caucasian Asian African American Hispanic Hispanic African American Other Caucasian African American Caucasian Caucasian Asian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic African American Caucasian Other Asian Caucasian Asian Hispanic Hispanic Hispanic African American Hispanic Caucasian Caucasian Caucasian Caucasian Hispanic Caucasian Caucasian Caucasian Caucasian Asian African American Caucasian Caucasian Married Married Married Divorced Separated Divorced Divorced Divorced Married Married Married Married Divorced Married Divorced Married Single Divorced Separated Divorced Married Married Married Married Married Married Divorced Married Single Divorced Married Married Divorced Divorced Married Married Married Married Divorced Widowed Married Single Divorced Widowed Married Married Married 0 1 2 2 7 2 4 3 3 0 3 3 2 1 1 5 0 10 1 1 4 1 2 3 1 1 1 1 2 1 4 2 12 2 5 7 4 1 3 0 11 4 7 2 0 6 5 Hispanic Caucasian Asian Caucasian Caucasian African American Asian Caucasian Caucasian Asian Caucasian Caucasian Other Caucasian Caucasian Other Other Caucasian Caucasian Caucasian Caucasian Asian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Caucasian Caucasian Caucasian Caucasian Hispanic African American Hispanic Caucasian Caucasian Caucasian Caucasian Asian Asian African American Caucasian Caucasian Hispanic Caucasian Married Divorced Divorced Married Married Married Married Divorced Married Married Married Married Widowed Divorced Single Married Divorced Married Married Married Divorced Divorced Divorced Married Married Married Divorced Divorced Widowed Divorced Widowed Married Married Divorced Married Married Married Married Separated Widowed Married Married Widowed Married Married Married Divorced 2 2 5 4 6 5 7 4 9 1 4 1 3 5 3 6 5 2 7 1 0 0 5 3 1 5 1 11 4 3 1 4 2 3 2 0 3 2 8 3 4 2 6 12 3 0 1 Caucasian Caucasian Hispanic Other African American Caucasian Caucasian Caucasian Asian African American Caucasian Caucasian Asian African American Caucasian Caucasian African American Caucasian Asian Caucasian Caucasian African American Caucasian Caucasian Caucasian African American Caucasian Caucasian Caucasian African American Caucasian Caucasian African American Caucasian Caucasian Other Hispanic Other Other Caucasian Caucasian Hispanic African American African American Caucasian Asian Hispanic Separated Married Widowed Single Divorced Married Separated Divorced Married Married Married Married Married Divorced Widowed Married Single Divorced Married Married Married Divorced Divorced Married Separated Married Divorced Married Married Divorced Divorced Married Married Divorced Divorced Single Separated Divorced Married Divorced Married Divorced Separated Divorced Married Divorced Widowed 1 0 13 2 2 4 5 2 2 4 1 1 0 3 1 1 1 4 1 3 5 0 6 0 0 9 6 0 2 2 0 1 1 2 0 0 0 3 4 3 1 1 2 1 2 4 2 Hispanic Caucasian Caucasian Caucasian Hispanic Caucasian Hispanic Asian Caucasian Other Caucasian Caucasian Other Caucasian African American African American African American Caucasian Hispanic Caucasian Caucasian Other Caucasian Caucasian Caucasian Caucasian African American Caucasian Hispanic Caucasian Asian Other Caucasian Caucasian Caucasian Caucasian Asian Hispanic Caucasian Caucasian Caucasian Caucasian Caucasian African American Caucasian Hispanic Asian 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Married Married Married Married Single Married Married Widowed Divorced Married Divorced Divorced Married Married Married Married Married Married Divorced Divorced Divorced Divorced Married Widowed Single Separated Divorced Widowed Divorced Divorced Married Married Married Separated Widowed Divorced Single Divorced Married Divorced Married Separated Separated 6 0 4 4 2 12 1 1 2 3 3 3 2 1 0 1 1 3 5 10 7 1 3 9 2 2 12 0 2 1 4 12 4 2 0 1 8 1 0 3 1 1 2 4 3 1 3 Caucasian Caucasian African American Caucasian Caucasian Caucasian Caucasian Hispanic Hispanic Caucasian Hispanic Asian Caucasian Caucasian Caucasian Hispanic Caucasian Caucasian Caucasian Asian Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Asian Hispanic African American Caucasian Caucasian African American Caucasian Caucasian African American Caucasian Caucasian Caucasian Caucasian Hispanic African American Married Married Divorced Married Single Divorced 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Married Widowed Divorced Married Married Widowed Married Divorced Separated Single Married Widowed Married Divorced Married Divorced Married Single Separated Married Married Married Married Married Married Married Married Divorced Divorced Widowed Divorced 3 4 1 2 0 10 1 3 5 1 2 13 0 6 9 7 5 1 7 5 2 2 9 11 6 1 7 4 4 3 5 1 3 1 1 2 2 1 2 9 2 3 2 3 3 5 2 Caucasian Other Caucasian Caucasian Hispanic Caucasian Asian Hispanic African American Asian Caucasian Hispanic Hispanic Other Caucasian Hispanic Caucasian Hispanic Caucasian Hispanic Caucasian Hispanic Caucasian Caucasian Caucasian Asian Caucasian Caucasian Other Caucasian Hispanic African American Caucasian Caucasian African American Caucasian Caucasian Caucasian Asian Asian Caucasian Caucasian Caucasian Caucasian Caucasian African American Caucasian Married Divorced Married Married Married Divorced Divorced Married Widowed Divorced Married Married Widowed Divorced Married Married Separated Divorced Married Divorced Divorced Married Married Married Married Married Married Widowed Married Widowed Widowed Married Widowed Married Married Married Married Married Divorced Married Married Married Divorced Married Divorced Married Separated 2 4 0 1 2 4 9 2 3 3 6 6 6 3 1 2 7 3 2 0 1 4 1 5 6 6 0 3 5 0 5 1 2 1 1 1 5 12 5 2 2 4 1 0 1 2 2 Caucasian Caucasian Caucasian African American Asian African American Hispanic Caucasian Caucasian Asian Caucasian Caucasian Caucasian Asian Caucasian Asian Asian African American Caucasian Caucasian Hispanic Caucasian Caucasian Hispanic Caucasian African American African American African American Hispanic Caucasian Hispanic Other Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Hispanic African American Caucasian Caucasian Other Caucasian Hispanic Caucasian Caucasian Married Married Married Married Widowed Married Divorced Single Divorced Married Married Married Widowed Married Married Married Divorced Married Divorced Married Divorced Married Single Married Married Married Divorced Single Single Married Married Married Widowed Married Single Separated Widowed Single Divorced Married Divorced Divorced Divorced Widowed Single Married Married 2 1 12 1 3 8 3 0 1 3 3 0 2 5 4 1 1 6 3 3 1 2 2 3 8 2 1 3 2 7 3 0 0 4 3 1 4 5 2 5 2 1 6 1 3 0 1 Hispanic Caucasian Hispanic Caucasian Caucasian Hispanic Caucasian Caucasian African American Caucasian Asian Asian African American Caucasian Caucasian Caucasian Caucasian Caucasian Hispanic Hispanic Caucasian Caucasian Caucasian Asian Caucasian Asian Hispanic African American Hispanic Caucasian Caucasian Hispanic Caucasian Asian Caucasian Caucasian Caucasian Caucasian Asian Caucasian Caucasian Hispanic Caucasian Caucasian Hispanic African American Caucasian Single Widowed Married Separated Widowed Married Single Widowed Single Divorced Separated Divorced Married Married Married Divorced Divorced Married Married Separated Married Separated Single Married Married Single Married Separated Divorced Widowed Single Married Married Married Divorced Single Married Divorced Divorced Married Married Married Divorced Divorced Married Married Married INSTRUCTIONS Read the case presented below from Businessweek on medical debt. Following the case are a series of questions, which must be answered using the provided dataset. The two rubrics provided will provide guidance on how the instructor will assess your submission. Type your responses to the questions into a single Word document titled QM6640_Assessment_LastFirst.docx. Insert tables and graphs in your report as appropriate. Upload the completed Word file to the Assessment Submission Area in Blackboard by the due date. Carmen Lewis, PhD QM 6640 ASSESSMENT Medical Debt QM 6640 Why Hospitals Want Patients to Pay Upfront By John Tozzi September 25, 2014 Tozzi is a reporter for Bloomberg Businessweek in New York. URL: http://www.businessweek.com/articles/2014-09-25/why-hospitals-want-patients-to-pay-upfront Melody Rempe spends much of her day telling people who are about to go into the hospital how much they’ll have to pay. As a patient financial counselor at Nebraska Methodist Health System, she calls patients about a week before they go in for procedures with estimates of their bills and what portion insurance will cover. Although many are grateful, some cry or yell. “Sometimes you’re talking to them about the biggest thing in their life,” she says. Rempe says most calls end well when she walks patients through the hospital’s payment-plan options or other financial assistance. Hospitals have good reason to be concerned about their patients’ finances: Even people with insurance are increasingly responsible for a big portion of their medical bills. Among Americans who get health coverage at work, 41 percent have deductibles of at least $1,000 they must meet before insurance starts paying. That’s up from 10 percent in 2006, according to the Kaiser Family Foundation. Those with employer coverage are joined by 7 million new enrollees in Obamacare plans, which typically make patients share a large chunk of costs. The average deductible in the most popular “silver” tier of coverage is $2,267, according to an analysis by the Robert Wood Johnson Foundation. Raising deductibles helps employers and insurers limit premium hikes. It also shifts more of the risk onto individuals. That in turn boosts the chances that doctors and hospitals won’t get paid. If a patient has a $2,900 deductible, “it’s far more difficult to get that $2,900 from an individual patient than it is from the Medicare program or from Blue Cross Blue Shield,” says Richard Gundling, vice president of the Healthcare Financial Management Association, a trade group. A March report on hospitals from Moody’s (MCO), the credit-rating firm, was blunt: “Today’s high deductibles are tomorrow’s bad debt.” Hospitals’ total cost of uncompensated care reached $46 billion in 2012, equal to about 6 percent of their expenses, the American Hospital Association says. Large for-profit chains such as LifePoint Hospitals (LPNT), which operates more than 60 medical centers in 20 states, have felt the impact of rising deductibles. LifePoint’s bad debt related to copays and deductibles is running at $25 million per quarter this year, up from $15 million per quarter in 2013, Leif Murphy, the company’s chief financial officer, said on an earnings call in July. He blamed the increase in part on the growing prevalence of highdeductible plans. As the mechanics of insurance policies become more complicated, Americans are having a harder time understanding how their plan choices will affect their finances. Only 14 percent of insured adults correctly understand insurance jargon such as deductibles, coinsurance, copays, and out-of-pocket maximums, according to a 2013 study published in the Journal of Health Economics. Many Americans aren’t prepared for a medical emergency. Dr. Marilyn Peitso, a pediatrician in St. Cloud, Minn., says parents often can’t afford $300 to $400 for antibiotics to treat an ear infection. “For young working families, this can get to be a real financial burden, and it can make them less likely to 1 seek needed care,” she says. About 44 percent of households have less than three months of savings, according to an analysis by the Corporation for Enterprise Development, an antipoverty group. “Tell me what 28-year-old is going to be able to provide, especially in this economy, $6,000 of their own money?” says Jan Grigsby, chief financial officer at Springhill Medical Center in Mobile, Ala. Like Nebraska Methodist, Springhill reaches out to patients before scheduled procedures with an estimate of what they’ll owe, Grigsby says. For those who can’t pay immediately, the hospital works with lenders to arrange no-interest payment plans of as long as two years. Staff members also check whether patients are eligible for charity care from the hospital or if they qualify for Medicaid. Many hospitals try to get patients to pay upfront 30 percent to 50 percent of what they’ll owe and some offer discounts for paying early, says Yaro Voloshin of health-care consultant MedAssets (MDAS). One reason is that they want to avoid the damage to their reputations that accompanies aggressive debt collection practices. “Over the years there’s been some stigma about collecting from patients,” says Zac Stillerman, an executive with the Advisory Board, which sells software and consulting services to hospitals. “It’s a bit of a third rail.” Managers at Nebraska Methodist noticed payment problems getting worse about seven years ago, when a large employer in the Omaha area introduced a plan with a $5,000 deductible. “We would bill a procedure for a patient; the entire amount would be applied to the deductible,” says Bob Wagner, the hospital’s director for revenue cycle. “We actually got no money.” Now any patient scheduling a procedure expected to cost more than $500 out of pocket gets a call from Rempe or another of Nebraska Methodist’s five financial counselors. The hospital tries to get some payment in advance, but it doesn’t turn away those who can’t pay. Even simply identifying the indigent can help, says Gundling of the Healthcare Financial Management Association. “For both the patient and the facility,” he says, “trying to collect a debt that can’t be paid just wastes everybody’s time.” The bottom line: Some of the 17 million U.S. residents covered by high-deductible health plans are racking up medical debt. 2 With this case, you are provided a sample of patient data for your analyses. The provided link in Blackboard to the Excel workbook contains data on patients in a particular hospital. The dataset contains the following variables: Income: Patient's annual household income Past Due Amount: Amount of debt or money owed by the patient to the hospital. Insurance: Type (if any) of medical insurance. Years since last visit: Number of years since the patient's last appointment. Year born: The year in which the patient was born Gender: Patient's gender Hospitalizations: Lifetime hospitalizations of the patient Ethnicity: Patient's Race Marital Status: Patient's Marital Status Hospitals are concerned about patients’ ability to pay their medical bills. It is suggested that there is a relationship between type of medical insurance and medical debt. Write a concise report answering questions 1-6. Using past due amount as the dependent variable and all other variables as predictors (independent variables), run appropriate statistical analyses in Excel to mine the data. Label sections of your report to correspond to the questions. Insert tables and graphs from Excel in your report as appropriate. 1. Summarize the issue(s) and/or problems from the case in your own words. Support your answer(s). 2. Identify strategies for solving the problem. 3. Propose one or more solutions/hypotheses which address the problem(s). 4. Evaluate potential solutions to the problem(s). Analyze own and others’ assumptions. What inferences can you make from your dataset? Some suggestions are listed below; however, conduct additional analyses as deemed appropriate. a. Create a frequency table of insurance types. b. Construct a bar chart, a pie chart, and a Pareto diagram of insurance types. c. Which graphical method do you think is best to portray these data? d. Based on this data, what conclusions can you make about the insurance status of the patients? 5. Choose a solution. 6. Evaluate outcomes by reviewing the results relative to the problem(s) defined. 3
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Running head: QM 6640 ASSESSMENT: MEDICAL DEBT

QM 6640 Assessment: Medical Debt
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QM 6640 ASSESSMENT: MEDICAL DEBT

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QM 6640 Assessment: Medical Debt
Healthcare facilities need financial information about patients as soon as they plan
medical care. The complexity surrounding medical insurance made it relatively hard for patients
to understand which premium options suit them. Similarly, employers and insurers raised
premium deductibles, which leaves patients with little or no medical reserve. For such reasons,
hospitals suggest to potential patients to at least commit to 30% or 50% of their ...


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