PatientFollow Project: Difference between revisions

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Our database collects patient ward stays, which means the data of a patient may be processed by several collectors during the admission. This leads to extra, wasted work of different collectors familiarizing themselves with the same patient. We are looking at ways to reduce this waste.
This page describes how collection of incoming patients is split across data collectors


Specifically, we are looking into having a single collector/laptop follow a patient for their whole admission, and how our processes would need to change to accommodate this, and what extra tools we might need.  
== Identifying admissions / Starting collection ==
Patients are assigned to laptops by the last two digits of their [[Chart number]]. [[Cognos2 Service Starter]] automatically filter them, just follow [[Using Cognos2 to keep track of patients]]. Special considerations apply to [[John or Jane Doe patient#PatientFollow Project considerations|John or Jane Doe patient]]s.
 
== Entering into the laptop ==
The initial ward would need to be entered as usual. For stays on subsequent wards, [[Cognos2 Service Starter]] and [[Patient Viewer Tab Cognos ADT2]] can be used to create another line in the [[Boarding Loc]] and [[Service tmp entry]].
 
Data would go into one profile unless a patient leaves the service. For example, if a pt starts in medicine, then goes to ICU, and then comes back to medicine, then coming back to medicine would mean starting a new profile.


== Pilot ==
== Fallback process when Cognos data is unavailable ==
* one or two collectors at HSC will do collection like this for specific patients in coordination with main office to better understand how this would work
See [[Cognos downtime procedure]]


=== prerequisites for pilot participation ===
== Medical Records requests ==
For a collector to be able to follow to another ward and enter that as a  [[Service/Location]] we need to add the additional wards to the laptop's [[S locations allowed collection table]] entries in [[CCMDB.accdb]], and assign a reasonable order in the dropdown for the locations. This has been done for:
Shelf split based on [[Laptop identifier]], see [[HSC Medical Records requests]] for details.
* [[HSC H4]] laptop


== Identifying admissions / Starting collection ==
== Actual chart number split per site and per laptop ==
Currently patients are assigned to collectors/laptops based on where they are admitted. To change to the new system, we would need to identify patients who enter a given site and then assign them to the collector pool equitably. We are planning on a process based on the last two digits of the chart number.  
The split is automatically reflected in [[Cognos2 Service Starter]], no additional filtering needed. The corresponding data is stored in [[S PatientFollow distribution table]].  


=== What would be the process for picking up new patients ===
=== Viewing the numbers assigned to a given laptop ===
{{Discuss|
The assignment is a matter of laptop, chart number ending and the date at which point a specific distribution started. We don't want to store it here on the wiki because it is kind of messy and hard to keep updated. Use [["Show PatientFollow allocation" button]] to see which numbers are assigned to the laptop you are working on during which timeframe.
* Details to follow, but it would likely involve [[Exporting and sorting an admission list from EPR]].


* The list would need to be exported on a regular basis and then made available to collectors. How would this best be done?
=== Assignment changes ===
See [[Processes around changing a PatientFollow assignment]]


* There was talk about EPR lists to help collectors keep track of their patients. If we use that method then how will we handle it when there needs to be coverage?
=== Exception: HSC_IICU ===
See [[HSC_IICU_Collection_Guide#Workload_Sharing_for_HSC_IICU]]


* How do we make sure no pts are missed? 
== Follow between medicine/critical care or just within one program ==
For now we are testing this just following patients within the same program, eg if a patient were admitted to a medicine ward, then ICU, and then back to the same medicine ward, then the medicine collector would get the two med admissions, and the CC collector would collect the ICU stay. This may change in the future but would require fairly significant changes to [[CCMDB.accdb Data Integrity Checks]] and other settings in [[CCMDB.accdb]].


* How do we make sure no pts are duplicated? }}
=== Programming that would need to be updated to be able to use a laptop across programs ===
* cross checks have been checked as part of previous project, should work
* Generupdate / query check_tmp_generate_allowed
* Hider
* Converter functions Hosp, Loc, Prog


=== If we split by chart number, how do we ensure no pts are missed? ===
{{Collapsable
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)
| always= old process and questions that were addressed
* The process of picking up patients would need to be very clear, and would need to change from what it is currently. We started this discussion with Val. They now get all of their pts off the EPR. That listing includes Chart numbers. So instead of looking at their ward, they can look at their chart number. The only thing is, we can’t sort that by “last two digits of chart number” to make it ''easy''. I hope we can still do better than that. I think it might be good to chat with someone like Laura or Lisa or someone from Med Records about what else we might be able to do with those lists – if we could export them we could filter them to laptops by last two digits.
| full=  
* Val showed us that she creates an EPR patient list that includes all of her current patients. This list enables the collector to more easily track patients throughout their stay, as you can have patients from multiple different locations on this one list. The only problem with this list is that it is specific to one collector's login, so if other collectors are cross covering (for vacation or other reasons), they would not have access to this master list. Michelle investigated whether it is possible to share patient lists between collectors and the EPR specialist informed her that it is ''not possible''.


=== What would be the actual chart number split per site and per collector ===
=== Old Process ===
We would essentially take the sum EFTs per program/site and consider them as 100%, and then assign the chart numbers based on that percentage. For example, if a site has 3 collectors that are each a .5EFT, each collector would get 33% of that site's new admissions, so collector A might get charts ending in 00-33, collector B 34-66, and collector C 67-99
Our database used to collect patient ward stays, which meant the data of a patient could be processed by several collectors during the admission. This lead to extra, wasted work of different collectors familiarizing themselves with the same patient. {{PAGENAME}} was set up to change to a system where one collector keeps following a patient.


As of Sept 2019 we are testing the split between medicine collectors at HSC.
Currently patients are assigned to collectors/laptops based on where they are admitted. To change to the new system, we would need to identify patients who enter a given site and then assign them to the collector pool equitably. We are planning on a process based on the last two digits of the chart number.  


{{Collapsable
=== If we split by chart number, how do we ensure no pts are missed or duplicated? ===
| always= The last two digits of chart numbers are evenly distributed and can be used for this.
* '''duplication''' - there could only be duplication if you enter a chart number that is someone else's; your [[Cognos2 Service Starter]] will only show your patients, so you would not duplicate someone else's, and any risk of duplicating your own is no higher than it was with the old process.  
| full= * 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)
* '''missing a patient''' - we have been testing the [[Cognos EPR Report]] to make sure patients are not missed from it; for PatientFollow we will simply filter that list, so if all patients were on it, they should still all be on the split list
** 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 similarity with few 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 showed 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:\CCMDB_Special_Projects\Project_PatientFollow_ChartNumberDistribution''.  The results support the viability of using the last two digits of the chart number in allocating patients among the data collectors.
** main office can run a check between Cognos Data and our data for the first few weeks to make sure all Cognos data is also in our data
** Julie also did the distribution of the '''first two digits''' numbers and  found out that the distribution was skewed to the right. Therefore, this cannot be used as a tool for allocating patients. The distribution is in ''X:\CCMDB_Special_Projects\Project_PatientFollow_ChartNumberDistribution'' .
* I think this is a good starting strategy to allocate patients among the data collectors proportionately in each site.}}


=== Would the LOS have any impact on this sharing plan? ===
=== Would the LOS have any impact on this sharing plan? ===
Line 47: Line 55:
| full= * 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.}}
| full= * 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.}}


== Entering into the laptop ==
=== [[EMIP]]s ===
The initial ward would need to be entered as usual. For stays on subsequent wards the [[Patient copier button]] can be used to create the next record.
[[EMIP]]'s will be distributed to collectors/laptops in the same way as we collect ward patients, using the assigned MRN's, so over time, they should have an equal distribution based on your EFT. Further, there will no longer be special collection instructions for EMIPs under [[Change from Service Location to Service, Boarding Loc and Transfer Ready DtTm tmp entry]].
 
Additional things we might be able to copy in the future are (not implemented now to allow general use of the copier button):
* Visit Admit Date and time
* import dispo and dispo_dttm (+ 5 min) into the previous location and arrive_dttm automatically
 
== Thoughts? ==
As usual, if you have thoughts or ideas about this, please post them here.
 
*This process seems complicated beyond belief. The potential to either, have multitudes of patients duplicated, or more importantly, patients missed seems astronomical. Inadvertently duplicating patients will end up being way more work for data collectors. How will we, as data collectors even know that we have duplicated a patient that another data collector has already done? Or conversely, how will we know if we have missed a patient?
*If my understanding of this process is correct, the idea is to have one patient/laptop follow the same patient for the entire length of stay. I have had some patients for example, that during the same admission transfer from my e5 medicine ward, to medical intensive care, and back again, THREE times. I am not trained to collect on critical care. Critical care is a whole different ballpark. That means that every data collector in the city, will need to be cross trained for both medicine and critical care.
*I do not see how this way of splitting patients can be done on a fair and equitable basis. We do not all have the same eft, and we don't all have nice simple .5 eft's. My eft is .65. Not quite as easy to fairly and equitably split.
**The division of workload should actually be easier using this method. If you are a .65 as opposed to a .5, then the chart numbers that are assigned to you would be equivilant to your EFT, so a .65 EFT would be assigned more of the chart numbers than a .5 EFT would be.[[User:Mlagadi|Mlagadi]] 12:24, 2019 September 5 (CDT)
*What about the emip's? How will they be handled? The number of emip's that I do are variable. I had 29 emip's in a 10 day time frame last month, and 40+ emip's for the month of August. Other months I'll have half of that for the entire month.
**EMIP's will be collected in the same way as we collect ward patients, using the assigned MRN's, so over time, they should have an equal distribution based on your EFT.[[User:Mlagadi|Mlagadi]] 12:24, 2019 September 5 (CDT)
*Bottom line, the current process works just fine. It has worked just fine since the inception of the program. "If it ain't broke, don't fix it." Contrary to what some may think, it IS NOT (in my humble opinion) a hard ship for me, to have to admit patients that have transferred to my unit from a different unit within the hospital. The alternative, as proposed by this patient follow project will be unbelievably more work, and a logistical nightmare of unfathomable proportions. [[User:DPageNewton|DPageNewton]] 09:41, 2019 September 5 (CDT)


=== How was the distribution initially defined and validated? ===
We would essentially take the sum EFTs per program/site and consider them as 100%, and then assign the chart numbers based on that percentage. For example, if a site has 3 collectors that are each a .5EFT, each collector would get 33% of that site's new admissions, so collector A might get charts ending in 00-33, collector B 34-66, and collector C 67-99.
{{Collapsable
| always= The last two digits of chart numbers are evenly distributed and can be used for this.
| full= * 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 similarity with few 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 showed 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:\CCMDB_Special_Projects\Project_PatientFollow_ChartNumberDistribution''.  The results support the viability of using the last two digits of the chart number in allocating patients among the data collectors. Additional analysis info is in S:\MED\MED_CCMED\ChartLastDigitAnalysis\NormalizedCounts_Comparison\2_Paired T-Test and Data.xlsx
*** Additional analyses were done separately for Medicine and Critical Program for each site and 1) each year, 2) each quarter and 3 )each month to determine any seasonal variation across time. The distributions are generally uniform across subgroups with relatively few peaks.  However, there seems to be some seasonal variation  which is observed more in Critical Care than Medicine Program. The histograms are also in  in ''X:\CCMDB_Special_Projects\Project_PatientFollow_ChartNumberDistribution''.
** Julie also did the distribution of the '''first two digits''' numbers and  found out that the distribution was skewed to the right. Therefore, this cannot be used as a tool for allocating patients. The distribution is in ''X:\CCMDB_Special_Projects\Project_PatientFollow_ChartNumberDistribution'' .
* I think this is a good starting strategy to allocate patients among the data collectors proportionately in each site.}}


Some questions/comments/concerns about the ''patientfollow project:
== Process to identify Medicine patients from EPR at STB ==
''
* For any collection unit for example, STB E6 four EPR reports have to be run (that is 4 EPR reports per unit) to ensure there are no duplicate patients, incorrectly enter disqualified patients or patients entered in error by MR staff.  So in the instance of laptops that have two units (B5/IMCU) that means 8 reports. All 4 lists must be checked because no one list is inclusive of all admission/discharge/transfer activity for a unit from date of last collection. These lists must be reconciled with each other and compared to the unit census. Collectors may use a different methodology/approach to collection that works best for them.
* For any collection unit for example, STB E6 four EPR reports have to be run (that is 4 EPR reports per unit) to ensure there are no duplicate patients, incorrectly enter disqualified patients or patients entered in error by MR staff.  So in the instance of laptops that have two units (B5/IMCU) that means 8 reports. All 4 lists must be checked because no one list is inclusive of all admission/discharge/transfer activity for a unit from date of last collection. These lists must be reconciled with each other and compared to the unit census. Collectors may use a different methodology/approach to collection that works best for them.


Breakdown per unit:
Breakdown per unit:
*1. The admission list
*2. The discharge list
*3. The transfer list
*4. The unit census


Simply looking at and entering patients from lists is not enough, list entries may require further analysis:


1. The admission list
On the transfer list for example there may be entries made in error that patient A was admitted to a unit. The error is usually followed by a “transfer to another unit” a few minutes later. My understanding is that when an entry error is made by MR staff once entered, the entry cannot be deleted, so to reconcile the error another entry is made to “transfer” the patient to the correct unit location. Additionally, sites and units may have certain “idiosyncrasies” for example chemo only admissions for STB E6 are not included in the data base. This can only be ascertained by entering the profile and taking a closer look at the information contained within to determine whether the patient should/should not be included in the database. Simply looking at/using  entries found on a list is not always sufficient or indeed accurate. The issue would be exacerbated by a random chart number assignment for no information at all can be gleaned from a record number.


2. The discharge list
In fact, there is a fair amount of “investigative” work involved in data collection such as running and reconciling 4 EPR lists per unit, and  follow up of patient list entries as necessary to ascertain “true/legitimate” patient admissions so as to avoid entry error, duplication, or missing patients.


3. The transfer list


4. The unit census
=== concerns about patient follow due to this complicated process ===
The process to identify patients for collection in our database is currently ill defined, complex and different between collectors and sites.  


*It should be questioned then whether amalgamating all data collection units within a site for example there are 4 medicine collection units at STB + EMIPs how to possibly track and reconcile all these lists with any semblance of accuracy. This would be a very labor/time intensive and complicated process, as well as a significant logistical challenge. Use of EPR lists to create further lists/spreadsheets in Excel seems redundant and a risky proposition in terms of inclusion and accuracy. There are also potential PHIA considerations whereas patient information on laptops is currently stored/accessed through a separate program, what are the implications for "personal" and/or redundant storage of patient information on data collector accounts? [[User:Ppiche|Pamela Piche]] 10:19, 2019 September 5 (CDT)
** There would be no extra lists, the allocation would happen automatically within Cognos, so the processes you guys have now would just go away, you would simply enter the patient that show up on your [[CSS]], and you wouldn't even see the ones that are not yours. RE concerns about patients that may be missing from Cognos, that is a separate issue: if pts are missing from Cognos, and still don't show up on the 2nd day after their admission, you need to tell me so we can troubleshoot that. If those are addressed then this should no longer be relevant to patientFollow. If this answers the concerns, please remove this discussion. If not, please elaborate. Ttenbergen 21:41, 2020 October 15 (CDT)
}}


Simply looking at and entering patients from lists is not enough, list entries may require further analysis:
== Background ==
We needed to implement {{PAGENAME}} in order to be able to streamline collection. Doing it by location meant multiple records per admission, [[Coordination of data between collectors]], and other issues.
Also, it prevented flexible re-allocation of workload according to differing collector EFTs - under the new scheme we can split patient load according to EFT.


=== Transition dates ===
Since this demarcation comes up repeatedly, use [[query created_PatientFollow]] so this is done consistently.


On the transfer list  for example there may be entries made in error that patient A was admitted to a unit. The error is usually followed by a “transfer to another unit” a few minutes later. My understanding is that when an entry error is made by MR staff once entered, the entry cannot be deleted, so to reconcile the error another entry is made to “transfer” the patient to the correct unit location. Additionally, sites and units may have certain “idiosyncrasies” for example chemo only admissions for STB E6 are not included in the data base. This can only be ascertained by entering the profile and taking a closer look at the information contained within to determine whether the patient should/should not be included in the database. Simply looking at/using  entries found on a list is not always sufficient or indeed accurate. The issue would be exacerbated by a random chart number assignment for no information at all can be gleaned from a record number.
{{Collapsable
 
| always= Transition dates
In fact, there is a fair amount of “investigative” work involved in data collection such as running and reconciling 4 EPR lists per unit, and  follow up of patient list entries as necessary to ascertain “true/legitimate” patient admissions so as to avoid entry error, duplication, or missing patients.
| full=


It should be questioned then whether amalgamating all data collection units within a site for example there are 4 medicine collection units at STB + EMIPs how to possibly track and reconcile all these lists with any semblance of accuracy. This would be a very labor/time intensive and complicated process, as well as a significant logistical challenge. Use of EPR lists to create further lists/spreadsheets in Excel seems redundant and a risky proposition in terms of inclusion and accuracy. There are also potential PHIA considerations whereas patient information on laptops is currently stored/accessed through a separate program, what are the implications for "personal" and/or redundant storage of patient information on data collector accounts? [[User:Ppiche|Pamela Piche]] 10:19, 2019 September 5 (CDT)
Patient Follow one record one episode model
====GRA====
GRA Med & GRA_CC
*patient allocation by Chart last two digits starting 2020-Oct-01;  All patients who have an ''Accept DtTm'' or [[Dispo DtTm field | Dispo DtTm]] ON or AFTER 2020-Oct_1, the collector will apply the patient follow model
====HSC====
HSC Med & HSC CC (MICU) (SICU) 
*(Excludes HSC IICU)
**All patients who have an ''Accept DtTm'' or [[Dispo DtTm field | Dispo DtTm]] ON or AFTER '''2020-Oct-15''' the collector will apply the patient follow model
** In the Task meeting '''Nov 30,2021''', it was decided that HSC CC has to go back to the old system, continuous admissions at HSC MICU and HSC SICU had been changed and made into 2 separate records onwards. From '''2020-Oct-15''', there were  only 11 vetted  records  having such case  and they were un-winded.  
====STB====
STB Med
*All patients who have a ''Accept DtTm'' or [[Dispo DtTm field | Dispo DtTm]] ON or AFTER '''2020-Oct-15''', the collector will apply the patient follow model


*This does represent a fairly big change in the way that we are used to collecting. This concept warrants an in-person explanation, where questions and concerns can be discussed. It is hard to read the WIKI page and get a full understanding of the intent of this project.[[User:Mlagadi|Mlagadi]] 12:32, 2019 September 5 (CDT)
STB ICU's
* S7-STB MICU & S9-STB ACCU >= '''2020-10-15'''
* S8-STB_CICU - Stephanie Cortilet: since Steph pre-enters, start with Patients with ''Accept DtTm'' >= 2020-10-16
** [[STB_CC#Decision to not combine collection of STB CC on one laptop]] but some of the [[Change from Service Location to Service, Boarding Loc and Transfer Ready DtTm tmp entry]] still apply.
}}


== Related articles ==  
== Related articles ==  

Latest revision as of 15:14, 2023 May 31

This page describes how collection of incoming patients is split across data collectors

Identifying admissions / Starting collection

Patients are assigned to laptops by the last two digits of their Chart number. Cognos2 Service Starter automatically filter them, just follow Using Cognos2 to keep track of patients. Special considerations apply to John or Jane Doe patients.

Entering into the laptop

The initial ward would need to be entered as usual. For stays on subsequent wards, Cognos2 Service Starter and Patient Viewer Tab Cognos ADT2 can be used to create another line in the Boarding Loc and Service tmp entry.

Data would go into one profile unless a patient leaves the service. For example, if a pt starts in medicine, then goes to ICU, and then comes back to medicine, then coming back to medicine would mean starting a new profile.

Fallback process when Cognos data is unavailable

See Cognos downtime procedure

Medical Records requests

Shelf split based on Laptop identifier, see HSC Medical Records requests for details.

Actual chart number split per site and per laptop

The split is automatically reflected in Cognos2 Service Starter, no additional filtering needed. The corresponding data is stored in S PatientFollow distribution table.

Viewing the numbers assigned to a given laptop

The assignment is a matter of laptop, chart number ending and the date at which point a specific distribution started. We don't want to store it here on the wiki because it is kind of messy and hard to keep updated. Use "Show PatientFollow allocation" button to see which numbers are assigned to the laptop you are working on during which timeframe.

Assignment changes

See Processes around changing a PatientFollow assignment

Exception: HSC_IICU

See HSC_IICU_Collection_Guide#Workload_Sharing_for_HSC_IICU

Follow between medicine/critical care or just within one program

For now we are testing this just following patients within the same program, eg if a patient were admitted to a medicine ward, then ICU, and then back to the same medicine ward, then the medicine collector would get the two med admissions, and the CC collector would collect the ICU stay. This may change in the future but would require fairly significant changes to CCMDB.accdb Data Integrity Checks and other settings in CCMDB.accdb.

Programming that would need to be updated to be able to use a laptop across programs

  • cross checks have been checked as part of previous project, should work
  • Generupdate / query check_tmp_generate_allowed
  • Hider
  • Converter functions Hosp, Loc, Prog
old process and questions that were addressed   

Old Process

Our database used to collect patient ward stays, which meant the data of a patient could be processed by several collectors during the admission. This lead to extra, wasted work of different collectors familiarizing themselves with the same patient. PatientFollow Project was set up to change to a system where one collector keeps following a patient.

Currently patients are assigned to collectors/laptops based on where they are admitted. To change to the new system, we would need to identify patients who enter a given site and then assign them to the collector pool equitably. We are planning on a process based on the last two digits of the chart number.

If we split by chart number, how do we ensure no pts are missed or duplicated?

  • duplication - there could only be duplication if you enter a chart number that is someone else's; your Cognos2 Service Starter will only show your patients, so you would not duplicate someone else's, and any risk of duplicating your own is no higher than it was with the old process.
  • missing a patient - we have been testing the Cognos EPR Report to make sure patients are not missed from it; for PatientFollow we will simply filter that list, so if all patients were on it, they should still all be on the split list
    • main office can run a check between Cognos Data and our data for the first few weeks to make sure all Cognos data is also in our data

Would the LOS have any impact on this sharing plan?

This would not be a problem.   
  • 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.

EMIPs

EMIP's will be distributed to collectors/laptops in the same way as we collect ward patients, using the assigned MRN's, so over time, they should have an equal distribution based on your EFT. Further, there will no longer be special collection instructions for EMIPs under Change from Service Location to Service, Boarding Loc and Transfer Ready DtTm tmp entry.

How was the distribution initially defined and validated?

We would essentially take the sum EFTs per program/site and consider them as 100%, and then assign the chart numbers based on that percentage. For example, if a site has 3 collectors that are each a .5EFT, each collector would get 33% of that site's new admissions, so collector A might get charts ending in 00-33, collector B 34-66, and collector C 67-99.

The last two digits of chart numbers are evenly distributed and can be used for this.   
  • 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 similarity with few 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 showed 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:\CCMDB_Special_Projects\Project_PatientFollow_ChartNumberDistribution. The results support the viability of using the last two digits of the chart number in allocating patients among the data collectors. Additional analysis info is in S:\MED\MED_CCMED\ChartLastDigitAnalysis\NormalizedCounts_Comparison\2_Paired T-Test and Data.xlsx
      • Additional analyses were done separately for Medicine and Critical Program for each site and 1) each year, 2) each quarter and 3 )each month to determine any seasonal variation across time. The distributions are generally uniform across subgroups with relatively few peaks. However, there seems to be some seasonal variation which is observed more in Critical Care than Medicine Program. The histograms are also in in X:\CCMDB_Special_Projects\Project_PatientFollow_ChartNumberDistribution.
    • Julie also did the distribution of the first two digits numbers and found out that the distribution was skewed to the right. Therefore, this cannot be used as a tool for allocating patients. The distribution is in X:\CCMDB_Special_Projects\Project_PatientFollow_ChartNumberDistribution .
  • I think this is a good starting strategy to allocate patients among the data collectors proportionately in each site.

Process to identify Medicine patients from EPR at STB

  • For any collection unit for example, STB E6 four EPR reports have to be run (that is 4 EPR reports per unit) to ensure there are no duplicate patients, incorrectly enter disqualified patients or patients entered in error by MR staff. So in the instance of laptops that have two units (B5/IMCU) that means 8 reports. All 4 lists must be checked because no one list is inclusive of all admission/discharge/transfer activity for a unit from date of last collection. These lists must be reconciled with each other and compared to the unit census. Collectors may use a different methodology/approach to collection that works best for them.

Breakdown per unit:

  • 1. The admission list
  • 2. The discharge list
  • 3. The transfer list
  • 4. The unit census

Simply looking at and entering patients from lists is not enough, list entries may require further analysis:

On the transfer list for example there may be entries made in error that patient A was admitted to a unit. The error is usually followed by a “transfer to another unit” a few minutes later. My understanding is that when an entry error is made by MR staff once entered, the entry cannot be deleted, so to reconcile the error another entry is made to “transfer” the patient to the correct unit location. Additionally, sites and units may have certain “idiosyncrasies” for example chemo only admissions for STB E6 are not included in the data base. This can only be ascertained by entering the profile and taking a closer look at the information contained within to determine whether the patient should/should not be included in the database. Simply looking at/using entries found on a list is not always sufficient or indeed accurate. The issue would be exacerbated by a random chart number assignment for no information at all can be gleaned from a record number.

In fact, there is a fair amount of “investigative” work involved in data collection such as running and reconciling 4 EPR lists per unit, and follow up of patient list entries as necessary to ascertain “true/legitimate” patient admissions so as to avoid entry error, duplication, or missing patients.


concerns about patient follow due to this complicated process

The process to identify patients for collection in our database is currently ill defined, complex and different between collectors and sites.

  • It should be questioned then whether amalgamating all data collection units within a site for example there are 4 medicine collection units at STB + EMIPs how to possibly track and reconcile all these lists with any semblance of accuracy. This would be a very labor/time intensive and complicated process, as well as a significant logistical challenge. Use of EPR lists to create further lists/spreadsheets in Excel seems redundant and a risky proposition in terms of inclusion and accuracy. There are also potential PHIA considerations whereas patient information on laptops is currently stored/accessed through a separate program, what are the implications for "personal" and/or redundant storage of patient information on data collector accounts? Pamela Piche 10:19, 2019 September 5 (CDT)
    • There would be no extra lists, the allocation would happen automatically within Cognos, so the processes you guys have now would just go away, you would simply enter the patient that show up on your CSS, and you wouldn't even see the ones that are not yours. RE concerns about patients that may be missing from Cognos, that is a separate issue: if pts are missing from Cognos, and still don't show up on the 2nd day after their admission, you need to tell me so we can troubleshoot that. If those are addressed then this should no longer be relevant to patientFollow. If this answers the concerns, please remove this discussion. If not, please elaborate. Ttenbergen 21:41, 2020 October 15 (CDT)

Background

We needed to implement PatientFollow Project in order to be able to streamline collection. Doing it by location meant multiple records per admission, Coordination of data between collectors, and other issues. Also, it prevented flexible re-allocation of workload according to differing collector EFTs - under the new scheme we can split patient load according to EFT.

Transition dates

Since this demarcation comes up repeatedly, use query created_PatientFollow so this is done consistently.

Transition dates   

Patient Follow one record one episode model

GRA

GRA Med & GRA_CC

  • patient allocation by Chart last two digits starting 2020-Oct-01; All patients who have an Accept DtTm or Dispo DtTm ON or AFTER 2020-Oct_1, the collector will apply the patient follow model

HSC

HSC Med & HSC CC (MICU) (SICU)

  • (Excludes HSC IICU)
    • All patients who have an Accept DtTm or Dispo DtTm ON or AFTER 2020-Oct-15 the collector will apply the patient follow model
    • In the Task meeting Nov 30,2021, it was decided that HSC CC has to go back to the old system, continuous admissions at HSC MICU and HSC SICU had been changed and made into 2 separate records onwards. From 2020-Oct-15, there were only 11 vetted records having such case and they were un-winded.

STB

STB Med

  • All patients who have a Accept DtTm or Dispo DtTm ON or AFTER 2020-Oct-15, the collector will apply the patient follow model

STB ICU's

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