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Using Data for QualityImprovementUsing Data for QualityImprovementTRISASI LESTARI - 2017
Using Data for QualityImprovementUsing Data for QualityImprovement
Puskesmas mana diYogyakarta yang
pelayanankesehatannya paling
bermutu?
Puskesmas mana diYogyakarta yang
pelayanankesehatannya paling
bermutu?
Puskesmas mana diYogyakarta yang
pelayanankesehatannya paling
bermutu?
Puskesmas mana diYogyakarta yang
pelayanankesehatannya paling
bermutu?
Rumah Sakit mana yangpaling baik untuk
penanganan pasienDemam Berdarah?
Rumah Sakit mana yangpaling baik untuk
penanganan pasienDemam Berdarah?
Rumah Sakit mana yangpaling baik untuk
penanganan pasienDemam Berdarah?
Rumah Sakit mana yangpaling baik untuk
penanganan pasienDemam Berdarah?
Spesialis Bedah manayang operasinya palingaman dan outcomenya
baik?
Spesialis Bedah manayang operasinya palingaman dan outcomenya
baik?
Spesialis Bedah manayang operasinya palingaman dan outcomenya
baik?
Spesialis Bedah manayang operasinya palingaman dan outcomenya
baik?
USNEWSRANKING
2016-2017
USNEWSRANKING
2016-2017
http://health.usnews.com/best-hospitals/rankings
PAST FOCUS
CURRENT FOCUSCURRENT FOCUS
http://www.who.int/healthinfo/indicators/2015/en/
Pertanyaan 2: Bagaimana kita tahu bahwaperubahan yang terjadi adalah suatuperbaikan?
Pertanyaan 2: Bagaimana kita tahu bahwaperubahan yang terjadi adalah suatuperbaikan?
Sulitnya mengukur mutu
Makan waktu, menambah pekerjaanMakan waktu, menambah pekerjaan
Harus memastikan akurasi data dan konsistensi metode pengambilan dataHarus memastikan akurasi data dan konsistensi metode pengambilan data
Terlalu banyak indikator, tapi bukan indikator yang tepatTerlalu banyak indikator, tapi bukan indikator yang tepatTerlalu banyak indikator, tapi bukan indikator yang tepatTerlalu banyak indikator, tapi bukan indikator yang tepat
Indikator terima jadi, tanpa ada proses diskusiIndikator terima jadi, tanpa ada proses diskusi
Bagaimana menggunakan data yg sudah dikumpulkanBagaimana menggunakan data yg sudah dikumpulkan
Pengumpulan data manual atau otomatisPengumpulan data manual atau otomatis
Hasil analisis tidak sesuai dengan pendapat manajemenHasil analisis tidak sesuai dengan pendapat manajemen
Sulitnya mengukur mutu
Makan waktu, menambah pekerjaan
Harus memastikan akurasi data dan konsistensi metode pengambilan dataHarus memastikan akurasi data dan konsistensi metode pengambilan data
Terlalu banyak indikator, tapi bukan indikator yang tepatTerlalu banyak indikator, tapi bukan indikator yang tepatTerlalu banyak indikator, tapi bukan indikator yang tepatTerlalu banyak indikator, tapi bukan indikator yang tepat
Indikator terima jadi, tanpa ada proses diskusiIndikator terima jadi, tanpa ada proses diskusi
Bagaimana menggunakan data yg sudah dikumpulkanBagaimana menggunakan data yg sudah dikumpulkan
Pengumpulan data manual atau otomatisPengumpulan data manual atau otomatis
Hasil analisis tidak sesuai dengan pendapat manajemenHasil analisis tidak sesuai dengan pendapat manajemen
Manfaat Pengumpulan DataMembantu mengidentifikasi masalah yang sebenarnya
Membantu pengambilan keputusan
Meningkatkan kepercayaan diri manajerMeningkatkan kepercayaan diri manajer
Menjadi petunjuk apa yang sedang terjadi : karakteristik masalah, kapanterjadinya, pola dan trend
Menunjukkan peluang perbaikan mutu
Menunjukkan seberapa jauh proses untuk mencapai target
Manfaat Pengumpulan DataMembantu mengidentifikasi masalah yang sebenarnya
Meningkatkan kepercayaan diri manajerMeningkatkan kepercayaan diri manajer
Menjadi petunjuk apa yang sedang terjadi : karakteristik masalah, kapanterjadinya, pola dan trend
Menunjukkan peluang perbaikan mutu
Menunjukkan seberapa jauh proses untuk mencapai target
Manfaat Pengumpulan Data (lanjutan)
Sebagai pembanding terhadap standar
Membantu tim fokus dan memilih prioritas masalah yang harus ditangani
Membantu tim ‘menjual’ ide perbaikan mutu pada manajemen/direksiMembantu tim ‘menjual’ ide perbaikan mutu pada manajemen/direksi
Membantu memahami hubungan antar bagian
Menghindari tim menyelesaikan masalah hasil dugaan seseorang saja.
Membantu tim mengidentifikasi apakah sudah terjadi perubahan kepada perbaikan atau belum
Manfaat Pengumpulan Data (lanjutan)
Membantu tim fokus dan memilih prioritas masalah yang harus ditangani
Membantu tim ‘menjual’ ide perbaikan mutu pada manajemen/direksiMembantu tim ‘menjual’ ide perbaikan mutu pada manajemen/direksi
Membantu memahami hubungan antar bagian
Menghindari tim menyelesaikan masalah hasil dugaan seseorang saja.
Membantu tim mengidentifikasi apakah sudah terjadi perubahan kepada perbaikan atau belum
“The more effort you put into understandingand utilizing data, the more you will berewarded in terms of solving the right
problem in the right way”.(The Victorian Quality Council Safety and Quality in Health)
“The more effort you put into understandingand utilizing data, the more you will berewarded in terms of solving the right
problem in the right way”.(The Victorian Quality Council Safety and Quality in Health)
“The more effort you put into understandingand utilizing data, the more you will berewarded in terms of solving the right
problem in the right way”.(The Victorian Quality Council Safety and Quality in Health)
“The more effort you put into understandingand utilizing data, the more you will berewarded in terms of solving the right
problem in the right way”.(The Victorian Quality Council Safety and Quality in Health)
Quality improvement bisa reactive dan proactive.Reaktif terhadap masalah yang ditemukan dalam data/laporan rutin.Proaktif dengan menganalisis data untuk mencari celah untuk perbaikan.
Quality improvement bisa reactive dan proactive.Reaktif terhadap masalah yang ditemukan dalam data/laporan rutin.Proaktif dengan menganalisis data untuk mencari celah untuk perbaikan.
Sumber data?
DataInternal
DataInternal
DataInternal
DataInternal
DataEksternal
DataEksternal
DataEksternal
DataEksternal
Jenis data
AdministrativeAdministrative
• Demografi• Statistik
pelayanan• Data finansial• Readmission• Length of stay
• Demografi• Statistik
pelayanan• Data finansial• Readmission• Length of stay
ClinicalClinical
• Adverse event• Risk factor• Mortalitas• Morbiditas• Infection rates
• Adverse event• Risk factor• Mortalitas• Morbiditas• Infection rates
Data
Bangsal
HRD
Gizi
Data
Rawat Jalan
Keuangan
HRD Data
Farmasi
PendaftaranData Pendaftaran
IGD
Rawat Jalan
Pengumpulan Data
SamplingSampling
• Populasi• Sample size• Sampling
teknik• Bias
Data entryData entry
• checking• Cleaning
• Populasi• Sample size• Sampling
teknik• Bias
• checking• Cleaning
Pengumpulan Data
Data entryData entry
• checking• Cleaning
Storing andmanaging
Storing andmanaging
• Spreadsheet• Database
program• Statistical
program
• checking• Cleaning
• Spreadsheet• Database
program• Statistical
program
Bias Sampling
Good Data
ReliableReliableReliableReliable
UnbiasedUnbiased
ValidValidValidValid
UnbiasedUnbiased
“If I had to reduce mymessage for
management to just afew words, I’d say it all
had to do withreducing variation”.
(W.E. Deming)
“If I had to reduce mymessage for
management to just afew words, I’d say it all
had to do withreducing variation”.
(W.E. Deming)
Principles of variation1. No two things are exactly alike.
2. Variation in a product or process can be measured
3. Things vary according to a definite pattern.
4. Whenever things of the same kind are measured, a large group of themeasurements will tend to cluster around the middle
5. It's possible to determine the shape of the distribution curve formeasurements obtained from any process.
6. Variations due to assignable causes tend to distort the normal distributioncurve
1. No two things are exactly alike.
2. Variation in a product or process can be measured
3. Things vary according to a definite pattern.
4. Whenever things of the same kind are measured, a large group of themeasurements will tend to cluster around the middle
5. It's possible to determine the shape of the distribution curve formeasurements obtained from any process.
6. Variations due to assignable causes tend to distort the normal distributioncurve
Principles of variation1. No two things are exactly alike.
2. Variation in a product or process can be measured
3. Things vary according to a definite pattern.
4. Whenever things of the same kind are measured, a large group of themeasurements will tend to cluster around the middle
5. It's possible to determine the shape of the distribution curve formeasurements obtained from any process.
6. Variations due to assignable causes tend to distort the normal distributioncurve
1. No two things are exactly alike.
2. Variation in a product or process can be measured
3. Things vary according to a definite pattern.
4. Whenever things of the same kind are measured, a large group of themeasurements will tend to cluster around the middle
5. It's possible to determine the shape of the distribution curve formeasurements obtained from any process.
6. Variations due to assignable causes tend to distort the normal distributioncurve
Cause of variation
Insidental
Cause of variation
Sistemik
Type of variation
Common Source of VariationCommon Source of Variation
Basic Data Presentation
1. Deskriptif Statistik
Basic Data Presentation
2. Percentage changePrevalence of pressure ulcers before and after intervention
2. Percentage changePrevalence of pressure ulcers before and after intervention
3. Measures of centre3. Measures of centre
Satisfaction survey (response rate)Satisfaction survey (response rate)
Satisfaction Survey ResultsSatisfaction Survey Results
4. Pie Chart
5. Using bar for comparison5. Using bar for comparison
6. Box Plots
2. Histogram
HistogramShows relative frequenciesProduced from grouped dataDetermine the number of classes◦ 2a−1 < n ≤ 2a
◦ n=100, 26 < n ≤ 27 = 7 classes
Get insight into the shape of of the distribution of population
Shows relative frequenciesProduced from grouped dataDetermine the number of classes◦ 2a−1 < n ≤ 2a
◦ n=100, 26 < n ≤ 27 = 7 classes
Get insight into the shape of of the distribution of population
Shows relative frequenciesProduced from grouped dataDetermine the number of classes◦ 2a−1 < n ≤ 2a
◦ n=100, 26 < n ≤ 27 = 7 classes
Get insight into the shape of of the distribution of population
Shows relative frequenciesProduced from grouped dataDetermine the number of classes◦ 2a−1 < n ≤ 2a
◦ n=100, 26 < n ≤ 27 = 7 classes
Get insight into the shape of of the distribution of population
3. Pareto Chart
ParetoChart
Show
loss
/N
egat
ive
outc
ome
Vital Few
Show
loss
/N
egat
ive
outc
ome
ParetoChart
Trivial many
Control ChartControl ChartControl ChartControl Chart
BasicControl
Chart
BasicControl
Chart
Control chart representingnosocomial infections in the EDControl chart representingnosocomial infections in the ED
Performance improvement DataChest Pain in EmergencyDepartment. Slide courtesy of IHI
Average CABG MortalityBefore and After implementation of a new Protocol(Slide courtesy of IHI)
Average CABG MortalityBefore and After implementation of a new Protocol(Slide courtesy of IHI)
A second look at the Data
2%
A second look at the Data
7%
2%
Angka rata-rata tidakmenggambarkan situasisesungguhnya
Hasil
peng
ukur
anHa
silpe
nguk
uran
Waktu
Angka rata-rata tidakmenggambarkan situasisesungguhnya
Χ (CL)
Waktu
Χ (CL)
Bagaimana variasi dalam sebuahsistem dengan berjalannya waktu?
Shewhart 1920: variasi terkontrol dantidak terkontrol (special cause)
Bagaimana variasi dalam sebuahsistem dengan berjalannya waktu?
Shewhart 1920: variasi terkontrol dantidak terkontrol (special cause)
Jenis VariasiTerkontrol (common cause)◦ Terkait dengan desain proses◦ Akibat proses regular, penyebab
natural, atau biasa.◦ Mempengaruhi semua outcome
proses◦ Hasilnya stabil◦ Bisa diprediksikan
Tidak terkontrol (special cause)◦ Bukan disebabkan karena desain
proses◦ Akibat proses ireguler atau tidak
alami◦ Mempengaruhi sebagian outcome
tapi tidak seluruhnya◦ Hasilnya tidak stabil◦ Tidak bisa diprediksikan
Terkontrol (common cause)◦ Terkait dengan desain proses◦ Akibat proses regular, penyebab
natural, atau biasa.◦ Mempengaruhi semua outcome
proses◦ Hasilnya stabil◦ Bisa diprediksikan
Tidak terkontrol (special cause)◦ Bukan disebabkan karena desain
proses◦ Akibat proses ireguler atau tidak
alami◦ Mempengaruhi sebagian outcome
tapi tidak seluruhnya◦ Hasilnya tidak stabil◦ Tidak bisa diprediksikan
Tidak terkontrol (special cause)◦ Bukan disebabkan karena desain
proses◦ Akibat proses ireguler atau tidak
alami◦ Mempengaruhi sebagian outcome
tapi tidak seluruhnya◦ Hasilnya tidak stabil◦ Tidak bisa diprediksikan
Tidak terkontrol (special cause)◦ Bukan disebabkan karena desain
proses◦ Akibat proses ireguler atau tidak
alami◦ Mempengaruhi sebagian outcome
tapi tidak seluruhnya◦ Hasilnya tidak stabil◦ Tidak bisa diprediksikan
Shewhart’s Control ChartHa
silpe
nguk
uran
Hasil
peng
ukur
an
Waktu
Biasanya diperlukan 15-20 data points
Shewhart’s Control Chart
Χ (CL)
UCLUpper Control Limit
Sigma Limit
Χ (CL)LCLLower Control Limit
Biasanya diperlukan 15-20 data points
Note: For sample size of <6 = LCL always 0Note: For sample size of <6 = LCL always 0
Average and Range(Xbar-R) ChartAverage and Range(Xbar-R) ChartAverage and Range(Xbar-R) ChartAverage and Range(Xbar-R) Chart
Characteristics of Xbar-R chart
1. It comprised of two charts used in tandem
2. It is used when you can collect measurements in subgroups of between twoand 10 observations.
3. The data is in time-order
4. The Xbar chart is used to evaluate consistency of process averages
5. It is efficient at detecting relatively large shifts (typically plus or minus 1.5 ? orlarger)
6. The R chart is used to evaluate the consistency of process variation.
7. Look at the R chart first; if the R chart is out of control, then the control limitson the Xbar chart are meaningless.
1. It comprised of two charts used in tandem
2. It is used when you can collect measurements in subgroups of between twoand 10 observations.
3. The data is in time-order
4. The Xbar chart is used to evaluate consistency of process averages
5. It is efficient at detecting relatively large shifts (typically plus or minus 1.5 ? orlarger)
6. The R chart is used to evaluate the consistency of process variation.
7. Look at the R chart first; if the R chart is out of control, then the control limitson the Xbar chart are meaningless.
Characteristics of Xbar-R chart
1. It comprised of two charts used in tandem
2. It is used when you can collect measurements in subgroups of between twoand 10 observations.
3. The data is in time-order
4. The Xbar chart is used to evaluate consistency of process averages
5. It is efficient at detecting relatively large shifts (typically plus or minus 1.5 ? orlarger)
6. The R chart is used to evaluate the consistency of process variation.
7. Look at the R chart first; if the R chart is out of control, then the control limitson the Xbar chart are meaningless.
1. It comprised of two charts used in tandem
2. It is used when you can collect measurements in subgroups of between twoand 10 observations.
3. The data is in time-order
4. The Xbar chart is used to evaluate consistency of process averages
5. It is efficient at detecting relatively large shifts (typically plus or minus 1.5 ? orlarger)
6. The R chart is used to evaluate the consistency of process variation.
7. Look at the R chart first; if the R chart is out of control, then the control limitson the Xbar chart are meaningless.
Ice Cream Shop
2 scoops = + 6 ounces (~170grams)
Control : weigh fivesamples every 30 minutes
Sum average range
Ice Cream Shop
2 scoops = + 6 ounces (~170grams)
Control : weigh fivesamples every 30 minutes
Sum average range
Rule: a point between UCL and LCL is a NORMAL VARIATION orcontrolled variation in the processRule: a point between UCL and LCL is a NORMAL VARIATION orcontrolled variation in the process
When the average isoutside the limitsthe process is out ofcontrol
Something hashappened, you maybe able to identifythe cause and youhave to correct it.
When the average isoutside the limitsthe process is out ofcontrol
Something hashappened, you maybe able to identifythe cause and youhave to correct it.
When the range isoutside the limitsthe process is out ofcontrol
R-chart is used toevaluate consistencyof the process
If R-chart is out ofcontrol the averagechart is meaningless
When the range isoutside the limitsthe process is out ofcontrol
R-chart is used toevaluate consistencyof the process
If R-chart is out ofcontrol the averagechart is meaningless
Determine control limit for RangeUCL=See table constanta control chartChoose D4 factor that corresponds tothe sample sizeUCL = D4 x RUCL=2.114 x 1.35 = 2.854LCL = 0 , sample size <6
UCL=See table constanta control chartChoose D4 factor that corresponds tothe sample sizeUCL = D4 x RUCL=2.114 x 1.35 = 2.854LCL = 0 , sample size <6
Determine control limit for Range
Decision chartfor workingwith range
SAMPLE SIZE=5UCL=See table constanta controlchartFind A2 factor that correspondsto the sample sizeUCL = X + (A2xR)UCL= 4.795 + (0.577x1.35)UCL= 4.795 + 0.779UCL= 5.574
LCL = X-(A2xR)LCL = 4.795-0.779LCL – 4.016
Determine controllimit for Average
SAMPLE SIZE=5UCL=See table constanta controlchartFind A2 factor that correspondsto the sample sizeUCL = X + (A2xR)UCL= 4.795 + (0.577x1.35)UCL= 4.795 + 0.779UCL= 5.574
LCL = X-(A2xR)LCL = 4.795-0.779LCL – 4.016
Decision chart for working with average
Once you have established the control limits and startusing them in regular operations, a different rule applies: Ifeven a single point, either range (R) or average (X), goesoutside a control limit, do not throw out the point. This is aclear indication that an assignable cause is present. Youmust find the assignable cause, and correct it.
Once you have established the control limits and startusing them in regular operations, a different rule applies: Ifeven a single point, either range (R) or average (X), goesoutside a control limit, do not throw out the point. This is aclear indication that an assignable cause is present. Youmust find the assignable cause, and correct it.
Decision chart for working with average
Once you have established the control limits and startusing them in regular operations, a different rule applies: Ifeven a single point, either range (R) or average (X), goesoutside a control limit, do not throw out the point. This is aclear indication that an assignable cause is present. Youmust find the assignable cause, and correct it.
Once you have established the control limits and startusing them in regular operations, a different rule applies: Ifeven a single point, either range (R) or average (X), goesoutside a control limit, do not throw out the point. This is aclear indication that an assignable cause is present. Youmust find the assignable cause, and correct it.
Median and Range(Xbar-R) ChartMedian and Range(Xbar-R) ChartMedian and Range(Xbar-R) ChartMedian and Range(Xbar-R) Chart
MEDIAN ANDRANGE CHART
It is a good chart to use when youknow that the process fordelivering or producing a service(1) follows a normal (bell-shaped) distribution, (2) is notvery often disturbed byassignable causes, and (3) can beeasily adjusted by the employee.If the process does not meetthese requirements, you shoulduse an average and range chart.
It is a good chart to use when youknow that the process fordelivering or producing a service(1) follows a normal (bell-shaped) distribution, (2) is notvery often disturbed byassignable causes, and (3) can beeasily adjusted by the employee.If the process does not meetthese requirements, you shoulduse an average and range chart.
I-MR ChartIndividual and Moving Range ChartI-MR ChartIndividual and Moving Range ChartI-MR ChartIndividual and Moving Range ChartI-MR ChartIndividual and Moving Range Chart
• Use if you are only able to take one reading during a time period.
• I chart
• one data point is collected at each point in time
• monitor the process average, process variation and time
• Is used to detects trend and shifts in the data
• The Individual data must be time-ordered
• MR chart
• is the difference between consecutive observations
• It shows short term variability in the data
• It is used to assess stability of the process
• Use if you are only able to take one reading during a time period.
• I chart
• one data point is collected at each point in time
• monitor the process average, process variation and time
• Is used to detects trend and shifts in the data
• The Individual data must be time-ordered
• MR chart
• is the difference between consecutive observations
• It shows short term variability in the data
• It is used to assess stability of the process
• Use if you are only able to take one reading during a time period.
• I chart
• one data point is collected at each point in time
• monitor the process average, process variation and time
• Is used to detects trend and shifts in the data
• The Individual data must be time-ordered
• MR chart
• is the difference between consecutive observations
• It shows short term variability in the data
• It is used to assess stability of the process
• Use if you are only able to take one reading during a time period.
• I chart
• one data point is collected at each point in time
• monitor the process average, process variation and time
• Is used to detects trend and shifts in the data
• The Individual data must be time-ordered
• MR chart
• is the difference between consecutive observations
• It shows short term variability in the data
• It is used to assess stability of the process
UCL RANGESee table constanta control chartFind A2 factor that corresponds to thesample sizeNumber of Sample = 2UCLR= (D4xRa)UCLR= 3.267 x RaRa= total R/number of sampleRa= 38.8 / 24 =1.616UCLR= 3.267 x 1.616UCLR= 5.28LCL = 0 (sample <6)
UCL AVERAGEX= total sample/25 = 701.5/25=28.06UCLx =X+(2.66xR)UCLx = 28.06+(2.66x1.616)=32.35LCLx = 28.06-(2.66x1.616)=23.76
UCL RANGESee table constanta control chartFind A2 factor that corresponds to thesample sizeNumber of Sample = 2UCLR= (D4xRa)UCLR= 3.267 x RaRa= total R/number of sampleRa= 38.8 / 24 =1.616UCLR= 3.267 x 1.616UCLR= 5.28LCL = 0 (sample <6)
UCL AVERAGEX= total sample/25 = 701.5/25=28.06UCLx =X+(2.66xR)UCLx = 28.06+(2.66x1.616)=32.35LCLx = 28.06-(2.66x1.616)=23.76
I-MR Chart
Zone B
Zone A
Pembagian Zona dalam ControlChart
Zone A
Zone B
Zone C
Zone C Χ (CL)
UCLUpper Control Limit+2 SL
+3 SL
Pembagian Zona dalam ControlChart
Χ (CL)LCLLower Control Limit
-3 SL
-2 SL
-1 SL
+1 SL
Aturan Control Chart untukmengidentifikasi adanya variasi
Rule 1: ada 1 point yang terletak di luar +/-3SL
Rule 2: ada 8 point berturut-turut yang terletak diatas ataudibawah center lineRule 2: ada 8 point berturut-turut yang terletak diatas ataudibawah center line
Rule 3: ada 6 atau lebih point yang terus naik/turun
Rule 4: ada 2 dari 3 point berturut-turut yang terletak di zonaA atau melewati zona A
Rule 5: ada 15 point berturut-turut yang terletak di zona Cpada kedua sisi
Aturan Control Chart untukmengidentifikasi adanya variasi
Rule 1: ada 1 point yang terletak di luar +/-3SL
Rule 2: ada 8 point berturut-turut yang terletak diatas ataudibawah center lineRule 2: ada 8 point berturut-turut yang terletak diatas ataudibawah center line
Rule 3: ada 6 atau lebih point yang terus naik/turun
Rule 4: ada 2 dari 3 point berturut-turut yang terletak di zonaA atau melewati zona A
Rule 5: ada 15 point berturut-turut yang terletak di zona Cpada kedua sisi
Variasi yang unik (specialcause) tidak selalu berarti
jelek, bisa juga menunjukkanperbaikan dan harus
dianalisis untuk membantupengambilan keputusan.
Variasi yang unik (specialcause) tidak selalu berarti
jelek, bisa juga menunjukkanperbaikan dan harus
dianalisis untuk membantupengambilan keputusan.
Variasi yang unik (specialcause) tidak selalu berarti
jelek, bisa juga menunjukkanperbaikan dan harus
dianalisis untuk membantupengambilan keputusan.
Variasi yang unik (specialcause) tidak selalu berarti
jelek, bisa juga menunjukkanperbaikan dan harus
dianalisis untuk membantupengambilan keputusan.
Time to surfactantadministration of premature infantsTime to surfactantadministration of premature infants
Jenis-jenis control chart
X bar and S X bar and R XmR
X-Bar, Rb, Rw CUSUM EWMA
StandardizedP C-chart
Jenis-jenis control chart
XmR Deviationfrom Nominal X-Bar, Rb, d
EWMA Np P-chart
U-chart Standardizedu
Bagaimana menilai variasi dalamproses perbaikan mutu?Bagaimana menilai variasi dalamproses perbaikan mutu?
Run ChartHa
silpe
nguk
uran
Plot the dots…
Hasil
peng
ukur
an
Waktu
X (Median)
Run adalah satu ataulebih data points padasalah satu sisi medianyang sama, tidaktermasuk data point yangterletak pada median.
Waktu
X (Median)
Non-random rules for run chartNon-random rules for run chart
“If I had to reducemy message formanagement to justa few words, I’d sayit all had to do withreducing variation”.(W.Edwards Deming)
“If I had to reducemy message formanagement to justa few words, I’d sayit all had to do withreducing variation”.(W.Edwards Deming)
“If I had to reducemy message formanagement to justa few words, I’d sayit all had to do withreducing variation”.(W.Edwards Deming)
“If I had to reducemy message formanagement to justa few words, I’d sayit all had to do withreducing variation”.(W.Edwards Deming)
Tugas1. Identifikasi Gap dalam pelayanan kesehatan dan tantangannya
2. Apa yang ingin anda ubah?
3. Jawab 3 pertanyaan Nolan model
4. Pilih intervensi yang ingin dilakukan (semakin spesifik semakin baik)
5. Buat rencana (Plan)
6. Pilih metode dan alat untuk implementasi perubahan
7. Pilih metode pengumpulan data untuk observasi
8. Pilih metode untuk penyajian data
Maksimal 3 halaman, font Times New Roman 12, spasi 1.5
1. Identifikasi Gap dalam pelayanan kesehatan dan tantangannya
2. Apa yang ingin anda ubah?
3. Jawab 3 pertanyaan Nolan model
4. Pilih intervensi yang ingin dilakukan (semakin spesifik semakin baik)
5. Buat rencana (Plan)
6. Pilih metode dan alat untuk implementasi perubahan
7. Pilih metode pengumpulan data untuk observasi
8. Pilih metode untuk penyajian data
Maksimal 3 halaman, font Times New Roman 12, spasi 1.5
1. Identifikasi Gap dalam pelayanan kesehatan dan tantangannya
2. Apa yang ingin anda ubah?
3. Jawab 3 pertanyaan Nolan model
4. Pilih intervensi yang ingin dilakukan (semakin spesifik semakin baik)
5. Buat rencana (Plan)
6. Pilih metode dan alat untuk implementasi perubahan
7. Pilih metode pengumpulan data untuk observasi
8. Pilih metode untuk penyajian data
Maksimal 3 halaman, font Times New Roman 12, spasi 1.5
1. Identifikasi Gap dalam pelayanan kesehatan dan tantangannya
2. Apa yang ingin anda ubah?
3. Jawab 3 pertanyaan Nolan model
4. Pilih intervensi yang ingin dilakukan (semakin spesifik semakin baik)
5. Buat rencana (Plan)
6. Pilih metode dan alat untuk implementasi perubahan
7. Pilih metode pengumpulan data untuk observasi
8. Pilih metode untuk penyajian data
Maksimal 3 halaman, font Times New Roman 12, spasi 1.5