PatientFollow Project

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.

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.

Pilot

This will be piloted

• at the Grace starting 2020-09-01
• at HSC starting 2020-10-14 (Wednesday after Thanksgiving)

prerequisites for pilot participation

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.

Identifying admissions / Starting collection

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. We are currently developing the EPR Reports Integrator that will help facilitate this (aside from making dealing with reports easier in the first place).

What would be the process for picking up new patients

See Using Cognos Report Integrator to keep track of patients; there would be a filter to limit the list to your location.

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; you will be able to filter to make sure you don't, and you will be able to see the chart number, so can confirm manually
• missing a patient - we have been testing the Cognos tool 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.

Medical Records requests

Split of shelves would need to become based on Laptop identifier (it may already be...); update HSC Medical Records requests.

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.

What would be the actual chart number split per site and per collector

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 actual split is implemented using the "Mine! Mine!" button. See Cognos Admit filter table for a the current state.

The excel sheet with Julie's analysis is at S:\MED\MED_CCMED\ChartLastDigitAnalysis\NormalizedCounts_Comparison\2_Paired T-Test and Data.xlsx

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 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.

Entering into the laptop

The initial ward would need to be entered as usual. For stays on subsequent wards, Cognos Admitter/Patient Viewer Tab Cognos ADT and if need be the Patient copier button can be used to create the next record.

Fallback process when Cognos data is unavailable

Discussing this with Grace now, details to follow.

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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

• not for cross-programs, but still needs doing: need to make sure the workload splitter takes into account the start and end dates in case the workload assignments have recently changed.

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)

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• 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.Mlagadi 12:32, 2019 September 5 (CDT)

Background

need to write up reasons why we need to do this; one would be to reduce / eliminate Coordination of data between collectors

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