Calculating readmissions within 30 day and 365 days intervals

narrowdave

New Member
Joined
May 3, 2017
Messages
19
Hi – first posting on here, so hopefully I give the right level of detail… I seem to need some help I’ve set up a spreadsheet to monitor numerous aspects of admissions and discharges from mental health hospitals across a UK region. Data is entered into an excel table – there are loads of connected pivot charts which are hidden and then loads of the data is brought together and shown via a dashboard type setup…

All’s fine – I’m a nurse rather than a techie, so much learning has occurred… I have one remaining challenge however… I need to be able to show readmission data. i.e. number of readmissions within a 30 day period (since recording began); number of readmissions within the last 30 days (i.e. ending today); number of readmissions within a 365 day period (since data collection began); and number of readmissions within the last 365 day period (i.e. ending today)…

Once in use the spreadsheet will rapidly grow in terms of number of patients being monitored and users of the system may need to access real time data at any point in time.

My spreadsheet is formatted as follows (dummy data included AND only showing the columns relevant to current query):

[TABLE="class: grid, width: 500"]
<tbody>[TR]
[TD][/TD]
[TD]A[/TD]
[TD]H[/TD]
[TD]Z[/TD]
[TD]AD[/TD]
[/TR]
[TR]
[TD]40[/TD]
[TD]Unique Patient Identifier[/TD]
[TD]Admission Date[/TD]
[TD]Discharge Date[/TD]
[TD]Admission number[/TD]
[/TR]
[TR]
[TD]41[/TD]
[TD]265 409 8111[/TD]
[TD]12/12/2017[/TD]
[TD][/TD]
[TD]1[/TD]
[/TR]
[TR]
[TD]42[/TD]
[TD]123 456 7890[/TD]
[TD]25/01/2017[/TD]
[TD]31/01/2016[/TD]
[TD]1[/TD]
[/TR]
[TR]
[TD]43[/TD]
[TD]123 765 8976[/TD]
[TD]12/02/2017[/TD]
[TD]28/02/2017[/TD]
[TD]1[/TD]
[/TR]
[TR]
[TD]44[/TD]
[TD]123 456 7890[/TD]
[TD]09/03/2017[/TD]
[TD][/TD]
[TD]2[/TD]
[/TR]
[TR]
[TD]45[/TD]
[TD]etc[/TD]
[TD]etc[/TD]
[TD]etc[/TD]
[TD]etc[/TD]
[/TR]
</tbody>[/TABLE]

As I said, the number of rows will grow rapidly…

So what I need are some formulae which will:


  • identify duplicate Unique Patient Identifiers – i.e. pick out readmissions
  • then compare measure the interval between admission date and previous discharge dates for those patients (of course some patients will still be in hospital so have no discharge date – for others there may be no readmissions i.e. it's their first admission).
  • Then tell me how many readmissions occurred within:
    • 30 days ending on the day of the query
    • 365 days ending on the day of the query
    • Any 30 day interval since recording began
    • Any 365 day interval since recording began

I’m competent but not good at writing formulae (getting better though!!). I would appreciate any suggestions, advice, assistance that people can offer…

Thx
 

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Re: Help needed calculating readmissions within 30 day and 365 days intervals

Welcome to the forum.

I did something similar to this for someone else

Code:
[TABLE="width: 798"]
<tbody>[TR]
[TD]ID[/TD]
[TD]Reason[/TD]
[TD]Name[/TD]
[TD]Admit Date[/TD]
[TD]Discharge Date[/TD]
[TD]30[/TD]
[TD]90[/TD]
[TD]Mth Yr[/TD]
[TD][/TD]
[TD]Feb-15[/TD]
[TD]# of Occurrences (Frequency)[/TD]
[TD]Occurrence Rate % (Frequency Rate)[/TD]
[/TR]
[TR]
[TD="align: right"]1234[/TD]
[TD]Lung[/TD]
[TD]Joe Schmoe[/TD]
[TD="align: right"]02/11/14[/TD]
[TD="align: right"]18/11/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Nov 14[/TD]
[TD][/TD]
[TD]Lung[/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]1234[/TD]
[TD]Lung[/TD]
[TD]Joe Schmoe[/TD]
[TD="align: right"]19/11/14[/TD]
[TD="align: right"]25/11/14[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Nov 14[/TD]
[TD][/TD]
[TD="align: right"]30[/TD]
[TD]0[/TD]
[TD]0%[/TD]
[/TR]
[TR]
[TD="align: right"]1234[/TD]
[TD]Lung[/TD]
[TD]Joe Schmoe[/TD]
[TD="align: right"]08/12/14[/TD]
[TD="align: right"]09/12/14[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Dec 14[/TD]
[TD][/TD]
[TD="align: right"]90[/TD]
[TD]0[/TD]
[TD]0%[/TD]
[/TR]
[TR]
[TD="align: right"]1234[/TD]
[TD]Lung[/TD]
[TD]John Doe[/TD]
[TD="align: right"]07/11/14[/TD]
[TD="align: right"]09/11/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Nov 14[/TD]
[TD][/TD]
[TD]Bone Marrow[/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]1234[/TD]
[TD]Lung[/TD]
[TD]John Doe[/TD]
[TD="align: right"]05/12/14[/TD]
[TD="align: right"]14/12/14[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Dec 14[/TD]
[TD][/TD]
[TD="align: right"]30[/TD]
[TD]0[/TD]
[TD]0%[/TD]
[/TR]
[TR]
[TD="align: right"]1234[/TD]
[TD]Lung[/TD]
[TD]John Doe[/TD]
[TD="align: right"]18/12/14[/TD]
[TD="align: right"]20/12/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Dec 14[/TD]
[TD][/TD]
[TD="align: right"]90[/TD]
[TD]0[/TD]
[TD]0%[/TD]
[/TR]
[TR]
[TD="align: right"]1234[/TD]
[TD]Lung[/TD]
[TD]John Doe[/TD]
[TD="align: right"]07/01/15[/TD]
[TD="align: right"]11/01/15[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Jan 15[/TD]
[TD][/TD]
[TD]Stomach[/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]7893[/TD]
[TD]Bone Marrow[/TD]
[TD]Jane Doe[/TD]
[TD="align: right"]16/06/14[/TD]
[TD="align: right"]26/06/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Jun 14[/TD]
[TD][/TD]
[TD="align: right"]30[/TD]
[TD]0[/TD]
[TD]0%[/TD]
[/TR]
[TR]
[TD="align: right"]7893[/TD]
[TD]Bone Marrow[/TD]
[TD]Jane Doe[/TD]
[TD="align: right"]27/06/14[/TD]
[TD="align: right"]03/07/14[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Jun 14[/TD]
[TD][/TD]
[TD="align: right"]90[/TD]
[TD]0[/TD]
[TD]0%[/TD]
[/TR]
[TR]
[TD="align: right"]7865[/TD]
[TD]Stomach[/TD]
[TD]Billy Bob Thornton[/TD]
[TD="align: right"]15/09/14[/TD]
[TD="align: right"]22/09/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Sep 14[/TD]
[TD][/TD]
[TD]Gall Bladder[/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]7865[/TD]
[TD]Stomach[/TD]
[TD]Billy Bob Thornton[/TD]
[TD="align: right"]06/10/14[/TD]
[TD="align: right"]14/10/14[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Oct 14[/TD]
[TD][/TD]
[TD="align: right"]30[/TD]
[TD]0[/TD]
[TD]0%[/TD]
[/TR]
[TR]
[TD="align: right"]7865[/TD]
[TD]Stomach[/TD]
[TD]Bo Peep[/TD]
[TD="align: right"]12/12/14[/TD]
[TD="align: right"]14/12/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Dec 14[/TD]
[TD][/TD]
[TD="align: right"]90[/TD]
[TD]0[/TD]
[TD]0%[/TD]
[/TR]
[TR]
[TD="align: right"]7865[/TD]
[TD]Stomach[/TD]
[TD]Bo Peep[/TD]
[TD="align: right"]18/12/14[/TD]
[TD="align: right"]20/12/14[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Dec 14[/TD]
[TD][/TD]
[TD]Toenail[/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]7865[/TD]
[TD]Stomach[/TD]
[TD]Bo Peep[/TD]
[TD="align: right"]07/01/15[/TD]
[TD="align: right"]11/01/15[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Jan 15[/TD]
[TD][/TD]
[TD="align: right"]30[/TD]
[TD]0[/TD]
[TD]0%[/TD]
[/TR]
[TR]
[TD="align: right"]2345[/TD]
[TD]Gall Bladder[/TD]
[TD]Jan Jones[/TD]
[TD="align: right"]07/11/14[/TD]
[TD="align: right"]09/11/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Nov 14[/TD]
[TD][/TD]
[TD="align: right"]90[/TD]
[TD]5[/TD]
[TD]100%[/TD]
[/TR]
[TR]
[TD="align: right"]2345[/TD]
[TD]Gall Bladder[/TD]
[TD]Jan Jones[/TD]
[TD="align: right"]12/12/14[/TD]
[TD="align: right"]14/12/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Dec 14[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]2345[/TD]
[TD]Gall Bladder[/TD]
[TD]Jan Jones[/TD]
[TD="align: right"]18/12/14[/TD]
[TD="align: right"]20/12/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Dec 14[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]2345[/TD]
[TD]Gall Bladder[/TD]
[TD]John Hancock[/TD]
[TD="align: right"]20/01/14[/TD]
[TD="align: right"]27/01/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Jan 14[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]2345[/TD]
[TD]Gall Bladder[/TD]
[TD]John Hancock[/TD]
[TD="align: right"]04/02/14[/TD]
[TD="align: right"]06/03/14[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Feb 14[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Jim Bob[/TD]
[TD="align: right"]06/10/14[/TD]
[TD="align: right"]14/10/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Oct 14[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Jim Bob[/TD]
[TD="align: right"]17/10/14[/TD]
[TD="align: right"]14/12/14[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Oct 14[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Jim Bob[/TD]
[TD="align: right"]14/11/14[/TD]
[TD="align: right"]20/12/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Nov 14[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Jim Bob[/TD]
[TD="align: right"]07/12/14[/TD]
[TD="align: right"]11/01/15[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Dec 14[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Jim Bob[/TD]
[TD="align: right"]09/12/13[/TD]
[TD="align: right"]09/11/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Dec 13[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Jim Bob[/TD]
[TD="align: right"]12/12/14[/TD]
[TD="align: right"]14/12/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Dec 14[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Joe Blob[/TD]
[TD="align: right"]18/12/14[/TD]
[TD="align: right"]20/12/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"]Dec 14[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Joe Blob[/TD]
[TD="align: right"]20/01/15[/TD]
[TD="align: right"]27/01/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Jan 15[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Joe Blob[/TD]
[TD="align: right"]04/02/15[/TD]
[TD="align: right"]06/03/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Feb 15[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Joe Blob[/TD]
[TD="align: right"]06/02/15[/TD]
[TD="align: right"]14/10/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Feb 15[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Joe Blob[/TD]
[TD="align: right"]12/02/15[/TD]
[TD="align: right"]14/12/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Feb 15[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Joe Blob[/TD]
[TD="align: right"]18/02/15[/TD]
[TD="align: right"]20/12/14[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Feb 15[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
[TR]
[TD="align: right"]3200[/TD]
[TD]Toenail[/TD]
[TD]Joe Blob[/TD]
[TD="align: right"]22/02/15[/TD]
[TD="align: right"]11/01/15[/TD]
[TD="align: right"] -[/TD]
[TD="align: right"] 1[/TD]
[TD="align: right"]Feb 15[/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[TD][/TD]
[/TR]
</tbody>[/TABLE]

Can you post more sample data to work with, also an idea of how you propose to display the results.
 
Upvote 0
Here's some more date:

[TABLE="class: grid, width: 707"]
<tbody>[TR]
[TD]
[/TD]
[TD]A
[/TD]
[TD]C
[/TD]
[TD]D
[/TD]
[TD]E
[/TD]
[TD]H
[/TD]
[TD]Z
[/TD]
[TD]AB
[/TD]
[TD]AD
[/TD]
[/TR]
[TR]
[TD]41
[/TD]
[TD]Patient Identifier
[/TD]
[TD]Date of Birth
[/TD]
[TD]Age
[/TD]
[TD]Gender
[/TD]
[TD]Admission Date
[/TD]
[TD]Discharge Date
[/TD]
[TD]Length of Stay
[/TD]
[TD]Admission number
[/TD]
[/TR]
[TR]
[TD]42
[/TD]
[TD]165 409 8111
[/TD]
[TD]08/04/1969
[/TD]
[TD]48
[/TD]
[TD]Female
[/TD]
[TD]15/12/2015
[/TD]
[TD]
[/TD]
[TD]1 years, 4 months, 18 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]43
[/TD]
[TD]123 456 7890
[/TD]
[TD]28/05/1966
[/TD]
[TD]50
[/TD]
[TD]Female
[/TD]
[TD]12/12/2016
[/TD]
[TD]
[/TD]
[TD]0 years, 4 months, 21 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]44
[/TD]
[TD]123 765 8976
[/TD]
[TD]09/04/1992
[/TD]
[TD]25
[/TD]
[TD]Female
[/TD]
[TD]16/12/2015
[/TD]
[TD]
[/TD]
[TD]1 years, 4 months, 17 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]45
[/TD]
[TD]345 654 3214
[/TD]
[TD]05/04/2004
[/TD]
[TD]13
[/TD]
[TD]Female
[/TD]
[TD]12/12/2015
[/TD]
[TD]
[/TD]
[TD]1 years, 4 months, 21 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]46
[/TD]
[TD]345 888 6590
[/TD]
[TD]06/04/1985
[/TD]
[TD]32
[/TD]
[TD]Male
[/TD]
[TD]22/02/2010
[/TD]
[TD]01/01/2015
[/TD]
[TD]4 years, 10 months, 10 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]47
[/TD]
[TD]345 888 6590
[/TD]
[TD]07/04/2000
[/TD]
[TD]17
[/TD]
[TD]Male
[/TD]
[TD]10/01/2015
[/TD]
[TD]12/12/2015
[/TD]
[TD]0 years, 11 months, 2 days
[/TD]
[TD]2
[/TD]
[/TR]
[TR]
[TD]48
[/TD]
[TD]345 888 6590
[/TD]
[TD]08/04/1999
[/TD]
[TD]18
[/TD]
[TD]Male
[/TD]
[TD]01/01/2016
[/TD]
[TD]
[/TD]
[TD]1 years, 4 months, 2 days
[/TD]
[TD]3
[/TD]
[/TR]
[TR]
[TD]49
[/TD]
[TD]456 876 1111
[/TD]
[TD]09/04/1982
[/TD]
[TD]35
[/TD]
[TD]Male
[/TD]
[TD]01/01/2017
[/TD]
[TD]
[/TD]
[TD]0 years, 4 months, 2 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]50
[/TD]
[TD]456 876 5436
[/TD]
[TD]10/04/1977
[/TD]
[TD]40
[/TD]
[TD]Male
[/TD]
[TD]12/12/2013
[/TD]
[TD]12/01/2017
[/TD]
[TD]3 years, 1 months, 0 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]51
[/TD]
[TD]646 482 9002
[/TD]
[TD]11/04/1962
[/TD]
[TD]55
[/TD]
[TD]Male
[/TD]
[TD]17/12/2015
[/TD]
[TD]
[/TD]
[TD]1 years, 4 months, 16 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]52
[/TD]
[TD]646 482 9002
[/TD]
[TD]11/04/1977
[/TD]
[TD]40
[/TD]
[TD]Male
[/TD]
[TD]01/01/2015
[/TD]
[TD]
[/TD]
[TD]2 years, 4 months, 2 days
[/TD]
[TD]2
[/TD]
[/TR]
[TR]
[TD]53
[/TD]
[TD]666 666 2323
[/TD]
[TD]22/05/1964
[/TD]
[TD]52
[/TD]
[TD]Male
[/TD]
[TD]28/05/2016
[/TD]
[TD]
[/TD]
[TD]0 years, 11 months, 5 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]54
[/TD]
[TD]986 432 7890
[/TD]
[TD]07/04/1968
[/TD]
[TD]49
[/TD]
[TD]Female
[/TD]
[TD]14/12/2015
[/TD]
[TD]
[/TD]
[TD]1 years, 4 months, 19 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]55
[/TD]
[TD]111 222 3333
[/TD]
[TD]12/12/1912
[/TD]
[TD]104
[/TD]
[TD]Male
[/TD]
[TD]13/05/2016
[/TD]
[TD]
[/TD]
[TD]0 years, 11 months, 20 days
[/TD]
[TD]1
[/TD]
[/TR]
</tbody>[/TABLE]

On the worksheet I use conditional formatting to grey out discharged patients and varying shades of red to flag up repeat admissions.

In terms of displaying data, there's another worksheet with various charts showing things like age profile, ethnicity and gender of current inpatients; admission source; hospital / ward admitted to; length of stay data; distance from home address; numbers of admissions / discharges each month (going back to 2010)...

I was thinking of simply adding a table to show readmissions in last month, and mean monthly readmissions since records began. And again readmissions in last year, and mean annual readmissions since records began... I have slicers connected to all charts so they can be filtered by diagnosis....

The dashboard forms the basis of monthly meetings where performance and challenges are considered....

Hope that makes sense... and thanks...
 
Upvote 0
Would be useful if you could crate & post a sample of the Table laid out as you want, based on the data above and the results you expect. More chance of getting what you want :-)
 
Upvote 0
The dummy data has a few errors in it... (e.g. one patient has a readmission without first having been discharged!!).. Will clean it up and re-post in a bit.

Am thinking results will look something like:

Readmissions in last 30 days
1
Average readmissions in any 30 day period
1.4
Readmissions in last 365 days
12
Average readmissions in any 365 day period
16

<tbody>
[TD="colspan: 2"]Readmission data
[/TD]

</tbody>

Will tart it up with a bit of colour and maybe conditional formatting to flag up where current performance is worse than average!!

Thanks
 
Upvote 0
This time with accurate data. Firstly the data sheet:

[TABLE="width: 707"]
<tbody>[TR]
[TD]
[/TD]
[TD]A
[/TD]
[TD]C
[/TD]
[TD]D
[/TD]
[TD]E
[/TD]
[TD]H
[/TD]
[TD]Z
[/TD]
[TD]AB
[/TD]
[TD]AD
[/TD]
[/TR]
[TR]
[TD]41
[/TD]
[TD]Patient Identifier
[/TD]
[TD]Date of Birth
[/TD]
[TD]Age
[/TD]
[TD]Gender
[/TD]
[TD]Admission Date
[/TD]
[TD]Discharge Date
[/TD]
[TD]Length of Stay
[/TD]
[TD]Admission number
[/TD]
[/TR]
[TR]
[TD]42
[/TD]
[TD]65 409 8111
[/TD]
[TD]05/04/2004
[/TD]
[TD]48
[/TD]
[TD]Female
[/TD]
[TD]01/01/2010
[/TD]
[TD]01/03/2012
[/TD]
[TD]2 years, 2 months, 0 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]43
[/TD]
[TD]123 456 7890
[/TD]
[TD]06/04/1985
[/TD]
[TD]50
[/TD]
[TD]Female
[/TD]
[TD]06/01/2012
[/TD]
[TD]12/12/2012
[/TD]
[TD]0 years, 11 months, 6 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]44
[/TD]
[TD]123 765 8976
[/TD]
[TD]07/04/2000
[/TD]
[TD]25
[/TD]
[TD]Female
[/TD]
[TD]12/12/2013
[/TD]
[TD]
[/TD]
[TD]3 years, 4 months, 22 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]45
[/TD]
[TD]111 222 3333
[/TD]
[TD]08/04/1999
[/TD]
[TD]13
[/TD]
[TD]Female
[/TD]
[TD]01/12/2014
[/TD]
[TD]
[/TD]
[TD]2 years, 5 months, 3 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]46
[/TD]
[TD]345 654 3214
[/TD]
[TD]09/04/1982
[/TD]
[TD]32
[/TD]
[TD]Male
[/TD]
[TD]01/05/2016
[/TD]
[TD]
[/TD]
[TD]1 years, 0 months, 3 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]47
[/TD]
[TD]345 888 6590
[/TD]
[TD]10/04/1977
[/TD]
[TD]17
[/TD]
[TD]Male
[/TD]
[TD]01/07/2016
[/TD]
[TD]07/08/2016
[/TD]
[TD]0 years, 1 months, 6 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]48
[/TD]
[TD]345 888 6590
[/TD]
[TD]11/04/1962
[/TD]
[TD]18
[/TD]
[TD]Male
[/TD]
[TD]01/10/2016
[/TD]
[TD]15/10/2016
[/TD]
[TD]0 years, 0 months, 14 days
[/TD]
[TD]2
[/TD]
[/TR]
[TR]
[TD]49
[/TD]
[TD]345 888 6590
[/TD]
[TD]11/04/1977
[/TD]
[TD]35
[/TD]
[TD]Male
[/TD]
[TD]01/11/2016
[/TD]
[TD]
[/TD]
[TD]0 years, 6 months, 3 days
[/TD]
[TD]3
[/TD]
[/TR]
[TR]
[TD]50
[/TD]
[TD]456 876 1111
[/TD]
[TD]22/05/1964
[/TD]
[TD]40
[/TD]
[TD]Male
[/TD]
[TD]01/12/2016
[/TD]
[TD]
[/TD]
[TD]0 years, 5 months, 3 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]51
[/TD]
[TD]456 876 5436
[/TD]
[TD]07/04/1968
[/TD]
[TD]55
[/TD]
[TD]Male
[/TD]
[TD]01/01/2017
[/TD]
[TD]12/01/2017
[/TD]
[TD]0 years, 0 months, 11 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]52
[/TD]
[TD]646 482 9002
[/TD]
[TD]11/04/1977
[/TD]
[TD]40
[/TD]
[TD]Male
[/TD]
[TD]01/02/2017
[/TD]
[TD]25/02/2017
[/TD]
[TD]0 years, 0 months, 24 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]53
[/TD]
[TD]646 482 9002
[/TD]
[TD]22/05/1964
[/TD]
[TD]52
[/TD]
[TD]Male
[/TD]
[TD]01/03/2017
[/TD]
[TD]
[/TD]
[TD]0 years, 2 months, 3 days
[/TD]
[TD]2
[/TD]
[/TR]
[TR]
[TD]54
[/TD]
[TD]666 666 2323
[/TD]
[TD]07/04/1968
[/TD]
[TD]49
[/TD]
[TD]Female
[/TD]
[TD]01/04/2017
[/TD]
[TD]
[/TD]
[TD]0 years, 1 months, 3 days
[/TD]
[TD]1
[/TD]
[/TR]
[TR]
[TD]55
[/TD]
[TD]986 432 7890
[/TD]
[TD]12/12/1912
[/TD]
[TD]104
[/TD]
[TD]Male
[/TD]
[TD]01/05/2017
[/TD]
[TD]
[/TD]
[TD]0 years, 0 months, 3 days
[/TD]
[TD]1


[/TD]
[/TR]
</tbody>[/TABLE]


And now the results:

Readmissions in last 30 days
0
Average readmissions in any 30 day period
2
Readmissions in last 365 days
3
Average readmissions in any 365 day period
3

<tbody>
[TD="colspan: 2"]Readmission data
[/TD]

</tbody>

Obviously this is made up data and will be more valid once there are hundreds of admissions entered...

Thanks
 
Upvote 0
It will be - but if (as is likely) people filter / sort it, there's no guarantee it'll stay that way!!! The data is all in an excel table so additional rows of data will be added on an ongoing basis....
 
Upvote 0
Just started looking at this, the last 30/365 days i think is straight forward.
How are you calculating ANY 30/365 day period?
 
Upvote 0
For ANY 30/365 period, was thinking of taking total readmissions since monitoring began (so earliest ever admission on the spreadhseet) then simply dividing by 30 / 365 days to establish the mean... The aim is to know whether current month / year are above or below longer term mean...
 
Upvote 0

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