Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review
Abstract
:1. Introduction
- RQ1. Which DTs support LH interventions?
- RQ2. What are the effects of LH interventions supported by DTs on healthcare services?
2. Theoretical Framework
2.1. Healthcare 4.0 Digital Technologies
2.2. Lean Interventions
2.3. Main Outcomes
3. Materials and Methods
3.1. Search Strategy
3.2. Selection of Studies
3.3. Data Extraction, Synthesis, and Risk of Bias
4. Results
5. Discussion
5.1. Effects of LH and DT Interventions on Healthcare Services
5.2. Digital Technologies Supporting Lean Healthcare Interventions
5.3. Settings and Challenges
5.4. Study Limitations
6. Conclusions
7. Future Research
7.1. Applications of Sensing–Communication Technologies in LH Interventions
7.2. Electronic Kanbans to Improve the Management of Patient Transportation
7.3. Virtual Reality Enabling Lean Layout Studies
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Process | Criteria | Description |
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Search strategy | Data sources |
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Studies |
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Selection of studies | Participants |
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Intervention |
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Comparator |
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Outcomes |
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Study design |
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Exclusion criteria |
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Data extraction and synthesis | Review process Extracted data |
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Risk of bias | Tool |
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(Authors, Year) Country | Settings; Study Design; n; Time Frame | Intervention | Main Outcomes | Summary of Findings |
---|---|---|---|---|
(Wongkrajang, 2020) [68] Thailand | Laboratory; case study, pre–post; n = 30,180; 3 mo | Lean and automation | 90th-percentile TAT | Reduced from 60 min to 50 min (p = 0.01) |
(Ankrum, 2019) [69] USA | pediatric facility; case study, pre–post; n = 47 room turnovers; 60 days | Lean, robotics, and electronic medical records | Median time between room breakdown to cleaning start time | Reduced from 10 min to 3 min (p = 0.004) |
(Recht, 2019) [90] USA | MRI; case study, pre–post; n1 = 5461 and n2 = 9221; 6 mo | Lean and automation of MRI (software tool) | Mean TAT (patients ready for scanning) | Reduced from 328 min to 132 min (p < 0.001) |
Mean TAT (all patients assessed) | Reduced from 537 min to 272 min (p < 0.001) | |||
(Shilpasree, 2019) [111] India | Clinical laboratory; pre–post; n = 3344; 2 mo | Lean, Six Sigma, automation and computerization | TAT | Reduced from 110 min to 78.7 min (p < 0.001) |
(Jensen, 2019) [113] USA | Laboratory; pre–post; n = 21,639; 20 mo; 4 mo follow up | Lean and automation (automated chemistry line and barcoding) | Specimen TAT | TnT: reduced from 56.64 min to 53.68 min (p < 0.001) |
K+: reduced from 40.88 min to 39.82 min (p < 0.001) | ||||
CMP-Alb: reduced from 43.44 min to 40.51 min (p < 0.001) | ||||
(Brunsman, 2018) [33] USA | Inpatient pharmacy; cohort study; n = 102; 15 mo | Lean and automation of dispensing cabinet | Median overall TAT from CMS-approved antibiotic order entry to medication administration | Reduced from 120 min to 80 min (p = 0.014) |
(Bhat, 2016) [114] India | Medical record department; case study; n = 100; 2 mo | Lean, Six Sigma and simulation | TAT of medical record preparation | Reduced from 19 min to 8 min |
(Thureson, 2015) [112] Sweden | Histopathology lab; pre–post; n = 46,675; 27 mo | Lean and automatic embedding console | Median TAT for patients with breast cancer | Reduced from 25 days to 15.5 days (p < 0.001) |
(Sanders, 2015) [115] USA | ED, hematology lab, and chemistry lab; pre–post | Lean, Six Sigma, ED tracking boards, electronic orders, and EHR | Median TAT for ED specimens of complete blood count analysis | Reduced from 15 min to 11 min |
(Wannemuehler, 2015) [116] USA | OR; pre–post; n = 644; 10 mo | Lean, Six Sigma and electronic tracking system | Median assembly times (instrument set) | Reduced from 8.4 min to 4.7 min (p < 0.001) |
Mean Mayo setup times | Reduced from 97.6 s to 76.1 s (p < 0.001) | |||
(White, 2014) [117] USA | ED; prospective controlled; pre–post; n = 59,687; 17 mo | Lean, Six Sigma, QT, TOC, and electronic patient tracking system | Median exam room time | The intervention group reduced by 34 min from 90 to 56 min (p < 0.001). The control group increased from 28 min to 36 min (p < 0.001). |
(Nelson-Peterson, 2007) [118] USA | General hospital; time-series; pre–post; n = 8; 5 mo | Lean and simulation | Registered nurse lead time | Reduced from 240 min to 126 min |
Setup time (minutes for one cycle of care) | Reduced from 20 min to 3 min |
(Authors, Year) Country | Settings; Study Design; n; Time Frame | Intervention | Main Outcomes | Summary of Findings |
---|---|---|---|---|
(Tsai et al., 2021) [120] Taiwan | Operating room; case study, pre–post; n = 2964; 24 mo | Lean, Six Sigma, electronic tracking system (electronic tags, registration | Mean LOS | Orthopedic surgery Reduced from 3.31 days to 1.57 days |
Mean LOS | Colon and rectal surgery Reduced from 2.49 days to 1.16 days | |||
APP, QR-codes, perioperative flow system, and HIS | Mean LOS | Urology surgery Reduced from 3.31 days to 1.57 days | ||
Mean LOS | Otorhinolaryngology surgery Reduced from 2.49 days to 1.16 days | |||
(Brunsman, 2018) [33] USA | Inpatient pharmacy; cohort study; n = 102; 15 mo | Lean and automation of dispensing cabinet | Median LOS | Reduced from 22.9 days to 13.2 days (p = 0.049) |
(Rutman, 2015) [121] USA | ED; pre–post; n = 98; 7 mo | Lean, simulation, and EMR | Mean LOS in ED | Reduced by 30 min |
(Beck, 2015) [108] USA | Inpatient pediatric service; pre–post; n = 3509; 12 mo | Lean, Six Sigma and tele-tracking systems | Mean LOS | Non-significant change, from 3.1 days to 3.0 days (p = 0.864) |
(Lee, 2015) [20] USA | Emergency care center; n = 18,726; 9 mo | Process mapping, machine learning, simulation, and optimization | Overall LOS | Reduced from 10.59 h to 7.14 h |
(Lo, 2015) [107] USA | Pediatric ED; pre–post; 7 mo | Lean, real-time voice recognition system, simulation, electronic charting, and EHR | Ambulatory patients’ LOS | Increased from 161 min to 168 min |
Inpatients’ LOS | No change (270 min) | |||
(Tejedor-Panchon, 2014) [122] Spain | ED; Quasi-experimental pre–post study; n = 256,628; 36 mo | Lean, simulation, and digital technology in X-ray | Mean LOS in ED (time spent in the examination area) | NUC reduced from 80.4 min to 61.6 min (p < 0.001); TC reduced from 137.8 min to 123.8 min (p < 0.05); MSC reduced from 219.7 min to 209.3 min (p = 0.108) |
(White, 2014) [117] USA | ED; prospective controlled, pre–post study; n = 59,687; 17 mo | Lean, Six Sigma, QT, and TOC; electronic patient tracking system | Median LOS for ambulatory patients | Intervention group reduced from 158 min to 143 min (p < 0.001) Non-significant change in the control group, from 265 min to 267 min (p = 0.69) |
(Furterer, 2014h [119] USA | ED; case study; 7 mo | Lean, Six Sigma, automation, electronic ED bed board, EMR | Mean LOS (all patients) | Reduced from 6.9 h to 4.7 h (p < 0.001) |
Mean LOS for inpatients | Reduced from 8.7 h to 6.1 h | |||
Mean LOS for ambulatory patients | Reduced from 5.8 h to 4.1 h | |||
(Burkitt, 2009) [35] USA | Department of surgery; cohort study; n = 1779; 48 mo | TPS, automatic control of antibiotics after surgery, and computerized medical record | Median LOS | Non-significant change (p = 0.90) |
(Eller, 2009) [123] USA | ED; pre–post; 25 mo | Lean, patient track, and electronic documentation system | Mean LOS for no RAD patients | Reduced 45 min |
Mean LOS for RAD patients | Reduced 208 min |
(Authors, Year) Country | Settings; Study Design; n; Time Frame | Intervention | Main Outcomes | Summary of Findings |
---|---|---|---|---|
(Ortiz-Barrios, 2020) [110] Colombia | ED; case study; n = 16,741; 15 mo | Lean, simulation and virtual modeling | Mean waiting time | Reduced from 201.6 min to 103.1 min |
(Baril, 2016) [109] Canada | Hematology–oncology clinic; case study; 10 mo, 2 mo of follow up | Lean, simulation, and business game virtual environment | Mean patient waiting time before treatment | Reduced from 61 min to 16 min |
(Rutman, 2015) [121] USA | ED; pre–post; n = 98; 7 mo | Lean, simulation, and electronic medical records | Median time to see a provider | Reduced from 43 min to 7 min |
Patients seen within 30 min | Increased from 33% to 93% | |||
(Rico, 2015) [124] USA | ED; pre–post; n = 50; 1 mo | Lean and automated infusion system | Mean waiting time for FDG Infusion | Reduced from 11.3 min to 6.4 min (p < 0.01) |
(Tejedor-Panchon) [122] Spain | ED; quasi-experimental pre–post study; n = 256,628; 36 mo | Lean, simulation, and digital technology in X-ray | Mean waiting time to see a physician | Reduced from 58.0 min to 49.1 min (p < 0.001) |
(Furterer, 2014) [119] USA | ED; case study; 7 mo | Lean, Six Sigma, automation, electronic ED bed board, and EMR | Time from door to doctor | Reduced from 100 min to 27 min |
(Authors, Year) Country | Settings; Study Design; n; Time Frame | Intervention | Main Outcomes | Summary of Findings |
---|---|---|---|---|
(Amati et al., 2022) [126] Switzerland | Operating room; case study, pre–post; 9 mo | Lean and simulation | Mean surgery changeover time (skin to skin) | Gynecological surgery Reduced from 58 min to 41 min |
Mean surgery changeover time (skin to skin) | General surgery Reduced from 63 min to 48 min | |||
(Ankrum, 2019) [69] USA | Pediatric facility; case study, pre–post; n = 47 room turnovers; 60 days | Lean, robotics, and electronic medical records | Median room turnover time | Reduced from 130 min to 65 min (p < 0.001) |
(Garza-Reyes, 2019) [125] Mexico | Ambulance service; case study; n = 850 ambulances; 1 mo | Lean and simulation, internet-based technologies, and GPS tracking devices | Average ambulance cycle time | Reduced from 124.9 min to 75.8 min |
(Brunsman, 2018) [33] USA | Inpatient pharmacy, cohort study; n = 102; 15 mo | Lean and automation of dispensing cabinet | Median time from order to medication verification | Increased from 5.5 min to 10.5 min (p = 0.11) |
(Bender, 2015) [93] USA | Operating room; pre–post; n = 25,903; 36 mo | Lean, Six Sigma, and robots | Mean turnover time | Non-significant change from 43 min to 44 min |
(Authors, Year) Country | Settings; Study Design; n; Time Frame | Intervention | Main Outcomes | Summary of Findings |
---|---|---|---|---|
(Lee, 2015) [20] USA | Emergency care center; n = 18,726; 9 mo | Process mapping, machine learning, simulation, and optimization | Percentage of patients LWBS | Reduced by 30% |
(Tejedor-Panchon, 2014) [122] Spain | ED; quasi-experimental pre–post study; n = 256,628; 36 mo | Lean, simulation, and digital technology in X-ray | Percentage of patients LWBS | Reduced from 2.8% to 2.0% (p < 0.001) |
(Furterer, 2014) [119] USA | ED; case study; 7 mo | Lean, Six Sigma, electronic ED bed board, electronic medical record, and automation | Percentage of patients LWBS | Reduced from 6.5% to 0.34 % |
(Eller, 2009) [123] USA | ED; pre–post; 25 mo | Lean, patient track, and electronic documentation system | Percentage of patients LWBS | Reduced 28% |
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Tlapa, D.; Tortorella, G.; Fogliatto, F.; Kumar, M.; Mac Cawley, A.; Vassolo, R.; Enberg, L.; Baez-Lopez, Y. Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review. Int. J. Environ. Res. Public Health 2022, 19, 9018. https://doi.org/10.3390/ijerph19159018
Tlapa D, Tortorella G, Fogliatto F, Kumar M, Mac Cawley A, Vassolo R, Enberg L, Baez-Lopez Y. Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review. International Journal of Environmental Research and Public Health. 2022; 19(15):9018. https://doi.org/10.3390/ijerph19159018
Chicago/Turabian StyleTlapa, Diego, Guilherme Tortorella, Flavio Fogliatto, Maneesh Kumar, Alejandro Mac Cawley, Roberto Vassolo, Luis Enberg, and Yolanda Baez-Lopez. 2022. "Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review" International Journal of Environmental Research and Public Health 19, no. 15: 9018. https://doi.org/10.3390/ijerph19159018