A
Abu-Hanna, A., 290, 294
Academic detailing: process, 486; profi ling and, 485–488
Accountability, 21, 444–445
Accreditation, 19; licensure, credentialing and, 65–71; licensure and, 68–70; scores with bed sores and infections, 70. See also Licensure
ACS. See American College of Surgeons Act, 23, 24. See also Plan-Do-Check Act
cycle
Active errors, 42, 169, 170
Acute clinical stability, 493, 499, 502 Acute myocardial infarction (AMI),
270; Medicare and, 362; patients and administration of aspirin, 287; patients and p chart of aspirin delivery, 288; patients and rebased control limits for aspirin delivery, 289; treatment, January 2011–August 2012, 315; treatment chart, 316; treatment check sheet, 314
Adaptation, 399, 400 Addiction, drugs of, 487
Adjustment. See Risk adjustment Administrative safeguards, 199, 203 Admissions: medical and surgical, 47;
pharmaceutical errors per, 1; U.S. and number of, 163, 437; variation and hospital, 47
ADR. See Adverse drug reactions Adult respiratory distress syndrome
(ARDS), 49, 429–430
Adverse drug reactions (ADR), 211, 253–254, 431
Adverse events: adding cases to data base for, 198–199; data set of, 191; frequency of, 192
African Americans, 451, 495, 496 Age, risk-adjust patients and, 495 Aggregate root cause analysis, 176 AHA Chartbook, 522 AIDS, 505 Allergies, 181, 183, 211, 492, 533; drug, 28, 203, 503; penicillin, 28; to vaccinations, 494 Alliotta, S. L., 442 Alternative hypothesis, 336, 344
AMA. See American Medical Association American Child Health Association, 43 American College of Surgeons (ACS),
19, 68
American Diabetes Association, 459 American Hospital Association, 19
American Medical Association (AMA), 16, 19, 489
American Nurses Association (ANA), 440 American Pain Society Patient Outcome
Questionnaire (APS-POQ), 344 AMI. See Acute myocardial infarction ANA. See American Nurses Association Analysis: chi-square, 335–344, 365; data,
188, 195–196, 207–210, 346; FMEA, 373–404; multiple-event, 189–199, 209–212; regression, 354–362; root cause, 161–212; single-event, 178–189, 205–209; statistical, 188; tools for process, 114–155
Analysis of means (ANOM), 333, 350; applications of, 354; control limits for Ritalin prescription rates, 353; defi nition, 334; number of patients prescribed Ritalin, 352; Ritalin
prescription rates, 351; upper and lower control limits for Ritalin prescription rates, 353
Analysis of variance (ANOVA), 333, 344; applications of, 350; data analysis functions and, 346; defi nition, 334; Excel ANOVA output, 347; Excel ANOVA pairwise output, control vs. staff only, 349; Excel ANOVA pairwise output, control vs. staff /patient, 348; Excel ANOVA pairwise output, staff only vs. staff /patient, 349; patient-reported pain, 345; single factor dialog box, 346 Anesthesia: administration application
and SPC, 293–294; deaths and mortality rates, 17
Angel of Death, 4. See also Harvey, Donald ANOM. See Analysis of means
ANOVA. See Analysis of variance Antibiotics, 55; morality rates, 432;
MRSA and, 382; overuse, 483; practice guidelines, 431–432 Antitrust, 490 APACHE II system, 504–505 Apkon, M., 393 Apollo 1, 547 Appendectomies, 44, 45, 46
Applications, cases and, 428; antibiotic practice guidelines, 431–432; ARDS, 429–430; chronic kidney disease, 430–431; diabetes mellitus guidelines, 432; multiple event analysis: patient falls in VA facilities, 209–212; single-event analysis: wrong patient, 205–209; SPC and anesthesia administration— application 4, 292–293; SPC and cardiac rehabilitation guidelines— application 1, 290; SPC and cardiac surgery—application 1, 257–258; SPC and door-to-needle time—application 3, 259–261; SPC and ER services— application 3, 292–293; SPC and hip replacement—application 2, 258–259; SPC and MRSA—application 1, 317–319; SPC and public health outbreak— application 2, 319–320; SPC and thyroid surgery complications—application 2, 291–292; SPC and volume of surgical procedures—application 3, 320–321 APS-POQ. See American Pain Society
Patient Outcome Questionnaire
Architecture, organizational, 540–541 ARDS. See Adult respiratory distress
syndrome
Aristotle, 409, 474, 506 Armola, R., 343
Army Corps of Engineers, 172 Arthritis, rheumatoid, 445–447 Aspirin, 287–289
Assessment, 343, 442, 459
Assignable variation, 227. See also Special cause variation
Association of American Medical Colleges, 16 Asthma, 56, 487 Astronauts, 547–548 Asymmetry, information, 85 Attitudes, 422, 500–501 Authority, 21
Authors, caveats and, 421 Automation, 399–400, 402
Automobile industry, 21–22, 161–162, 302
B
Babies. See Infants Background, 419 Bacon, Francis, 261 Baicker, K., 47
Balanced scorecards: for benchmarking, 523–531; causal chain implied in, 529; defi nition, 512; health care value chain and, 527; Kaplan and Norton’s, 524; reenvisioning health care’s, 530 Balas, E. A., 482
Bariatric surgery, mortality rates, 18 Bates, David, 1, 177
Becher, E. C., 174, 205, 207 Becker, David, 362
Beckford, J., 516
Beds: cost of hospital, 173; sores and accreditation scores, 70
Benchmarking, 53, 410, 511, 543; balanced scorecards and, 523–531; best practice and, 512–523; dashboards and, 531–536; data sources, 522–523; as essential ingredient for quality improvement, 20; implementation strategies and, 537–542; obstacles, 519; performance improvement through, 523; process,
514–522; steps, 515; structure-process-outcomes, 517; types of, 513
Benefi ts: marginal, 75–77, 81, 83; SPC, 323–324 Benson, L. M., 444 Bernet, V., 448 Bernstein, M., 397 Berwick, D. M., 465 Bias, 188, 363, 454 Billing, 527–528 Binomial data, 266–282, 302 Births: cesarean section rates, 467;
expectation of life at, 561–562; high-risk pregnancies and, 447–448; maternity wards and mortality rates with, 13, 15; midwife clinics, 12–13; perinatal care and, 467–468; women and, 12
Black box, 29, 379 Blood pressure, 255, 256 Blue Cross, 90, 98
Board certifi cation, mortality data and, 340
Bondarenko, Valentin, 547–548 Bonetti, P. O., 259, 260
Boston, MA, 47–48 Boyer, C. G., 445, 446 BP. See British Petroleum Brickley, J., 540
British Medical Journal, 462 British Petroleum (BP), 548 Budgets: CBO review, 461–462;
constraints, 75
“Bulletin Number Four” (Flexner), 16–17 Bunin, J. L., 448
Bureaucrats in Business (World Bank), 538
C
c chart: for medical record errors, 307; u and, 302–312
Cabana, M. D., 422
CABG. See Coronary artery bypass graft California, physician complaints in, 66 Camp, R. C., 512, 519, 521–522 Canada, 43, 85; health care costs per
person, 64; health care system, 541 Cancer, 450, 478, 498
Capability, 327 Capitation, 90–92
Capture theory of regulation, 65 Cardarelli, R., 67
Cardiac rehabilitation guidelines application, SPC and, 290
Cardiac surgery: mortality rates, 270; SPC and, 257–258
Care: clinical pathways in perinatal, 467–468; managed, 94, 95; optimal, 9; return on investments on delivering high-quality, 96–97; role of self-, 457–458; SCIP, 104
Care Continuum Alliance (CCA), 449–450, 454
Carnett, W. G., 422 Carroll, J. S., 205
Case management (CM), 53, 54, 437, 440; case manager’s role in, 441; cases, 445–449; CM functions and intermediate and fi nal outcomes with, 444; disease, outcomes and, 439; distribution of U.S. medical spending and, 438; functions, 442–444; with measurement and accountability, 444–445
Case management cases: case manager’s duties with, 449; high-risk pregnancy and, 447–448; hypercalcemia and, 448; process for high-risk pregnancy, 447; rheumatoid arthritis and, 445–447 Case mix index (CMI), 104
Cases: academic detailing, 486–488; disease management, 459–463; profi ling, 482–484. See also Applications, cases and Castronova, F., 482 Categorical data, 221 Categorical variable, 335 Caucasians, 451, 495, 496
Causal chain, balanced scorecard and, 529 Cause and eff ect diagrams, 137–138; cause
and eff ect template, 139; defi nition, 25; fi ve majors causes and frequent sub-causes, 139–140; with major causes of problems identifi ed, 254; for wrong medication, 141
Cause and eff ect example: diagram for wrong medication, 141; medication error, 140–141
Causes: data analysis and identifying, 188; of errors, 170–173; pathophysiology and, 459
Caveats, 85, 421
CBC. See Complete blood count CBO. See Congressional Budget Offi ce
review
CCA. See Care Continuum Alliance Center line, 238–239, 242–244, 246–247,
250
Centers for Medicare and Medicaid Services. See CMS
Certifi cate of need (CON) laws, 67 Certifi cation, mortality data and board,
340
Cesarean sections, 467 Chaff ee, Roger, 547 Chandra, A., 47
Charge reimbursement, 90 Charlson index, 504–505
Charts: AMI treatment, 316; c and u, 302– 312; control, 321–322; Curran et al. SPC, 318; defi nition, 235; dietary concerns and u, 310; Duclos et al. SPC, 291–292; Fasting and Gisvold SPC, 294; fl ow, 137, 145–148, 179; medical record errors c, 307; medical record errors u, 308; p and np, 266–282; pareto, 116–122; Pollard and Garnerin SPC, 320–321; run, 152– 155, 534; stratifi cation, 122–127, 129; Walberg, Frøslie, and Røislien SPC, 319; X1 and R, 231–254. See also Flowcharts Chassin, Mark, 55, 56, 174, 205, 207 Check, 23, 24. See also Plan-Do-Check Act
cycle
Check sheets: AMI treatment, 314; defect factor, 135–136; defect location, 136– 137; defective item, 135; defi nition, 132; process distribution, 133–134
Chen, J., 69
Children: asthma in, 487; life expectancy by race, 495; Ritalin prescribed to, 351; tonsillectomies and, 43
Chi-square analysis, 335, 336, 365; applications of, 343–344; board certifi cation and mortality data, 340; drag and drop, 342; expected infections, January–April 2012, 337; expected
surgical mortality, 342; observed infections, January–April 2012, 337; observed infections, January–August 2012, 339; observed surgical mortality, 342; PivotTable, 341; selecting function of, 338
Cholecystectomy, 44, 45, 71
Cholesterol: data set, 358–359; levels, 361 Chong, C., 418
Circle. See Quality circle Clancy, C. M., 364 Cleary, P., 377 Cleveland Clinic, 536
Clinical College of Surgeons of North America, 19
Clinical decision-making model, 413 Clinical judgment, of physicians, 38–39 Clinical outcomes, 502
Clinical pathways, 416
Clinical patient inputs: cancer staging, 498; leading causes of death by race, 2007, 496; risk factors and, 495–499 Clinical practice guidelines, 53, 54 Clinical presentation, 459
Clinics: midwife, 12–13; mortality rates (1841–1846), 12
Clustering, 483
CM. See Case management CMI. See Case mix index
CMS (Centers for Medicare and Medicaid Services), 98; Hospital Compare, 516– 517, 522; quality initiative, 102–105 Codman, Ernest (1869–1940), 20, 22,
31, 378, 493; data collection and, 552; as health care pioneer, 17–19; patient outcomes, 474, 521; quality management tools and, 553
Coeffi cients, 361; of determination, 356; slope, 357 Cognitive errors, 171 Cognitive functioning, 500 Cohen, E. L., 441 Cohen, H., 402 Coin tossing, 227
Commission, errors of, 165
Common cause variation, 226. See also Natural variation
Comorbidities, 39, 185, 498
Comparisons: data, 20; interventions with other, 421; outcomes and peer, 18 Competitive benchmarking, 513 Complacency, 24, 208, 511 Complete blood count (CBC), 377
Complications: iatrogenic illness and, 502; thyroid surgery, 291–292
Composition, fallacy of, 450
Computer decision support, 427–428 Computerized physician order entry
(CPOE), 200, 203
CON. See Certifi cate of need
Conditions: CMS hospital quality initiative demonstration clinical, 104; cost reduction in chronic, 461; fi ve most costly medical, 450; reform, 563–564 Confl ict of interest, 490
Conformance, 267, 295–296, 458 Confounders, 363, 364
Confusion Assessment Method, 343 Congressional Budget Offi ce (CBO) review,
461–462 Conover, C. J., 71
Consensus, 45, 145, 188, 254, 388 Constant control limits, 286
Consumer: expectations, 228; surplus, 81, 83
Contingency table. See PivotTable Continuous data, 220–221. See also
Statistical process control, for continuous data: X1 and R charts Continuous quality improvement (CQI),
21, 26, 27, 52, 254, 550 Continuous variable, 344 Contracts, exclusive, 490
Control: charts, 321–322; freedom through, 555; groups, 186; postexecution, 537, 540
Control limits: aspirin administration among AMI patients and, 287; aspirin delivery to AMI patients and rebased, 289; average sample size and constant, 285; changes monitored over time and rebasing, 287–290; constant, 286; constant and variable, 284–286; ER wait times and calculating, 242–243; establishing valid, 282–284; heart failure
and breach of lower, 281; medication management and calculating, 246–247; p chart for aspirin delivery to AMI patients and, 288; period-specifi c sample size and variable, 285; Ritalin prescription rates with ANOM, 353; Ritalin prescription rates with upper and lower, 353; SPC for binomial data and calculating, 269–271; SPC for continuous data, X1 and R charts, and calculating, 235–237; surgical coding and calculating, 276–277; upper and lower, 224 Convenience sample, 363 Cook, D. J., 363 Cookbook medicine, 10, 412 Cookson, J., 442 Coombs, J. B., 463
Copayments: with eff ect of third-party payment on demand, 84; health insurance with 20%, 83
Coronary artery bypass graft (CABG), 104, 417; defi nition, 377; mortality rates in New York, 474, 475
Coronary artery disease, 463 Coronary Drug Project, 377
Corrective action: instances of required, 219–220; multiple-event analysis on patient falls in VA facilities, 210–212; with single-event analysis, 188–189; with single-event analysis on wrong patient, 209
Correlation, 41, 45, 141–142, 145, 334, 361 Cost reimbursement, 88, 90–91
Costs: CBO review of health outcomes and, 461–462; fi ve most costly medical conditions, 450; of hospital beds in 2003, 173; with medical spending in U.S., 438; opportunity, 64; outpatient, 461; of quality improvement, 101; reduction in chronic conditions, 461; of treatment, 99–101
Cottage industry, 20, 21, 22 Count data, 221
Cowley, M., 467
CPOE. See Computerized physician order entry
Credentialing: defi nition, 65; economic, 488–490; licensure and, 70–71. See also Licensure
Credibility, 538
Cristianson, J., 484, 485 Critical value, 338
CRM. See Customer relationship management
Cross tabulation. See PivotTable C-section. See Cesarean sections Cullen, Charles, 4
Curran, T. A., 399, 402 Curry, W. J., 486 Customer intimacy, 524 Customer perspective, 524, 530 Customer relationship management
(CRM), 526
Customization, mass, 20, 21, 22
D
Damberg, C., 362
Dartmouth Atlas of Health Care, 522 Dashboards: benchmarking, 531–536;
defi nition, 512; operating room, 535; run chart, 534
Data, 113, 114, 198–199; binomial, 266–282, 302; board certifi cation and mortality, 340; cholesterol data set, 358–359; comparisons, 20; continuous, 220–221; gaming of the, 280; outcome, 378; set of adverse events, 191; sources and benchmarking, 522–523; types, 220–221
Data analysis: cause identifi cation and, 188; functions with variance analysis, 346; multiple-event analysis and, 195–196; multiple-event analysis on patient falls in VA facilities, 209–210; with single-event analysis on wrong patient, 207–208 Data collection, 552; data set of adverse
events, 191; as essential ingredient for quality improvement, 20; frequency of adverse events, 192; medical decision-making process and, 39; with multiple-event analysis, 191–195; multiple-multiple-event analysis on patient falls in VA facilities, 209; with single-event analysis on
wrong patient, 206–207; time between registration and patient fall, 193, 194 Data collection techniques: fi ve whys,
180–181; Is-Is Not matrix, 184–187; Why-Why diagram, 181–183 Davis, S., 6
De Bruin, S. R., 461 Deadweight loss, 83
“Dealing with Medical Practice Variations: A Proposal for Action” (Wennberg), 46 Deaths, 10; anesthesia, 17; astronauts, 547–
548; motor vehicle, 161; race and leading causes of, 496. See also Mortality; Mortality rates
Decisions: clinical decision-making models, 413; computer decision support, 427–428; health care markets and producing, 79–80; health care markets and purchasing, 75–77; need-based decision making, 63, 64; physicians and medical decision-making process, 37–40 Deductive approach, 195
Deepwater Horizon, 528, 548 Defect factor check sheet, 135–136 Defect location check sheet, 136–137 Defective item check sheet, 135 Defensiveness, 164
Degree of freedom, 338
Demand: copayments with eff ect of third-party payment on, 84; defi nition, 67; law of, 75; medical services supply and, 73–74; reducing, 92; supplier-induced, 46, 85–87
Demand curve, 77; defi nition, 75–76; for physician visits, 78; for physician visits by diff erent individuals, 79
Deming, W. Edwards (1900–1993), 24–25, 555, 558 Denial, 42, 67, 69, 71, 490, 548–549 Dependent variable (DV), 142, 336, 344, 355–356, 357, 359, 502 Depression, 447, 461, 463, 466 Descriptive statistics, 222 Detection, 390, 397 DeToro, I. J., 512, 519 Detre, K. M., 417 Dew, J. R., 201
Diabetes, 451; mellitus guidelines, 432; type 2, 459–460
Diagnoses: diff erential, 459; principal, 493; severity of principal, 502
Diagnosis-related groups (DRGs), 69, 94; 143 LOS 2005, 144; reimbursement, 88–89; with structure of Medicare MS-DRG reimbursement system, 491–492 Diagrams: cause and eff ect, 25, 137–140,
254; scatter, 137, 141–145, 355; Venn, 439; Why-Why, 181–183, 389
Dietary concerns: calculate control limits, 310; calculate descriptive statistics, 310; collect data, 309; December 2009, 309; graph actual and expected performance, 310; interpret performance, 311;
investigate instability and improve as needed, 311–312; u chart for, 310 Diff erential diagnoses, 459
Dilation, 44
Direct relationship, 142 Disability, 10
Discharge planning, 444 Discipline, lack of, 24
Discomfort, 10, 234–235, 502–503 Discrepancy, 457
Discrete data, 220
Disease management, 437, 449; case, outcomes and, 439; cases, 459–463; defi nition, 53; distribution of U.S. medical spending and, 438; fi ve most costly medical conditions, 450; goals, 458; with opportunities for improvement, 451–452; practice requirements for, 456; program and general structure, 458–463; program development for, 452–457; program development steps, 452; PubMed
searching and, 454; with role of self-care, 457–458
Disease management cases: CBO review of costs and health outcomes, 461–462; review of clinical and economic outcomes in chronic disease, 462–463; review of cost reduction in chronic conditions, 461; telemedicine, 460–461; type 2 diabetes, 459–460
Disease Management for Nurse Practitioners, 458
Disease-modifying antirheumatic drugs (DMARDs), 446
Diseases: chronic kidney, 430–431; comorbidities, 39, 185, 498; defi nition, 10; iatrogenic illness and, 502;
information on injury and, 38; review of clinical and economic outcomes in chronic, 462–463. See alsospecifi c disease
Dissatisfaction, 10, 292, 377, 385, 422, 502 Dissemination, 42–43, 421, 547–548 Distribution: marketing and, 385;
NORMDIST, 127, 223–224 DMARDs. See Disease-modifying
antirheumatic drugs
Do, 23, 24. See also Plan-Do-Check Act cycle
Doctors. See Physicians
Donabedian, Avedis, 22, 53, 59, 102–103, 478, 516; on quality and variation, 552–553; structure-process-outcome paradigm, 50–51
Door-to-needle time (DTNT), 259–261 Dosages, medications, 234
Drag and drop, chi-square analysis and, 342
DRGs. See Diagnosis-related groups Drucker, Peter, 148
Drugs, 377; of addiction, 487; ADR, 211, 253–254, 431; allergies, 28, 203, 503; DMARDs, 446; pareto chart examining rate of adverse drug events, 120; pareto chart of adverse drug events, 119. See also Medications; specifi c drugs DTNT. See Door-to-needle time Duclos, A., 291–292, 294 Due process, procedural, 490 Duncan, W. J., 385, 526–527 Duwe, B., 396
DV. See Dependent variable
E
EBM. See Evidence-based medicine Economic Control of Quality of
Economic credentialing, 488–490
Economics, health outcomes and, 419, 420 Economics, of health care markets: caveats,
85; copayments, 83–84; decision to produce, 79–80; decision to purchase, 75–77; demand and supply of medical services, 73–74; demand curve, 77–79; health insurance and moral hazard, 82–83; supplier-induced demand and target income hypothesis, 85–87; supply curve, 80–82
Economy of eff ort, 148
Eddy, David, 40, 58, 413–414, 415, 419; medical decision-making process and, 37–38; practice policies and, 426 Edgman-Levitan, S., 377
Education, 16–17; as black box process, 29; nursing, 14
Eff ective, 21
Eff ective feedback, 479 Effi cacious, 554 Effi cient, 21 Eff ken, J., 465, 466 Eff ort, economy of, 148 Egan, M., 536
Einstein, Albert, 42, 551, 558 Elasticity, 84
Ellrodt, G., 454, 541
Ellwood, P. M., 364, 365, 463–464 Emergency room services, SPC and,
292–293
Emergency room (ER) wait times: calculate control limits, 242–243; calculate descriptive statistics, 242; collect data, 241–242; graph actual and expected performance, 243; interpret graphs, 244; investigate instability and improve as needed, 244; January–May 2012, 242; process distribution check sheets, 133 Encapsulation, 548
End Result Idea, 17–18 England, 46
Environment: with cause and eff ect template, 140; as cause of errors, 172– 173; defi nition, 7; external and internal, 374–375
Epstein, A. M., 484 Equifi nality, 379
Equilibrium price, 80
Equipment: cause and eff ect template, 139; error with facilities, materials and, 171–172
ER. See Emergency room wait times Error term, 345
Errors: active, 42, 169, 170; c chart for medical record, 307; causes of, 170–173; classifying, 169–170; cognitive, 171; of commission, 165; environment, 172–173; execution, 40; with facilities, equipment and materials, 171–172; human, 40–43, 170–171, 401; inspection and, 171–172, 399, 401; knowledge-based, 41–42, 168; latent, 42, 169, 170; medical record, 306; medical record, u chart, 308; mitigation of, 199; mortality rates and medical, 5; mortality rates and medication, 55; occurrence and Swiss cheese model, 166; of omission, 165; pharmaceutical errors per admission, 1; planning, 40; in production process, 26; RCA goal and types of, 164–167; realization and hierarchical safeguards, 201; rule-based, 41–42, 168; skill-based, 40, 42; storage, 40; Th ree Faces of Quality and, 2, 51–54, 549; type I, 226; type II, 226; why errors occur, 167–168
Errors, variation and: James, practice guidelines and, 49–50; medical decision-making process and, 37–40; with outcomes improved and variation controlled, 57–58; reason and human, 40–43; structure-process-outcome paradigm and, 50–51; Th ree Faces of Quality and, 2, 51–54, 549; underuse, overuse, misuse and, 55–56, 106; Wennberg, small area variations and, 43–49
To Err Is Human, 43 Etchells, E., 397
Ethnicity, life expectancy by, 495 Evaluation, of patient preferences, 420 Evans, J. H., 482
Evidence, 419
Evidence-based medicine (EBM), 53, 54, 411, 463
Excel: ANOVA output, 347; ANOVA pairwise output, control vs. staff only, 349; ANOVA pairwise output, control vs. staff /patient, 348; ANOVA pairwise output, staff only vs. staff /patient, 349; frequency function, 123, 124, 129; multiple regression output, 360; NORMDIST formula, 223–224; range and mean, 233–235; regression output, 357 Excellence, operational, 524 Exceptional violations, 168 Exclusive contracts, 490 Execution, 539 Execution errors, 40
Expectations: consumer, 228; with performance and variation, 229 Expenditures, 87
Experimental groups, 186 Expert judgment, 188 Externality, 85
F
FAA. See Federal Aviation Administration Facilities: error with equipment, materials
and, 171–172; licensing of, 67–68 Failure mode and eff ects analysis (FMEA),
373, 404; designing better systems and, 398–403; general system theory, 374– 383; value chain, 383–386
Failure mode and eff ects analysis process, 387; defi ne failure and boundaries, 388; defi ne process, 388–389; document analysis and recalculate residual risk, 397–398; evaluate potential eff ects, 390– 396; failure priority calculations, 394; failure priority work list, 395; identify corrective actions to reduce or eliminate failure, 396–397; identify failure modes, 389–390; identify potential causes for each failure mode, 390; monitor, 398; probability of occurrence, 392; risk priority matrix, 395; steps, 388 Failures: corrective action to reduce or
eliminate, 396–397; heart, 280–282; types of, 389; weapons, 390–391. See also Statistical process control, for monitoring failure rates
Fallacy of composition, 450 Farr, William, 15
Fasting, S., 293, 294 Fastman, B. R., 190
Federal Aviation Administration (FAA), 267
Feedback: defi nition, 28; eff ective, 479; general system theory and, 381–383 FIM. See Functional Independent
Measures
Financial perspective, 523–524, 530 Financing. See Health care fi nancing,
history of
First dollar coverage, 82
Fishbone charts. See Cause and eff ect diagrams
Five Ds of health care quality, 10
Five rights of medication administration, 140–141
Five whys, of data collection, 180–181 Fixed fee per enrollee, 91. See also Per
member per month Flexner, Abraham, 16–17 Flow, information, 41
Flowcharts: common fl owchart symbols, 147; defi nition, 137; RCA and, 179; tools to identify causes, 145–146; Word’s fl owchart capability, 146, 148 Flum, D. R., 493
FMEA. See Failure mode and eff ects analysis
Follow-up, 459
For what population, 493 Ford, Henry, 21, 22, 162 Ford Motor Company, 161 Formulas, 96–97, 223–224 Foster, A. P., 445
Freedom: through control, 555; degree of, 338; of information lawsuit, 474 Frøslie, K. F., 319
Fuchs, B., 396
Functional benchmarking, 514
Functional Independent Measures (FIM), 443
G
Gagarin, Yuri, 547 Gaming of the data, 280
Garnerin, P., 320–321 Gattis, W. A., 451
GDP. See Gross domestic product
Gender: life expectancy by, 493; risk-adjust patients and, 495
General system theory: feedback, 381–383; input, 380–381; outcome, 376–378; output, 378–379; production process and, 375; system view and, 374; throughput, 379–380 Generalizable, 363, 364, 366, 462 Germ theory, 14 Ginter, P. M., 385, 526–527 Gisvold, S., 293, 294 Gittelsohn, Alan, 44, 45–46, 47 Glabman, M., 480 Glickman, S. W., 104, 105 Goals: disease management, 458;
performance goals based on organizations, 228; of quality
management, 5–11, 115; types of errors and RCA, 164–167 Godfrey, A. B., 233, 267, 303 Goetsch, D., 6 Goldstein, M., 428 Goud, R., 290, 294 Gray, J. E., 205 Greenfi eld, S., 483 Grenier-Sennelier, C., 177 Grimshaw, J., 428 Grissom, Virgil, 547
Gross domestic product (GDP), 38, 64 Guidelines: antibiotic practice, 431–432;
clinical practice, 53, 54; defi nition, 416; diabetes mellitus, 432; practice, 49–50; practice standards, options and, 417; SPC and cardiac rehabilitation guidelines application, 290
Guyatt, G. H., 363, 364
H
Halifax Harbor disaster, 18 Hand washing, 13
Handley, M., 447 Hansen-Flaschen, J., 396 Harm, mitigation of, 399, 402 Harrison, 226
Harrison, B., 343
Harvey, Donald, 3–4 Hatakenata, S., 205
Hawaii: LOS in, 130; pay for performance in, 98
Hawthorne eff ect, 260, 291 Hazard. See Moral hazard
Health, economic outcomes and, 419, 420 Health care fi nancing, history of: cost
of quality improvement, 101; cost of treatment, 99–101; DRG reimbursement objective, 89; impact on net income, 101–102; reimbursement methodologies and operating incentives, 90–92; reimbursement with overuse, underuse and misuse, 92–96; return on investment on delivering high-quality care, 96–97; revenue, 98–99
Health care markets. See Economics, of health care markets
Health care systems, 541 Health care value chain, 385, 527 Health expenditures, 87
Health insurance: with 20% copayment, 83; moral hazard and, 82–83; overutilization of physician visits and, 82; self-pay, no, 81 Health problem, 419
Health status, 483 Heart conditions, 450
Heart failure: breach of lower control limit and, 281; smoking cessation example with, 280–282 Heinrich triangle, 165, 166 Hemorrhoidectomy, 44, 46 Henry, D., 479, 482–483 Herniorrhaphy, 44, 45, 46 Hershey, N., 488–489 Hetlevik, I., 432 Hindsight bias, 188 Hip replacements, 71, 258–259 Hippocratic oath, 63, 64, 113 Hispanics, 451, 496
Histograms, 129; distributions, 131; for infant weights, 127; LOS by state, 128; for LOS by state, 130; LOS frequency, 130; with money incomes, 132 Historical probability, 268 Hofer, T. P., 483, 489 Homa, Karen, 354
Hospital Standardization Program, 19 “Hospital Use and Mortality among
Medicare Benefi ciaries in Boston and New Haven” (Wennberg), 47
Hospitals, 534; admissions and variation, 47; CMS Hospital Compare, 516–517, 522; cost of beds in 2003, 173; Medicare hospital quality initiative example, 312– 317; multiple-event analysis on patient falls in VA facilities, 209–212; with purchased inputs, 28; in Texas, 67; U.S. and number of, 163
Huang, S., 529
Human body, temperature of, 219–220 Human error, 170–171; rates and designing
better systems, 401; variation, reason and human, 40–43
Human Error (Reason), 40–43 Hurricane Katrina, 172–173 Hwang, Y., 482 Hypercalcemia, 448 Hyperlipidemia, 463 Hyperthermia, 219–220 Hypocalcemia, 291, 292 Hypothesis: alternative, 336, 344; as essential ingredient for quality improvement, 20; null, 336, 344; target income, 46, 85–87
Hysterectomy, 44, 45, 46, 71
I
Iatrogenic illness, 502
Iezzoni, L., 493, 494, 499–500, 502–505 Improvement, 25; benchmarking and, 20;
costs of quality, 101; CQI, 21, 26, 27, 52, 254, 550; disease management and opportunities for, 451–452; expected outcomes of quality, 561; Institute of Healthcare Improvement, 396–397; process, 20, 21; QI program, 101–102; research and quality, 362–365; SCIP, 104; skills for health care, 19, 20–22 Inattention, 40, 139, 343–344
Incentives, 90–92, 103
Incomes: health care fi nancing and impact on net, 101–102; histograms and money, 132; target income hypothesis, 46, 85–87 In-control, 227. See also Stable
Independent variable (IV), 336 Indiana University Hospital, 534 Inductive approach, 195
Industry: automobile, 21–22;
benchmarking, 513; cottage, 20, 21, 22; information, 228; restaurant, 6–8 Industry, quality control in: Deming
(1900–1993), 24–25; evolution of quality control, 26; evolution of quality management, 26–27; Ishikawa (1915– 1989), 25; PDCA cycle, 23; Shewhart (1891–1967), 23–24
Inequality, 409, 474, 506 Inertia, 33, 274, 422, 424
Infants: birth of, 12; histogram for infant weights, 127; mortality rates, 13, 15, 562; weights, 128
Infections: accreditation scores and, 70; expected, January–April 2012, 337; MRSA, 317–319, 382; observed, January–April 2012, 337; observed, January–August 2012, 339 Inferential statistics, 221 Information, 113; asymmetry, 85; defi nition, 114; fl ow, 41; freedom of information lawsuit, 474; industry, 228; on injury and disease, 38; safeguards, 202–203
Inherent variation, 226. See also Natural variation
Injury, 38
Inputs: clinical patient, 495–499; defi nition, 28; general system theory and, 380–381; nonclinical patient, 499– 501; problems, 28–29; risk adjustment and assessing treatment, outcomes and, 495
Inspection, 24, 26–27, 288, 303, 380; error and, 171–172, 399, 401; operation and, 147; safeguards, 201, 203–205
Institute of Healthcare Improvement, 396–397
Institute of Medicine (IOM), 5, 43, 55, 56, 164, 176, 381
Institutional review board (IRB), 364 Insurance: defi nition, 74; health, 81–83 Integration, 399
Internal benchmarking, 513 Internal perspective, 525, 529
Interventions, comparison with other, 421 Inverse relationship, 142
Investments, 96–97 Ioannidis, J. P., 39, 364, 415 IOM. See Institute of Medicine IRB. See Institutional review board Ireland, 43
Irving, M. J., 430
Ishikawa, Kaoru (1915–1989), 25, 165 Ishikawa diagram. See Cause and eff ect
diagrams
Is-Is Not matrix, 184, 186, 230; Is-Is Not example, 187; Is-Is Not template, 185 IV. See Independent variable
IV pumps, 402 J James, B. C., 418, 432 James, Brent, 37, 49–50, 57, 58, 380, 429– 430, 465 Jamtvedt, G., 480 Japan, 21, 24 Johns Hopkins, 16 Johnson, C. C., 258, 259 Johnson, D., 482 Joint Commission, 19, 51, 120, 164, 167; accreditation and, 68–69; FMEA and, 387; RCA and, 173–174, 176; sentinel events and, 231
Joint Commission on Accreditation of Healthcare Organizations, 19
Joint Commission sentinel events: pareto chart, 122; statistics: Jan 1995–March 2009, 121
Jones, M. L., 467 Judgment, 38–39, 188 Juran, J. M., 233, 267, 303
Jury verdict, innocence or guilt, 225
K Kaerhle, P., 465, 466 Kahan, N., 419, 424 Kaimal, A., 417 Kaplan, H. S., 190 Kaplan, R., 524, 528 Kassirer, J., 480, 484 Kepner, C. H., 184, 186
Key performance indicators (KPI), 531 Kidney disease, chronic, 430–431 Killers, serial, 3–4 Kizer, K., 377 Knowledge, 363, 422; hierarchy, 114; safeguards, 203; skill-rule-knowledge framework, 167 Knowledge-based errors, 41–42, 168 Kohatsu, N. D., 67
KPI. See Key performance indicators Krein, S. L., 477 Kupperman, M., 417 Kurtz, E., 442 L Ladd, A., 399, 402 Lairson, B. R., 459 Lapses, 40–41, 167 Latent errors, 42, 169, 170 Laws: CON, 67; of demand, 75; of
diminishing marginal utility, 76 Learning and growth perspective, 531 Legal system, 226
Legislation. See Laws Leider, H., 463
Length of stay (LOS), 94–95; DRG 143, 2005, 144; frequency, 130; histogram for LOS by state, 130; scatter diagram of LOS and patient volume, 145; SPC and, 232; by state, 128
Levich, B. R., 457, 458 Licciardone, J. C., 67
Licensure: accreditation and, 68–70; credentialing and, 70–71; defi nition, 65; licensing of facilities, 67–68; occupational licensing, 65–66; suspended licenses, 66–67
Life: birth and expectation of, 561–562; health-related quality of, 499–500, 503; quality of, 493
Life expectancy: by gender, 493; by race and ethnicity, 495; in U.S., 88
Likert scale, 377
Limits. See Control limits Lindenauer, P. K., 104 Line, center, 238 Lister, Joseph, 14, 365
LL Bean, 515 Localio, A. R., 72, 73
Long-term follow-up, of patients, 17, 18 LOS. See Length of stay
Loss, deadweight, 83
Lower control limits, 224, 353 Lucey, C., 440
M
Machiavelli, Niccolò, 1, 5, 34 Mainous, A. G., 482–483 Maljanian, R., 465, 466
Malpractice, 71, 228; claims fi led, 73; suits (2001–2010), 72
Managed care, 94, 95
Management, 547–548, 565; case, 53, 54, 437–449; control limits calculated and medication, 246–247; CRM, 526; disease, 53, 437–439, 449–463; evolution of quality, 26–27; with expectation of life at birth, 561–562; expected outcomes of quality improvement and, 561; function of, 25; future of health care and quality, 561–564; goal of quality, 5–11, 115; with health care quality defi ned, 9–11; with infant mortality rates, 562; medical practice, 556–558; medication, 244–249; mission, measurement and, 565; outcomes, 53, 437–439, 463–469; patients and self-, 459; quality and variation with, 551–553; with quality defi ned, 6–9; with Reason’s system reforms, 549; research and quality, 334; successful, 549–558; theory of quality, 52; three conditions for reform and, 563–564; tools for quality, 553–556; TQM, 21, 26, 27, 52–53; value and role of managers with, 558–561
Managers: duties of case, 449; role of case, 441; value and role of, 558–561
Manpower, with cause and eff ect template, 139 Marginal benefi t, 75–77, 81, 83 Margo, C. E., 425 Marketing, 385 Mars company, 22 Marshall, M. N., 473 Martin, M., 258, 259 Mass customization, 20, 21, 22 Mass production, 20, 21, 22 Mastectomy, 44 Materials, 171–172
Maternity wards, mortality rates, 13, 15 Mattie, L., 442
McGlynn, E. A., 56, 231, 451 McNeil, B., 418
McPherson, K., 46
Mean, 222–223, 233–235. See also Analysis of means
Measurement, 11, 255, 465; CM with accountability and, 444–445; mission, management and, 565; safeguards, 203 Medicaid, 93, 102–105, 516–517, 522 Medical conditions, fi ve most costly, 450 Medical decision making, 37–40 Medical events, root causes of, 173;
sentinel events and, 174; typologies of, 175
Medical Outcomes Study Short Form, 443 Medical practice management, 556–558 Medical practice tools, 53
Medical record coding: calculate control limits, 307; calculate descriptive statistics, 306; collect data, 306; graph actual and expected performance, 307–308; interpretive performance, 308; investigate instability and improve as needed, 309; medical record errors, 306; medical record errors c chart, 307; medical record errors u chart, 308 Medical schools, 16–17
Medical services, 73–74
Medical staff independence, 490
Medicare, 47, 86–87, 93, 95, 560; AMI and, 362; CMS, 98, 102–105, 516–517, 522; MS-DRG reimbursement system and structure of, 491–492; Quality Initiative, 563; reimbursement, 48, 88–89, 90 Medicare hospital quality initiative,
307–308, 312; AMI treatment, January 2011–August 2012, 315; AMI treatment chart, 316; AMI treatment check sheet, 314; calculate control limits, 314–315; calculate descriptive statistics, 313–314; collect data, 313; graph actual and expected performance, 315; interpret
performance, 315–316; investigative instability and improve as needed, 316–317
Medication management, 244; calculate control limits, 246–247; calculate descriptive statistics, 245–246; collect data, 245; graph actual and expected performance, 247; interpret graphs, 248; investigate instability and improve as needed, 248–249; X1 and R charts and Rx administration, 247
Medications, 459; cause and eff ect diagram for wrong, 141; dosages, 234; fi ve rights of medication administration, 140–141; stratifi cation chart for patient falls by level of, 126; top 10 in adverse events, 117–118; top 10 in adverse events: most frequent to least frequent, 119
Medicine: as black box process, 29; EBM, 53, 54, 411, 463; telemedicine, 460–461 Medicine, cookbook, 10, 412 Meliones, J., 529 Menger, Carl, 76 Mental disorders, 450 Merwin, E., 70 Michael, C., 441 Michigan, 98 Midwives, clinics, 12–13 Miller, D. C., 478 Miller, M. R., 69
Mission: measurement, management and, 565; organization’s, 11
Mistakes, 168. See also Errors Misuse, 37, 38; reimbursement with
overuse, underuse and, 92–96; underuse, overuse and, 55–56, 106
Mitigation: of errors, 199; of harm, 399, 402; systems, 387; techniques, 396 Models: clinical decision making, 413;
error occurrence and Swiss cheese, 166; medical decision making, 38; OM, 466; system, 28, 31–33
Money incomes, histograms and, 132 Monitoring, 443–444
Moral hazard: defi nition, 75; health insurance and, 82–83; overutilization of physician visits and, 82
Morbidity, 10 Morgan, M., 536 Morreale, G. F., 482 Morrison, J., 66, 67
Mortality: data and board certifi cation, 340; deaths and, 10, 17, 161, 496, 547– 548; surgical, 342
Mortality rates: accreditation and, 69; anesthesia deaths and, 17; antibiotics, 432; of bariatric surgery, 18; cardiac surgery, 270; clinic (1841–1846), 12; defi nition, 10; infants, 13, 15, 562; leading causes of death by race, 2007, 496; medical error and, 5; medication error and, 55; in New York with CABG, 474, 475; with preventable deaths, 9, 10; prostatectomies, 18
Motivational interviewing, 457 MRSA. SeeStaphylococcus aureus Multiple-event analysis, 189–190; cases
added to adverse event data base and, 198–199; data analysis, 195–196; data collection, 191–195; with preventative action implemented, 196–198
Multiple-event analysis, patient falls in VA facilities: corrective action implemented, 210–212; data analysis, 209–210; data collection, 209; outcomes achieved for fall prevention at VA hospitals, 210; root causes contributing to patient falls, 210
Multivoting: defi nition, 148; fi nal vote results, 150; fi rst vote results, 150; process, 149; second vote results, 150; table of preferences, 149
Myocardial infarction, 362
N
Nagarian, N., 482
NASA. See National Aeronautics and Space Administration
Nash, D. B., 456–457
Nash, David, 2, 57, 59, 103, 459; OM and, 463; Th ree Faces of Quality, 2, 51–54, 549
National Aeronautics and Space Administration (NASA), 547–548 National Cancer Institute, 498
National Health Service (NHS), 180 National Pneumonia Project, 104 Native Americans, 451
Natural safeguards, 202 Natural variation, 226–227 Naylor, C. D., 364
Near-miss events, 165
Need-based decision making, 63, 64 Negligence, 72
Nelson, E., 464–465 Nerve palsy, 291 Net income, 101–102
Nevada State Health Division, 68 New England, 46 New Haven, 47, 48 New Jersey, 270 New Orleans, 172–173 New York, 130, 474, 475, 525 Newsday, 474
NHS. See National Health Service
Nightingale, Florence (1820–1910), 14–17, 476, 552, 553 No relationship, 142 Noetscher, C. M., 482 No-harm events, 164 Nolan, T. W., 398, 400, 402, 465 Nonconformance, 267, 276 Nonconformities, 301, 302–303. See also Statistical process control, for monitoring nonconformities Noninherent variation, 227. See also
Special cause variation Nonlinear relationship, 142
Normal distribution (NORMDIST), 127, 223–224
North Carolina: MRSA in, 382; occupational licensing in, 65–66 North Dakota, 130
Norton, D., 524, 528 Norway, 46
Norwegian National Public Health Institute, 319–320
np charts: defi nition, 269; for
nonconforming surgical claims, 279; p and, 266–282 Null hypothesis, 336, 344 Nuovo, J., 457 Nurses, 14, 440, 458, 466 O Oath, Hippocratic, 63, 64, 113 Occam’s razor, 140 Occupational licensing, 65–66 O’Connell, D. L., 479, 482–483
OECD. See Organisation for Economic Co-operation and Development Ofman, J., 462
Olin, G. L., 450
OM. See Outcomes management Omission, errors of, 165
O’Neill, C., 397
Operating incentives, 90–92 Operating room dashboard, 535 Operational excellence, 524 Opportunity costs, 64 Options, 416, 417
Organisation for Economic Co-operation and Development (OECD), 64
Organizational architecture, 540–541 Organizations: mission of, 11; performance
goals based on, 229; with transformation processes monitored, 30, 31
Osheroff , J. A., 427 Outcome measures, 478
Outcomes: benchmarking structure-process-outcomes, 517; CBO review of costs and health, 461–462; chronic disease with review of clinical and economic, 462–463; clinical, 502; CM functions and intermediate and fi nal, 444; Codman and patient, 474, 521; coin tossing, 227; defi nition, 4, 9; general system theory and, 376–378; health and economic, 419, 420; peer comparisons and, 18; quality improvement and expected, 561; risk adjustment and assessing inputs, treatment and, 495; risk factors and, 502–504; structure-process-outcome paradigm and, 50–51; system thinking and, 30–31; theories, tools and, 54; variations controlled and improved, 57–58
Outcomes management (OM), 437, 463–464, 469; case, disease and, 439; case studies summary, 468; cases, 467–468; defi nition, 53; distribution of U.S. medical spending and, 438;
model, 466; program development for, 465
Outcomes management cases: cesarean section rates and, 467; clinical pathways in perinatal care and, 467–468
Out-of-control, 228. See also Unstable Outpatient costs, 461
Outputs: defi nition, 20; Excel ANOVA, 347; Excel ANOVA pairwise output, control vs. staff only, 349; Excel ANOVA pairwise output, control vs. staff /patient, 348; Excel ANOVA pairwise output, staff only vs. staff /patient, 349; Excel multiple regression, 360; Excel regression, 357; general system theory and, 378–379; quality control processes and, 30; why systems don’t product expected, 219. See also Quality output
Overuse, 37, 38; reimbursement with underuse, misuse and, 92–96; underuse, misuse and, 55–56, 106
P
p charts: for aspirin delivery to AMI patients, 288; defi nition, 269; np and, 266–282; for surgical coding accuracy, 278
Pain: chronic, 463; levels, 345–349 Parathyroidectomy, 448
Pareto charts: of adverse drug events, 119; defi nition, 116; examining rate of adverse drug events, 120; joint commission sentinel event statistics: Jan 1995–March 2009, 121; of joint commission sentinel events, 122; top 10 medications in adverse events, 117–118; top 10 medications in adverse events: most frequent to least frequent, 119 Pareto rule, 116–117, 120
Park, K., 535–536 Patel, B., 466
Pathophysiology, causes and, 459 Patients: aspirin administration among
AMI, 287; clinical patient inputs, 495– 499; Codman and patient outcomes, 474, 521; evaluation of patient preferences, 420; Excel ANOVA pairwise output, control vs. staff /patient, 348; Excel
ANOVA pairwise output, staff only vs. staff /patient, 349; factors, 28; falls, 123; high-risk, 57; long-term follow-up of, 17, 18; multiple-event analysis on patient falls in VA facilities, 209–212; nonclinical patient inputs, 499–501; number of patients prescribed Ritalin, 352; outpatient costs, 461; p chart for aspirin delivery to AMI, 288; physicians and relationships with, 18, 19, 29, 39, 86, 325; rebased control limits for aspirin delivery to AMI, 289; run chart of patient falls, 153; run chart of patient falls with average, 154; run chart of patient falls with trendline, 154; satisfaction of, 6–9; self-management, 459; single-event analysis on wrong, 205–209; stratifi cation chart for patient falls by age, 125; stratifi cation chart for patient falls by level of medication, 126; time between registration and patient fall, 193, 194; variance analysis and patient-reported pain, 345; volume and scatter diagram of LOS, 145
Patterns, 272 Patwardhan, M., 427
Pay for performance, 98, 102–105 Payments. See Copayments
PDCA cycle. See Plan-Do-Check Act cycle Peek, N., 290, 294
Peer comparisons, outcomes and, 18 Penicillin allergies, 28
Pennsylvania Cost Containment Council, 144
People, with cause and eff ect template, 139 Per case reimbursement, 94
Per diem reimbursement, 91
Per member per month (PMPM), 91–92, 98 Perez, H. E., 466
Performance, 473; benchmarking and improving, 523; by day, 252; across days, 251–253; dietary concerns and, 310–311; ER wait times, graph actual and expected, 243; goals based on organizations, 228; KPI, 531; Medicare hospital quality initiative and interpret, 307–308; medication management, graph actual and expected, 247; pay for,
98, 102–105; across shifts compared, 251; variations and expectations with, 229. See also Statistical process control, for monitoring system performance Performance targets: with expected
performance and variation, 229; setting, 228–231
Perinatal care, 467–468 Pestotnik, S. L., 431, 432 Peters, C., 467
Pharmaceutical errors per admission, 1 Physical functional status, 493, 499, 502 Physical safeguards, 199, 202
Physician cost profi ling, 488 Physician profi les, 53, 481
Physician profi ling: concerns over use of practice profi les, 484–485; practice profi les developed and, 477–482; profi ling cases and, 482–484; report cards and, 476–485
Physicians: clinical judgment of, 38–39; complaints lodged against, 66; demand for visits from, 78; with demand for visits from diff erent individuals, 79; medical decision-making process, 37–40; overutilization of physician visits and, 82; patients and relationships with, 18, 19, 29, 39, 86, 325; practice style of, 45; role of, 10; supply and demand for visits by, 81; suspended licenses, 66–67; U.S. and number of, 163, 437
Pioneers, in health care: clinic mortality rates (1841–1846), 12; Codman (1869– 1940), 17–19; Nightingale (1820–1910), 14–17; number of U.S. medical schools, 16; Semmelweis (1818–1865), 12–14 PivotTable, 335, 336, 341
Plan, 23
Plan-Do-Check Act (PDCA) cycle, 23, 24, 274–275, 311, 365, 424
Planning, 443, 537; discharge, 444; errors, 40
PMPM. See Per member per month Policies. See Practice policies Political desirability, 538 Political feasibility, 538 Pollard, J. B., 320–321 Population, for what, 493
Porter, M., 384
Postexecution control, 537, 540 Power, J. D., 302
Practice guidelines, 49–50; benchmarking and, 512–523; clinical, 53, 54
Practice policies, 411, 414–415, 433; cases and applications, 428–432; clinical decision-making model, 413; components, 419–421; computer decision support, 427–428; defi nition, 412; implementation approaches to avoid, 425–426; implementation of, 421–425; practice standards, guidelines and options, 417; tailoring, 421; uses and barriers of, 426–427; variety of, 416–418; ventilator compliance and, 430
Practice profi les: concerns over use of, 484–485; developing, 477–482; implementing, 477–482; physician profi les and, 481
Practice style, 45
Preferences, attitudes and, 500–501 Pregnancy, high-risk, 447–448 Premiums, medical malpractice, 72 Prescription rates: ANOM control limits
for Ritalin, 353; Ritalin, 351; upper and lower control limits for Ritalin, 353 Prevention, 387
Prevention, recovery and: hierarchical safeguards and error realization, 201; with reliability and eff ectiveness of safeguards, 200; root cause analysis and, 199–205; types of safeguards, 201 Preventive action: implementing, 196–198;
with single-event analysis, 188–189 Price, 26; equilibrium, 80; marketing and,
385; satisfaction and, 7, 559 Principal diagnosis, 493 Principal-agent relationship, 85 Probability, historical, 268 Probability of occurrence, 397 Problems: cause and eff ect diagram,
254; diagnosing, 249–251; health, 419; identifying major causes of, 253–254; input, 28–29. See also Tools, to identify problems
Process, 27, 51; benchmarking, 514–522; with cause and eff ect template, 140 Process analysis. See Tools, process
analysis
Process distribution check sheets: with dates, 134; ER wait time, 133
Process improvement, 20, 21 Process measures, 477 Producers, 8
Product leadership, 524 Production, mass, 20, 21, 22 Production processes: errors in, 26;
evolution of, 20–22; with general system theory, 375
Products, 6, 8, 385
Profi ling, 473–474, 506; academic detailing and, 485–488; CABG mortality rates in New York, 474, 475; cases, 482–484; economic credentialing and, 488–490; physician cost, 488; report cards and physician, 476–485; risk adjustment and, 490–505
Progress. See Tools, to monitor progress Promotion, marketing and, 385
Prostatectomies: mortality rates, 18; rates, 44, 45, 46
Provenzale, D., 71 Provider incentives, 92
Pseudomonas aeruginosa, 319–320 Psychological functioning, 500 Public interest theory of regulation, 65 PubMed, 38, 139, 453, 454
Pulmonary conditions, 450 Purchasing decisions, 75–77 p-value, 347
Q
QI. See Quality improvement
Q-sort, 150; defi nition, 148; process, 151; ranked interventions, 152; table of preferences, 151; weighted preferences, 152
Quach, S., 505
Qualities, importance of, 6
Quality, 26; defi nition, 6; of environments, 7; management variation and, 551–553; with price and value, 7; producers as
evaluators of, 8; products, 6; selections, 7; service, 7; Th ree Faces of, 2, 51–54, 549; timeliness and, 7
Quality, in health care: defi ned, 9–11; fi ve Ds of, 10; industry quality control, 23–27; management and future of, 561–564; management goals, 5–11, 115; pioneers, 12–19; skills for improving, 19–22; system thinking, 27–33 Quality, regulating quantity and:
economics of health care markets, 73–87; health care fi nancing history in U.S., 88–102; licensure, accreditation and credentialing, 65–71; malpractice, 71–73; pay for performance and CMS quality initiative, 102–105
Quality circle, 25
Quality control: evolution of, 26; in industry, 23–27; processes with outputs, 30
Quality improvement (QI): benchmarking and, 20; cost of, 101; CQI, 21, 26, 27, 52, 254, 550; management and expected outcomes of, 561; program, 101–102; research and, 362–365
Quality management theory, 52 Quality of life, 493
Quality output, 24
Quantity. See Quality, regulating quantity and
R
R charts, 231–254
Race: leading cause of death by, 496; life expectancy by, 495
Random events, 501–502 Randomization, 363
Randomized controlled trial (RCT), 49 Raney, G., 458
Range, mean and, 233–235 Rasmussen, Jens, 40, 167, 168
Rates: of adverse drug events, 120; cesarean section, 467; design of better system and human error, 401; prostatectomies, 44, 45, 46. See also Mortality rates; Prescription rates; Statistical process control, for monitoring failure rates
RBRVS. See Resource-based relative value scale
RCA. See Root cause analysis
RCT. See Randomized controlled trial Reason, James, 40–43, 167, 549 Rebasing, 287–290
Recovery. See Prevention, recovery and Reengineering, 373, 374, 403
Reexamination, 399 Referral, 459
Reform: defi nition, 548; dissemination, reorganization and, 42; Reason’s system, 549; three conditions for, 563–564 Regression, 334
Regression, multiple, 359, 361; cholesterol data set, 358; Excel multiple regression output, 360; multiple regression dialog box, 360
Regression analysis, 354; applications of, 362; Excel regression output, 357; with multiple regression, 358–361; regression dialog box, 356; scatter diagram of average charge and case volume, 355
Regulating. See Quality, regulating quantity and
Regulation, capture theory of, 65 Regulation, public interest theory of, 65 Reimbursement: charge, 90; cost, 88,
90–91; DRG, 88–89; Medicare, 48, 88–89, 90; methodologies and operating incentives, 90–92; with overuse,
underuse and misuse, 92–96; per case, 94; per diem, 91; provider incentives under diff erent systems of, 92; with structure of Medicare MS-DRG reimbursement system, 491–492 Relationships: CRM, 526; patient-physician, 18, 19, 29, 39, 86, 325; principal-agent, 85; types, 142 Reorganization, 42, 399 Repair, 548
Report cards, 476. See also Physician profi ling
Research: quality improvement and, 362–365; quality management and, 334 Resistance, rolling with, 458
Resource use measures, 477, 503 Resource-based relative value scale
(RBRVS), 93
Resources, tools to identify solutions, 148 Response, temperature and, 220
Restaurant industry, 6–8
Return on investment (ROI), 96–97 Revenue, 98, 99
Rewards, 103–104
Rheumatoid arthritis, 445–447 Rhoades, J. A., 450
Ricci, M., 177
Risk: tools to identify solutions and, 148; of what, 493
Risk adjustment: clinical patient inputs and, 495–499; with inputs, treatment and outcomes assessed, 495; nonclinical patient inputs and, 499–501; outcomes and, 502–504; profi ling and, 490–505; random events and, 501–502; risk factors and, 493–504; scoring APS in APACHE II system, 504–505; structure of Medicare MS-DRG reimbursement system, 491–492; systems, 504–505; treatment and, 501
Risk Adjustment for Measuring Healthcare Outcomes (Iezzoni), 503–504
Risk priority number (RPN), 395 Ritalin: number of patients prescribed,
352; prescription rates, 351; prescription rates with ANOM control limits, 353; prescription rates with upper and lower control limits, 353
Roemer, M. I., 46
ROI. See Return on investment Røislien, J., 319
Rolling with resistance, 458
Root cause analysis (RCA), 161, 163; aggregate, 176; cases and application, 205–212; defi nition, 162; error causes, 170–173; error classifi cation, 169–170; goal of RCA and types of error, 164–167; Heinrich triangle, 166; of medical events, 173–176; multiple-event analysis, 189–199; multiple-event analysis: patient falls in VA facilities, 209–212; prevention and recovery, 199–205;
event analysis, 178–189; single-event analysis: wrong patient, 205–209; SPC and, 217–218; Swiss cheese model of error occurrence, 166; types, 176–177; why errors occur, 167–168
Ross, G., 482
Rossi, P., 442, 443, 448
Routine violations, 168, 171, 204 RPN. See Risk priority number Rudolph, J. W., 205
Rule-based errors, 41–42, 168
Rules: Pareto, 116–117, 120; skill-rule-knowledge framework, 167
Run charts, 155; dashboard, 534; defi nition, 152; of patient falls, 153; of patient falls with average, 154; of patient falls with trendline, 154
S
Sabotage, 168, 171, 202 Sackett, D. L., 363
Safeguards: error realization and
hierarchical, 201; inspections and, 201, 203–205; reliability and eff ectiveness of, 200; types of, 199, 201–203
Saigal, C. S., 478
Sample, convenience, 363
Sanitation: germ theory and, 14; hand washing and, 13
Satisfaction: health-related quality of life and, 503; of patients, 6–9; price and, 7, 559
Scatter diagrams, 141–143; of average charge and case volume, 355; defi nition, 137; DRG 143 length of stay 2005, 144; of LOS and patient volume, 145
Schectman, J. M., 482 Scheduled time, 267 Schneider, E. C., 484
Schools. See Medical schools Schwab, R. A., 294
Science, 44
SCIP. See Surgical Care Improvement Project
Scorecards. See Balanced scorecards Seigel, D., 487
Selection, 7, 48
Selection bias, 363 Self-care, role of, 457–458
Self-management, patients and, 459 Self-pay, no health insurance and, 81 Self-selection bias, 454
Sematech, 393
Semmelweis, Ignaz (1818–1865), 12–14, 365, 552, 553
Sensitivity, 55, 56
“Sentinel Event Data: Root Causes by Event Type, 2004–2012” (Joint Commission), 173–174 Sentinel events, 164, 231 Serb, C., 536 Serial killers, 3–4 Service, 7, 26 Severity, 478, 502 Shahian, D. M., 257 Shain, M., 46 Shaw, C., 350 Shaw, J., 487 Shea, C., 174 Shewhart, Walter (1891–1967), 6, 27, 226, 228, 553, 558; on quality and variation, 551–552; with quality control in industry, 23–24; SPC and, 232 Shojania, K. G., 188 Shortell, S. M., 4 Short-sightedness, 24 Shumway, N. M., 448 Simon, S. R., 486 Simplifi cation, 399 Sinanan, M., 145
Single-event analysis: corrective or preventative action implemented, 188– 189; data analysis and identifying causes, 188; data collection techniques, 180–187; single case root cause analysis processes and, 178
Single-event analysis, wrong patient, 205; corrective action implemented, 209; data analysis, 207–208; data collection, 206–207; timeline of events, 207
Sins, quality output and seven deadly, 24 Sipkoff , M., 57
Skill-rule-knowledge framework, 167 Skills, for health care improvement:
essential ingredients for quality improvement, 20; evolution of production processes, 20–22; medical and analytical, 19
Slips, 40–41, 167 Sloan, F. A., 71 Slogans, 25
Slope coeffi cient, 357 Small area variations, 37, 38 Smith, C., 540
Smith, S. A., 460–461
Smoking cessation: breach of lower control limit and, 281; heart failure example with, 280–282
Social functioning, 500 Socioeconomic status, 500 Solomon, D. H., 488
Solutions. See Tools, to identify solutions South Dakota, LOS in, 130
SPC. See Statistical process control Special cause variation, 226 Specifi city, 55, 56
Spending, U.S. and medical, 438. See also Costs
Stable, 227
Staff : Excel ANOVA pairwise output, control vs. staff only, 349; Excel ANOVA pairwise output, control vs. staff /patient, 348; Excel ANOVA pairwise output, staff only vs. staff /patient, 349 Standard deviation, 222 Standardization, 21, 53–54 Standards, 416 Standards, practice, 417 Standiford, L., 467 Stanton, M., 447
Staphylococcus aureus (MRSA), 317–319, 382
Statistical Abstract of the United States, 38, 161, 495
Statistical analysis, 188
Statistical process control (SPC), 23, 24, 26; barriers to use of, 327; benefi ts of, 323–324; inhibitors of use of, 325–329; steps, 232
Statistical process control, for binomial data: accuracy of surgical coding example, 275–280; calculate control limits, 269–271; calculate descriptive statistics, 268–269; collect data, 266–268; graph actual and expected performance, 271; heart failure and smoking cessation counseling example, 280–282; indicators of unstable systems, 273–274; interpret performance, 271– 274; investigate instability and improve as needed, 274–275; steps, 266
Statistical process control, for continuous data: X1 and R charts, 231; calculation of control limits, 235–237; calculation of descriptive statistics, 233–235; comparing performance across shifts, 251; control chart factors for X1 and R charts, 236; data collection, 232–233; diagnosing problems, 249–251; ER wait time example, 241–244; graphing performance, 238; importance of calculating mean and range, 233–235; interpreting performance, 238–240; investigate instability and improve as needed, 240–241; major causes of problems identifi ed, 253–254; medication management example, 244– 249; performance across days, 251–253; steps, 232; X1 and R charts, 236, 239 Statistical process control, for counts: c
and u charts, 302–312; calculate control limits, 304–305; calculate descriptive statistics, 303–304; collect data, 303; dietary concerns example, 309–312; graph actual and expected performance, 305; interpret performance, 305; investigative instability and improve as needed, 305; medical record coding example, 306–309; steps, 303
Statistical process control, for monitoring failure rates, 265, 296; for binomial data: p and np charts, 266–282; cases and applications, 290–294; constant and variable control limits, 284–286; performance improved using SPC, 295; with rebasing control limits and
monitoring changes over time, 287–290; valid control limits established, 282–284 Statistical process control, for monitoring
nonconformities, 301, 330; benefi ts, 323–324; cases and applications, 317–321; control chart selection, 321– 322; counts: c and u charts, 302–312; inhibitors of use of, 325–329; Medicare hospital quality initiative example, 312–317
Statistical process control, for monitoring system performance, 217–218; Bonetti et al. SPC chart, 260; cases and application, 257–261; for continuous data: X1 and R charts, 231–241; with corrective action required, 219–220; Johnson and Martin SPC chart, 259; with performance targets set, 228–231; Shahian et al. SPC chart, 257; SPC and cardiac surgery application, 257–258; SPC and door-to-needle time application, 259–261; SPC and hip replacement application, 258–259; statistics review, 220–226; temperature and response, 220; tinkering, 255–257; variation types, 226–228; why systems don’t product expected output, 219
Statistical signifi cance, 334 Statistics, 52, 557; descriptive, 222;
inferential, 221. See also Tools, statistical Statistics review, 220–223; jury verdict
and innocence or guilt, 226; normal distribution, 224; statistical inference and system state, 225
Storage errors, 40
Strategies, for benchmarking, 538, 540–542; with change induced, 539; prerequisites for successful implementation, 537
Stratifi cation charts, 122, 127; Excel’s frequency function, 123, 124, 129; patient falls, 123; for patient falls by age, 125; for patient falls by level of medication, 126
Structure: defi nition, 50; disease management program and general, 458–463; of Medicare MS-DRG reimbursement system, 491–492 Structure-process-outcome paradigm: benchmarking, 517; Donabedian’s, 50–51 Stryer, D. B., 364
Students, education process and, 29 Style, practice, 45
Summary, 419
Supplier-induced demand: defi nition, 46; health expenditures and impact of, 87; target income hypothesis and, 85–87 Supplies, with cause and eff ect template,
139
Supply: defi nition, 66; medical services demand and, 73–74
Supply curve: economics of health care markets and, 80–82; self-pay, no health insurance, 81; supply and demand for physician visits, 81
Suppression, 548
Surgeries, 459; cardiac, 257–258, 270; mortality rates of bariatric, 18; variation in surgical rates, 44; wrong-site, 121. See alsospecifi c surgeries
Surgical Care Improvement Project (SCIP), 104
Surgical coding example, accuracy of: calculate control limits, 276–277; calculate descriptive statistics, 276; collect data, 275–276; errors in surgical coding, 276; graphing performance, 277–279; interpreting performance, 279; investigate instability and improve as needed, 279–280; np chart for nonconforming surgical claims, 279; p chart for surgical coding accuracy, 278 Surgical mortality, 342
Surplus, consumer, 81, 83 Suspension, of licenses, 66–67 Swayne, L. E., 385, 526–527 Swiss cheese model, 166
Symbols, common fl owchart, 147 System performance. See Statistical
process control, for monitoring system performance
System thinking: black box process and, 29; defi nition, 27; outcome and, 30–31; system model, 28, 31–33; white box process and, 29, 30
Systems: commonly implemented system changes, 400; defi nition, 4; designing better, 398–403; general system theory, 374–383; health care, 541; human error rates with, 401; legal, 226; mitigation, 387; Reason’s system reforms, 549; risk adjustment, 504–505; scoring APS in APACHE II, 504–505; with statistical inference and system state, 225; structure of Medicare MS-DRG reimbursement, 491–492; view, 374; why systems don’t product expected outputs, 219
T
Target income hypothesis: defi nition, 46; supplier-induced demand and, 85–87 Technical safeguards, 199
Techniques, mitigation, 396 Telemedicine, 460–461
Temperature: of human body, 219–220; response and, 220
Test statistic, 338
Testing, 465; as essential ingredient for quality improvement, 20; sensitivity and specifi city with lab, 55–56
Tests, uses of statistical, 334 Texas, 67
Th eories: capture theory of regulation, 65; general system, 374–383; germ, 14; public interest theory of regulation, 65; quality management, 52; tools, outcomes and, 54
Th inking. See System thinking Th ird-party payment, 84 Th ornlow, D. K., 70
Th ree Faces of Quality, 2, 51–54, 549 Th roughputs, 28, 379–380
Th yroid surgery complications application, SPC and, 291–292
Time, scheduled, 267
“Time to Go Public on Performance?” (Marshall), 473
Timeliness, 7
Timing, tools to identify solutions, 148 Tinkering: blood pressure, 255; blood
pressure after, 256; defi nition, 219; SPC for monitoring system performance and, 255–257
Todd, W. E., 456–457, 459 Tomlins, R., 479, 482–483
Tonsillectomies: children and, 43; rates, 44, 45, 46
Tools: medical practice, 53; quality management, 553–556; theories, outcomes and, 54
Tools, process analysis: to identify causes, 137–146; to identify problems, 116–137; to identify solutions, 146–152; knowledge hierarchy, 114; to monitor progress, 152–155; uses of, 115 Tools, statistical, 333, 366; chi-square
analysis, 335–344; means analysis, 350–354; with quality improvement and research, 362–365; with quality management and research, 334; regression analysis, 354–362; uses, 334; variance analysis, 334–350
Tools, to identify causes: cause and eff ect diagrams, 137–140; cause and eff ect example: medication error, 140–141; fl owcharts, 145–146; scatter diagrams, 141–145
Tools, to identify problems: check sheets, 132–137; histograms, 127–132; pareto charts, 116–122; stratifi cation charts, 122–127
Tools, to identify solutions, 146; common fl owchart symbols, 147; multivoting, 148–150; Q-sort, 150–152; risk, economy of eff ort, timing and resources with, 148; world’s fl owchart capability, 148
Tools, to monitor progress: run chart, 152–155; run chart of patient falls, 153; run chart of patient falls with average, 154; run chart of patient falls with trendline, 154
Topp, R., 343
Total quality management (TQM), 21, 26, 27, 52–53
Toyota, 302
TQM. See Total quality management Transformation processes, 30, 31 Trauma, 450
Treatments: AMI, 314–316; costs, 99–101; risk adjustment and assessing inputs,
