PatientFollow Project: Difference between revisions

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=== Discussion ===

* Tina has taken a basic look at the distribution of these numbers and emailed Julie and Trish for feedback. Ttenbergen 17:31, 2019 August 1 (CDT)

** Julie did additional analysis by looking at the distribution of the last two digits numbers from last 5 years 2014 to 2018 as follows: 1) all sites together, 2) each site separately 3) each year from all sites separately and 4) each site and year - the distributions showed some peaks in some numbers. She grouped the last two digits numbers into a) 10 subgroups (e.g. 0-9,10-19,20-29, …, 90-99 ) and b) 20 subgroups (e.g. 0-4, 5-9, 10-14, 15-19, …, 95-99) and their distributions show uniformly across subgroups. Each of the 10 subgroups showed counts close to 10% while each of the 20 subgroups showed counts close to 5%. The histograms are in X:\Julie\CC Projects\Julie\~~Chart~~. The results support the viability of using the last two digits of the chart number in allocating patients among the data collectors.

** Julie did additional analysis by looking at the distribution of the last two digits numbers from last 5 years 2014 to 2018 as follows: 1) all sites together, 2) each site separately 3) each year from all sites separately and 4) each site and year - the distributions showed some peaks in some numbers. She grouped the last two digits numbers into a) 10 subgroups (e.g. 0-9,10-19,20-29, …, 90-99 ) and b) 20 subgroups (e.g. 0-4, 5-9, 10-14, 15-19, …, 95-99) and their distributions show uniformly across subgroups. Each of the 10 subgroups showed counts close to 10% while each of the 20 subgroups showed counts close to 5%. The histograms are in X:\Julie\CC Projects\Julie\ChartNumberDistribution. The results support the viability of using the last two digits of the chart number in allocating patients among the data collectors.

* I think this is a good starting strategy to allocate patients among the data collectors proportionately in each site. The one thing which is unclear yet to me is how to make sure we will '''not miss''' any patient in a given ward(Med/ICU) using this strategy. Who will be responsible to check or monitor that '''all''' patients who were admitted in a given ward are already entered in '''all''' laptops? How long will the DC run after that patient who left the ward but still in the hospital? How easy to catch those transfers from other service who haven't been in ICU/Med and now have been admitted to ICU/Med service? For now, these are my thoughts. --[[User:JMojica|JMojica]] 15:32, 2019 August 6 (CDT)

* We discussed whether different [[LOS]] will cause problems with this distribution of patients. We would expect LOS to be equally distributed across Chart Numbers; if it is we should be able to ignore it in distributing patients, since the “average” patient with an “average” chart number would have an “average” LOS.

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 === Discussion ===
 * Tina has taken a basic look at the distribution of these numbers and emailed Julie and Trish for feedback. Ttenbergen 17:31, 2019 August 1 (CDT)
-** Julie did additional analysis by looking at the distribution of the last two digits numbers from last 5 years 2014 to 2018 as follows: 1) all sites together, 2) each site separately 3) each year from all sites separately and 4) each site and year - the distributions showed some peaks in some numbers.  She grouped the last two digits numbers  into a) 10 subgroups (e.g. 0-9,10-19,20-29, …, 90-99 ) and b) 20 subgroups (e.g. 0-4, 5-9, 10-14, 15-19, …, 95-99)  and their distributions show uniformly across subgroups. Each of the 10 subgroups showed counts close to 10% while each of the 20 subgroups showed counts close to 5%. The histograms are in X:\Julie\CC Projects\Julie\Chart.  The results support the viability of using the last two digits of the chart number in allocating patients among the data collectors.
+** Julie did additional analysis by looking at the distribution of the last two digits numbers from last 5 years 2014 to 2018 as follows: 1) all sites together, 2) each site separately 3) each year from all sites separately and 4) each site and year - the distributions showed some peaks in some numbers.  She grouped the last two digits numbers  into a) 10 subgroups (e.g. 0-9,10-19,20-29, …, 90-99 ) and b) 20 subgroups (e.g. 0-4, 5-9, 10-14, 15-19, …, 95-99)  and their distributions show uniformly across subgroups. Each of the 10 subgroups showed counts close to 10% while each of the 20 subgroups showed counts close to 5%. The histograms are in X:\Julie\CC Projects\Julie\ChartNumberDistribution.  The results support the viability of using the last two digits of the chart number in allocating patients among the data collectors.
 * I think this is a good starting strategy to allocate patients among the data collectors proportionately in each site.  The one thing which is unclear yet to me is how to make sure we will '''not miss''' any patient in a given ward(Med/ICU) using this strategy. Who will be responsible to check or monitor that '''all''' patients who were admitted in a given ward are already entered in '''all''' laptops? How long will the DC run after that patient who left the ward but still in the hospital? How easy to  catch those transfers from other service who haven't been in ICU/Med and now have been admitted to ICU/Med service?  For now, these are my thoughts. --[[User:JMojica|JMojica]] 15:32, 2019 August 6 (CDT)
 * We discussed whether different [[LOS]] will cause problems with this distribution of patients. We would expect LOS to be equally distributed across Chart Numbers; if it is we should be able to ignore it in distributing patients, since the “average” patient with an “average” chart number would have an “average” LOS.