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    The Dashwood screening is carried out
    daily by our disease at Westmead.
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    We can have a range between 200 to
    220 category one into patients.
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    Per day.
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    Out of this.
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    It is expected that we see about 60
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    to 70 Category 1 and 2 DEA patients.
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    With the help of onsite
    health she dieticians.
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    We have now managed to narrow
    down this criteria and are able
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    to identify patients who will
    benefit most from a DEA visit.
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    But due to feasibility issues we end up
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    saying only about 5 patients per day.
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    However when staffing levels are adequate
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    at Westmead we will have a full day
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    shift per week allocated to dashboards
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    which can see about 40 to 50 patients.
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    Most involved.
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    Part of the screening process is.
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    To identify the patients just see
    first taking you through the steps.
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    Frankly.
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    The DEA and 98 protocol
    patients are first filtered
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    by transferring data onto
    an Excel spreadsheet.
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    The clear fluids and fluids are excluded.
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    Patients are then categorized into
    category one category two groups.
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    Priority is given to Category 2 patients.
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    Those having low intake first.
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    And lastly long length of
    stay the longest days.
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    Patients get priority.
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    Once a spreadsheet is printed we go on to
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    the wards and discuss with
    the patients about their
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    particular low income low
    supply we preferences
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    to see if the patient is
    consuming the extras.
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    And if not make alternative.
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    Their likes and dislikes are discussed.
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    And.
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    Moved from home or other
    sources get an ACIM pamphlet
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    on nutrition in the New
    South Wales hospitals.
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    We then returned to the office and amend
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    the MFC notes and seaboard preferences.
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    And finally notify the ward dietician.
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    There are some limitations as
    to why the meal intake cannot
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    be conducted by the food
    service assistance such as.
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    If we have seaboard issues
    and the train tickets are
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    printed on A4 paper instead
    of the automated printer.
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    Or if the meal intake.
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    Can only be done if it's
    been from properly promoted
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    to the final stage of
    the life food choice process.
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    So for instance when a train ticket is
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    printed it needs to be pushed through to
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    plating stage to reach
    those stage delivery
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    and then finally the pick up stage.
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    So if there is a place
    that's been missed the FSA
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    is then counting the final
    intake for the patient.
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    Recently we also found that
    the shortage of iPods and Wi-Fi
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    connectivity issues caused a
    delay in data connection.
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    Collection.
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    Additionally we have found that
    once a day code has changed.
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    Multiple meal intakes need to be carried
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    out by the food services system.
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    This leads to corrupted data.
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    So I'll show you on the next slide.
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    In room service you can see
    that this patient has
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    changed giant codes
    after the cut off time.
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    The train tickets were
    printed three times.
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    That.
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    10 0 6 eleven twenty two
    and eleven twenty five.
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    And two of these tray
    tickets were identical
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    as you can see on the bottom screenshots.
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    This means that the moves to where
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    the food service stack went to collect
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    the meal tray they were required to
    take three separate meal intakes.
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    The food service then conducted
    the meal intake on the first meal.
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    As you can see by the green dots.
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    And.
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    For the old following meals placed in any
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    or the recent code food or tray missing.
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    We are still unsure how this data is
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    then interpreted into the dashboard
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    because there's two separate mailing
    texts for the same patient.
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    Another feature of the document
    we're still unclear about
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    is how the NH option affects
    the overall data quality.
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    Says now had mentioned that we also have.
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    Patients who have had a
    enter during meal intake.
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    When you do do that it prompted
    you to put in a recent
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    code and some of the recent
    codes are on the second.
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    On the right so it can be due to diet
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    change which is a popular one for us.
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    If she kept tray or food
    or if the food items are missing.
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    It is important that I
    mentioned that we all see
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    the NRA as a reason for
    the UNC unaccounted data.
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    At Wesley.
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    Some of the challenges we face
    in the Indonesia shouldn't dash
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    or daily the long list of
    category one patients each day.
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    Approximately 200 patients.
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    And since the dashboard has
    so much data in it it can be tedious
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    and time consuming to be
    filtering out on a daily basis.
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    We also find that
    the color coding the red
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    and green is this in
    the analytical data section is
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    the same for the reason
    code and the default
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    meals so it can get a
    bit confusing at times.
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    Lastly the risk category does not
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    necessarily identify patients at risk.
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    We found that most of our Category
    1 and 2 patients indicate that
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    they're consuming adequate meals
    from home or from other sources.
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    However the intake does
    make account for this.
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    >> Passing on so much.
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    Thank.
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    You.
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    >> Thank you.
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    So.
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    I'll just speak about
    the some findings of
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    the student project conducted last year.
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    So there were specific errors
    that we had. The first was to
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    review the quality of the nutrition
    additional data and display.
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    Analyze the rate of missing data.
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    And we also analyzed
    the rate of different news as
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    was indicated by
    the nutrition dish at the time.
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    The third aim was to
    assess the usability of
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    nutrition dashboard data
    and clinical practice.
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    And the third was to assist in
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    the mobility
    and the relevance of nutrition.
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    >> Meal data over three consecutive
    days or nine main meals were
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    analyzed and then an average was
    calculated for each category.
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    >> Was accounted for.
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    >> So we discovered that nutrition Deshpande
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    displayed many reasons for quality data.
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    We knew about the first three reasons
    which were that patient kept tray
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    of food item tray of food item was
    missing and patients refused meals.
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    However we did know that.
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    That poor quality data was
    also reflective of all
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    the changes computer system
    errors and also where
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    food service staff mentally
    entered reasons such as
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    food item missing or when
    the tape was not done.
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    So.
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    What we found in in particular
    we found that the neuro gastro
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    and surgical cardiothoracic wards
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    averaged the highest poor quality data.
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    And the reasons where their patients kept
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    their tray of food item
    away and it was by
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    choice the cardiology
    and the strong wards
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    demonstrated the highest quality data.
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    There were some computer system errors
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    that contributed to the quality of data
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    at the time and this was specifically
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    specifically identified in three wards.
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    This was reported to share for
    rectification at the time.
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    Then we also looked at
    the range of different builds
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    as was indicated by
    the nutrition dashboard.
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    This time we studied
    the items selected in that.
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    And where a rare segment indicated
    patients receiving default meals
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    and the Green Square indicated no issues
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    but to our discovery a blank section.
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    Indicated.
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    That patients did not select a meal
    but also did not get a default.
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    This was not known prior to this study.
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    And Hilscher was consulted
    for advice again.
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    So we found that there was a default
    rate of 30 percent which was
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    different to the health share
    report that we usually would get.
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    And I think if it will agree with me
    we get about 15 to 16 percent of
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    default after Michael choice was
    implemented for Westmead behind you.
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    Neurology and the trauma wards
    have the highest default rate.
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    Incidentally we also
    discovered that two out of.
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    That two out of three study days are
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    few wars according to the nutrition
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    dashboard did not receive any meals
    nor did they make any selections.
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    We later found that this was a system
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    error and nominal indeed was conducted.
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    For the mentioned wards.
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    Now moving on to the staff survey.
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    So we surveyed about eight
    dietitian assistants some open
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    and close questions were asked
    such as whether the data used.
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    Nutrition dashboard if they understood
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    the nutrition dashboard functions
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    the reasons why they
    used it and if standing
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    of the risk category that was correct.
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    We found that 100 percent of
    the dietician assistance used
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    in nutrition dashboard as
    part of their work routine.
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    However 30 percent misinterpreted
    the risk category.
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    Similarly we surveyed the diet
    conditions and in addition
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    we investigated
    if the dietitians correctly understood.
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    The reason he.
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    Will map at patients meal consumption.
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    We found that almost 80 percent of
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    the dietitians used a
    nutrition dashboard of which.
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    80 percent.
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    Viewed intake analysis.
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    Seventy five percent looked
    at the data quality.
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    Only 25 percent viewed meal selection.
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    And 8 percent.
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    Only 8 percent looked
    at the risk category.
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    We also found that 50 percent
    indicated that nutrition
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    dashboard assisted with
    identification of nutrition risk.
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    50 percent did not find it to be a time
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    consuming so time saving tool but I found
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    it helpful for pictures from not picking
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    the crown or the geriatric portions.
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    Of 80 percent indicated
    that nutrition digital
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    dashboard did not assist to prioritize
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    debt and 75 per cent of the dietitians
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    made incorrect interpretation of the meal.
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    Indeed about.
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    About 50 percent also did not refer to
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    the reason he called Map and subsequently
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    were unable to make an interpretation
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    of the red amber and the green squares.
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    There was some discussion that came
    out of this project and there
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    were that milk consumption data
    quality is a complex issue.
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    Energy and put in consumption on.
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    Nutrition dashboard may
    be underestimated. High
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    quality milk consumption
    data does not affect.
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    Patient consumption.
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    There may be some computer system around
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    and it was difficult to decipher
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    the reasons why a patient did not
    select a meal not provided with one.
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    So.
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    The recommendations that were
    made out of this project
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    again was ongoing training for
    full service systems and.
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    Communication of food consumption
    standards with patients.
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    Integration of nutrition dashboard.
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    Seaboard empowerment chart.
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    For dietician assistance to
    be trained to include data
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    collection to collect data on
    specific patients assigned
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    by the dietitians maybe
    and some comprehensive education
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    on all segments of nutrition
    dashboard for dietitian.
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    And dietitian assistance.
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    Again there were some limitations
    so not all the boards
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    were included in this study due
    to the limited time frame.
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    >> It was difficult to validate the results.
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    It was also difficult to
    draw conclusions between
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    rates of milk consumption
    data and specific boards.
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    And.
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    I've.
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    >> With my time.
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    So in conclusion the study
    results demonstrated some of
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    the liability issues with
    the quality of data that was displayed.
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    In.
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    Training with particular
    focus on clinical practice
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    and user visibility in large
    hospitals like Westmead.
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    May put in shared some enhancement
    and improvement of patient outcomes.
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    And I would like to say not
    blind our practices at
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    Westmead should be
    considered as best practice.
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    >> So our final presenter today will
    be tracing Patrick's who's a hero.
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    He's a head dietician of Coffs Harbour
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    Hospital in the mid north coast LH day.
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    >> Already unfortunately was on
    leave today so we were very
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    appreciate appreciative of
    her preparing the slides.
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    We're also very grateful to
    Tracy for agreeing to step in.
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    Today for us she's up tracing
    presenting on how the train might
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    and dashboard data is used a small
    city with limited dietitian
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    hours against a different perspective
    on how it's used to benefit
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    clinical practice and we say
    Tracey for joining us to do so.
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    >> The nutrition dashboard
    and tray monitor are
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    used routinely at Maxwell
    district hospital to
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    assist with the management
    of the dietetic
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    caseload and in
    the nutrition care of patients.
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    I'll give an overview of
    their use and potential
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    benefits that other sites
    so Mexico is one of
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    our smaller facilities
    in the cost clinical
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    network of Mid North Coast
    Local Health District.
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    It's a level 3 48 bed facility about 60 K
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    south of coffee so about
    a 40 minute drive.
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    Today having a look at Maxwell today
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    there's 36 patients in Macksville today
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    so about 75 per cent
    capacity and two thirds
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    of those patients over 65 years of age.
  • 15:01 - 15:03
    The patients there there are a few acute
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    but mostly they're subacute
    patients mostly rehab
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    and recovering from
    surgery or complicated
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    extended hospital stay
    hearing costs or waiting.
  • 15:15 - 15:18
    Residential aged care facility placement
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    Brody commenced at Macksville in
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    the beginning of 2016 with the brand new
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    position so she's built
    it from the ground
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    up and done an amazing job and as we've
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    seen on the wall has a wonderful
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    close relationship with food services
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    and they work extremely well together.
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    Next slide please.
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    Okay so the first My Free Choice
    Program and nutrition dashboard use
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    at Macksville Brody uses that in
    a number of ways for triaging
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    MSP patients for screening patients
    not referred via MSF T for
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    nutrition risk and for triaging
    conventionally placed referrals.
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    So for the three out of M S T
    patients like most places due to
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    limited dietician hours we need
    to try out our patients and many
  • 16:17 - 16:21
    of the patients referrals at Macksville
    are automatically generated
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    referrals from an MSA team
    completed by the nursing staff.
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    So the nutrition dashboard is helpful
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    for Brody to filter through those M.S.
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    to refer patients to see
    what else may be going on.
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    So having a look at today's list
    there are three three new referrals
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    three M S T two patients and of
    those three to have an M S T.
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    Oh sorry.
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    There are three Category 1 patients
    and 6 Category 4 patients.
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    Nothing in between and two of those in
  • 17:03 - 17:08
    those Category 1 patients are don't have
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    any m t completed and one of those
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    Category 1 patients have an M S T of 2.
  • 17:14 - 17:19
    So Brody would you the nutrition
    dashboard to have a look at those Ms t
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    patients particularly the two and use
    that further nutrition dashboard
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    data to treat those patients for
    the screening of nutrition risk for
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    patients that don't have any Misty
    referral or any other referral.
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    Again Brody can have a
    look at the nutrition
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    dashboard and see which
    again the risk category
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    1 and 2 patients and place
    a referral herself
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    and see those patients as time permits.
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    >> For conventionally placed referrals.
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    Again Brody can use
    the risk categories to
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    determine who are
    the higher priority patients
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    and who might be a little more complex
  • 18:09 - 18:13
    and make time and try out
    those patients as well.
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    Another way that Brody uses
    the nutrition dashboard is
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    to use the estimated energy
    and protein intake again to
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    help him form her nutrition
    assessment along with all
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    of the other data collected
    from a variety of sources.
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    The other system that Brody uses is
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    the train monitor a wall crime monitor
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    is conventionally used by food
    service staff to manage workflow.
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    It can be a really valuable source of
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    information for the dietitian as well.
  • 18:52 - 18:55
    So there's two applications in train
    monitor that proteins will use at
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    Macksville and that includes
    the mobile intake data and export control.
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    >> The next slide.
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    >> Yeah so you can see there with
    a monitor which we have a
  • 19:17 - 19:20
    link to connect to which I'm
    sure everyone's familiar with.
  • 19:21 - 19:22
    We can have a look at the reports
  • 19:23 - 19:28
    and the mobile intake data
    so the mobile intake data.
  • 19:28 - 19:33
    First up on the next slide is used to
  • 19:33 - 19:35
    check me or Camille what patients are
  • 19:35 - 19:39
    ordering to check
    if patients are consuming
  • 19:40 - 19:45
    supplements or other nutritious food
  • 19:45 - 19:47
    or drink items that the dietician might
  • 19:47 - 19:51
    have implemented
    and to see which patients
  • 19:51 - 19:53
    are declining meals
    or if meal choices are
  • 19:53 - 19:57
    being made without the patient's input.
  • 19:57 - 20:00
    So I find it useful to cross-check this
  • 20:00 - 20:04
    data also with 24 hour recalls with
  • 20:04 - 20:07
    patients particularly those who may be
  • 20:07 - 20:13
    poor historians or a little confused.
  • 20:13 - 20:21
    Also we use that here it costs
    to check on consumption meal by
  • 20:21 - 20:27
    meal to make sure that items that
    the patient enjoys has been
  • 20:27 - 20:32
    chosen and two to check that
    patients are consuming those
  • 20:32 - 20:37
    items that we might have discussed
    and implemented with them.
  • 20:37 - 20:42
    Another thing that we use to try
    and take data for was to do
  • 20:42 - 20:46
    an intake data audit which students
    conducted for us last year
  • 20:46 - 20:52
    so they gained access to the to
    their train take data and use
  • 20:52 - 20:57
    that within their audit which
    I'll discuss in a little while.
  • 20:58 - 21:01
    So they monitor reports are often
    run at Macksville to assess
  • 21:01 - 21:05
    the intake of patients that have been
    admitted for less than three days.
  • 21:05 - 21:08
    As we know the nutrition
    dashboard kicked in after
  • 21:08 - 21:11
    patients have had data
    collected for three days.
  • 21:12 - 21:17
    So for both patients who are
    in the first three days
  • 21:17 - 21:20
    of their admission
    and the majority of patients
  • 21:20 - 21:22
    at Macksville are in for
    greater than three days
  • 21:22 - 21:26
    they have an average
    length of stay of 13 days.
  • 21:26 - 21:31
    So because BART is only
    there two days a week.
  • 21:31 - 21:33
    Often you know she'll be referred
  • 21:33 - 21:35
    patients within those first three days.
  • 21:35 - 21:39
    So the thing the train modelling
    data is really useful
  • 21:40 - 21:43
    until the nutrition dashboard
    information kicks in.
  • 21:44 - 21:49
    So those reports are really useful to
    evaluate not just in a GM protein
  • 21:50 - 21:54
    but to also look at any other nutrients
    that you might want to consider
  • 21:55 - 21:59
    for patients whether that be carbohydrate
    distribution or fiber intake
  • 21:59 - 22:07
    fluid exchanges or any of
    the electrolytes or vitamin intake as well.
  • 22:07 - 22:08
    >> Next slide.
  • 22:08 - 22:08
    Thanks.
  • 22:10 - 22:15
    >> So we very interested as well to
    have a look at thin accuracy
  • 22:15 - 22:19
    and the reliability of the data within
  • 22:20 - 22:22
    tray monitor and the nutrition dashboard.
  • 22:23 - 22:28
    So last year we asked our food
    service students from Newcastle uni
  • 22:28 - 22:34
    to do an audit for us and have a
    look at the accuracy of the data.
  • 22:35 - 22:37
    The guys have my fair choice.
  • 22:37 - 22:43
    Of course Brad was hesitant to rely
    too much on the data as we were
  • 22:43 - 22:49
    unsure of the accuracy being in
    the early days of my food choice.
  • 22:50 - 22:53
    So we were really keen to get a bit of a
  • 22:53 - 22:57
    reflection of how accurate that data is.
  • 22:57 - 23:01
    So that said last year students
    from Newcastle uni conducted
  • 23:02 - 23:10
    an intake data audit for us
    and Macksville got the gold star.
  • 23:10 - 23:15
    They were rated at 80 per cent
    86 per cent reliability.
  • 23:15 - 23:18
    So above the 80 per cent benchmark set
  • 23:18 - 23:25
    by health share the overall reliability
  • 23:25 - 23:31
    of the data for cost clinical network
    was 81 per cent but I think Matt
  • 23:31 - 23:33
    Max feels good data pushed this up a
  • 23:33 - 23:36
    little bit because costs was overall
  • 23:36 - 23:40
    seventy seven per cent Burlington
    or so a very commendable 80 per cent.
  • 23:41 - 23:47
    And as I said gold star to Macksville
    86 per cent so that improved.
  • 23:47 - 23:52
    Di dietician confidence in in the data.
  • 23:52 - 23:55
    But of course you know it's it's part of
  • 23:55 - 24:00
    a lot of different data that we use for
  • 24:00 - 24:02
    assessment and monitoring and evaluation
  • 24:03 - 24:06
    not just ourselves source of data.
  • 24:07 - 24:10
    So how was that good result achieved.
  • 24:10 - 24:13
    As I said borrowed the dietician down
  • 24:13 - 24:15
    at Macksville since then instigation
  • 24:15 - 24:20
    of the position in the beginning in
    2016 and has a great relationship
  • 24:20 - 24:26
    with food services down there
    and she conducts regular in services
  • 24:26 - 24:34
    and consults you know weekly where
    food services staff she made
  • 24:34 - 24:39
    sure that that the that the good
    results of this particular audit were
  • 24:39 - 24:44
    relayed back to food service staff
    and so food services staff you
  • 24:44 - 24:49
    know they know the importance of being
    accurate with this data and they
  • 24:49 - 24:53
    understand that they're also
    an integral part of great patient care.
  • 24:53 - 24:54
    Dan Maxfield.
  • 24:55 - 24:58
    >> Great nutrition care and having that
  • 25:00 - 25:02
    understanding of
    the importance of the role
  • 25:02 - 25:08
    that they play and that they have a very
  • 25:08 - 25:10
    important part to play in nutrition care
  • 25:10 - 25:14
    >> I think he has continued not
    only the great relationship
  • 25:14 - 25:20
    road has vested services
    but the reliability of that data.
  • 25:20 - 25:21
    Next slide please.
  • 25:26 - 25:30
    So yeah we see hospital wide benefits of
  • 25:30 - 25:39
    the tray monitor
    and nutrition dashboard data.
  • 25:39 - 25:45
    It allows folks limited time to be
    efficiently used in the third to
  • 25:46 - 25:52
    to trash priority patients by having
    a look and cross checking with
  • 25:52 - 25:56
    the key referrals the regular
    referrals and those patients are not
  • 25:56 - 26:01
    referred at all by cross checking
    with nutrition dashboard data.
  • 26:01 - 26:04
    We can zero in on those patients in most
  • 26:04 - 26:06
    need within a limited time and limited
  • 26:06 - 26:13
    FTE it assists with estimating oral
    intake of the patients and gives a
  • 26:14 - 26:17
    quantitative methods
    that perhaps we haven't
  • 26:17 - 26:20
    had the intensity of that information
  • 26:20 - 26:23
    before and it decreases their reliance
  • 26:23 - 26:26
    on nursing staff to complete C chart.
  • 26:27 - 26:31
    There's a pretty high nurse to
    patient ratio down at Macksville
  • 26:31 - 26:36
    so anything that we can do
    to assist in alleviating
  • 26:36 - 26:41
    their workload demand to also
    help to improve working
  • 26:41 - 26:45
    relationships between
    dietetics and nursing staff.
  • 26:45 - 26:47
    So lastly I think.
  • 26:49 - 26:52
    It's ongoing.
  • 26:52 - 26:55
    Party continues to work very
    closely both with food
  • 26:55 - 26:59
    services staff and ward
    staff and ensure that
  • 27:00 - 27:04
    the kitchen staff are
    empowered and feel that they
  • 27:04 - 27:09
    have an important role
    in the care of patients.
  • 27:09 - 27:14
    And we would like to properly
    implement annual audits to
  • 27:14 - 27:19
    ensure that the accuracy of
    the data remains as high
  • 27:19 - 27:23
    and perhaps to set a
    benchmark for the rest of
  • 27:23 - 27:28
    the cost clinical network to
    run to meet Maxfield results.
  • 27:28 - 27:31
    So putting a bit of a
    challenge out there between
  • 27:31 - 27:36
    our facility and even
    if the data isn't accurate.
  • 27:36 - 27:39
    What we can consider is
    is using train monitor
  • 27:39 - 27:42
    to check what the patients are ordering.
  • 27:42 - 27:44
    If nothing else to better inform our
  • 27:44 - 27:49
    nutrition assessments and to also use
  • 27:49 - 27:56
    the nutrition dashboard to flag those
    patients who are on the ordering.
  • 27:56 - 27:57
    That's it for me.
  • 27:58 - 27:58
    >> Thank you.
  • 27:59 - 28:00
    Thank you Tracy.
  • 28:00 - 28:02
    Appreciate that.
  • 28:02 - 28:05
    >> Please say thank you to all that for this.
  • 28:05 - 28:07
    Well that's the end of our presentation
  • 28:07 - 28:09
    session today and I'm sure you'll agree
  • 28:09 - 28:11
    that there was lots of different
  • 28:11 - 28:16
    perspectives provided and I think
    that demonstrates that the data from
  • 28:16 - 28:18
    the dashboard and from
    tray monitor is just
  • 28:18 - 28:21
    not one solution as to how you can use it.
  • 28:21 - 28:24
    >> You can customize it to
    your own facility. You
  • 28:24 - 28:27
    can make it fit your
    workflow as you think
  • 28:27 - 28:29
    is most appropriate and most practical
  • 28:29 - 28:32
    and sometimes implementation
    is quite challenging.
  • 28:32 - 28:34
    So there's some hurdles
    you need to get over
  • 28:35 - 28:38
    but I think it's also really reassuring
  • 28:38 - 28:40
    that we've got lots of departments in
  • 28:40 - 28:43
    the state now who have
    who are utilizing it.
  • 28:43 - 28:46
    You've had experience and I'm
    sure would always be very well.
  • 28:46 - 28:51
    >> JJ any tips or tricks
    or advice or or you would just
  • 28:51 - 28:54
    be a shoulder to cry on
    if you need it so reach out
  • 28:54 - 28:56
    and talk to your colleagues
    especially our presenters
  • 28:56 - 28:58
    today I'm sure that I'll
    be happy to contact you.
  • 28:59 - 29:02
    >> So after some pressure we
    still got 10 minutes left.
  • 29:02 - 29:04
    I've received a couple
    of questions through
  • 29:04 - 29:06
    slideshow and email which I can lead with.
  • 29:06 - 29:08
    So if you've got any questions
    I'd like to share.
  • 29:08 - 29:11
    By the way I can I can raise that.
  • 29:11 - 29:13
    So the first question I
    received by e-mail.
  • 29:15 - 29:16
    Was.
  • 29:18 - 29:19
    From Caitlin.
  • 29:20 - 29:25
    So a question if no one else is us
    is there any concerns regarding
  • 29:25 - 29:28
    the quality of the per cent
    consumed data being entered.
  • 29:29 - 29:33
    If there have been concerns that
    this may not be accurate estimates.
  • 29:33 - 29:35
    Have you had any strategies
    to manage this.
  • 29:35 - 29:38
    And I feel that this is a large barrier
  • 29:38 - 29:40
    to using the dashboard so I might
  • 29:40 - 29:43
    open that up to one of our presenters
    to contribute to that response.
  • 29:43 - 29:45
    If you have something to say.
  • 29:49 - 29:52
    I guess with us that's something
    that we want to work with.
  • 29:53 - 29:53
    So.
  • 29:54 - 29:59
    That we sort of continue doing
    ongoing training and ongoing.
  • 30:00 - 30:02
    >> Compliance checks because
    there is a lot of
  • 30:02 - 30:05
    interpretation when you actually
    go to pick up the food.
  • 30:06 - 30:08
    It's all well and good when
    it's in the prepackaged meals
  • 30:08 - 30:12
    and all the protein and the veggies
    and everything all set up.
  • 30:12 - 30:14
    But once you actually go
    to collect the meals
  • 30:14 - 30:17
    and all the food is sort of
    mashed up together it's
  • 30:17 - 30:20
    hard to sort of determine
    which portion of
  • 30:20 - 30:23
    the veg which portion of
    the protein has been eaten.
  • 30:23 - 30:25
    So there's a lot up to interpretation
  • 30:26 - 30:29
    and that's why we kind
    of take the dashboard
  • 30:29 - 30:31
    as face value and then go into our own
  • 30:31 - 30:33
    investigation to find out whether that is.
  • 30:34 - 30:35
    Accurate or not.
  • 30:37 - 30:39
    Any other presenters like to add to that.
  • 30:41 - 30:42
    I think.
  • 30:42 - 30:43
    >> Like to do it.
  • 30:43 - 30:45
    It's definitely a concern
    and it's something
  • 30:45 - 30:47
    that we're looking into investigating
  • 30:47 - 30:50
    as a project because I wouldn't think
  • 30:50 - 30:52
    it is a lot of work that can be done.
  • 30:52 - 30:53
    So nothing that we've done yet.
  • 30:53 - 30:53
    But.
  • 30:53 - 30:55
    We have our eyes on it.
  • 30:57 - 30:59
    >> So if I could just reflect
    on the presentation
  • 30:59 - 31:01
    from Tracey it sounds like Brody's done
  • 31:01 - 31:03
    a lot of work in that space to try to
  • 31:03 - 31:06
    assure the quality of
    the data is accurate.
  • 31:06 - 31:09
    And I know that he'll share
    in-house training programs
  • 31:09 - 31:12
    for their staff to do that
    but also keep in mind.
  • 31:12 - 31:15
    What did nursing staff do before
    when they recorded a food chart.
  • 31:15 - 31:19
    They've been having different
    challenges or the same challenges.
  • 31:19 - 31:21
    So I think it's great that we've.
  • 31:22 - 31:24
    >> This process in place and yes it could be
  • 31:24 - 31:27
    improvement strategies that going forward.
  • 31:27 - 31:27
    But.
  • 31:28 - 31:28
    Great.
  • 31:28 - 31:30
    Thank you.
  • 31:30 - 31:31
    The next question we have is that if.
  • 31:32 - 31:32
    We're.
  • 31:32 - 31:34
    Providing a supplement to the patient
  • 31:34 - 31:37
    and they move out of
    categories Category 1
  • 31:37 - 31:39
    is there any evidence to show that
  • 31:39 - 31:41
    consuming more and not just ordering more.
  • 31:43 - 31:43
    >> Yes.
  • 31:43 - 31:47
    So on the dashboard at
    the bottom this some options.
  • 31:47 - 31:49
    And one of them is intake analysis.
  • 31:49 - 31:50
    So when you open that up on.
  • 31:51 - 31:51
    My.
  • 31:52 - 31:52
    Side.
  • 31:55 - 31:55
    I.
  • 31:55 - 32:01
    Just pull the sign up so I can talk you.
  • 32:08 - 32:08
    Kept.
  • 32:08 - 32:09
    Coming.
  • 32:10 - 32:11
    Up.
  • 32:13 - 32:13
    Well.
  • 32:14 - 32:15
    Yeah.
  • 32:15 - 32:17
    So looking at that side there.
  • 32:18 - 32:18
    So that's the.
  • 32:19 - 32:24
    Protein and energy contribution
    the red part of the bars is the.
  • 32:24 - 32:26
    Contribution from the supplement.
  • 32:26 - 32:29
    So if you open up the patient's
    file you can actually
  • 32:29 - 32:32
    see how much of the supplement
    they are consuming.
  • 32:32 - 32:36
    So for that particular example
    that I had the day I did
  • 32:36 - 32:40
    offer the sausage and it did
    provide more nutrition which.
  • 32:42 - 32:42
    Had.
  • 32:42 - 32:45
    Allowed the patient to move out a
    Category 1 but what had actually
  • 32:46 - 32:49
    happened is they were then
    Category 2 because they weren't
  • 32:49 - 32:53
    actually eating enough of energy
    and protein but these other
  • 32:53 - 32:57
    processes in place like embassy
    screening and the dieticians using.
  • 32:58 - 33:01
    The dashboard for what screening
    was also being present
  • 33:01 - 33:05
    in empty ts in hope that
    they will pick up a patient
  • 33:05 - 33:08
    like this to do further
    intervention and investigation
  • 33:08 - 33:11
    but yet generally can investigate
    how much they drink.
  • 33:11 - 33:12
    So.
  • 33:13 - 33:15
    >> Thanks.
  • 33:15 - 33:17
    We have a comment from Shannon
    Singh from he'll share
  • 33:18 - 33:20
    that it was interesting
    learnings from all sides.
  • 33:20 - 33:23
    And thanks so much for sharing everyone.
  • 33:23 - 33:26
    It's great to see that
    the dashboard is being used.
  • 33:26 - 33:27
    I agree.
  • 33:27 - 33:28
    That is good news.
  • 33:28 - 33:31
    >> I haven't received any other question.
  • 33:31 - 33:34
    A question I want to throw out there.
  • 33:41 - 33:44
    >> Making it easy or easier or better
  • 33:44 - 33:55
    or enhancing your bill be a clinician.
  • 33:56 - 34:00
    >> I think it's definitely
    enhanced our workflows.
  • 34:00 - 34:03
    I think previously you know if we wanted
  • 34:03 - 34:06
    some detailed information around patient
  • 34:06 - 34:09
    consumption you would
    have to implement some
  • 34:09 - 34:14
    food chart for the nursing staff to to do.
  • 34:14 - 34:21
    And I was always a fan
    of using the escapes
  • 34:21 - 34:23
    me in Cebu and you could turn on.
  • 34:26 - 34:26
    A.
  • 34:27 - 34:28
    Calorie count that's it.
  • 34:28 - 34:29
    Sorry.
  • 34:29 - 34:29
    Thank you.
  • 34:31 - 34:33
    But then you would need
    to bring the food cart
  • 34:33 - 34:37
    back from the board
    and input the percentages.
  • 34:37 - 34:41
    And you could print some reports
    out doing that if you wanted to
  • 34:41 - 34:46
    get you know I think called report
    around patient consumption.
  • 34:46 - 34:49
    But again you know you
    were relying on nursing
  • 34:49 - 34:51
    staff to be accurate and we all know that
  • 34:51 - 34:55
    the food the food charts
    by nursing staff were
  • 34:55 - 34:58
    rarely completed the way
    we would like them to.
  • 34:58 - 35:03
    So I think it's just really handy
    to be able to have access to that
  • 35:03 - 35:07
    data through either train monitor
    or the nutrition dashboard.
  • 35:07 - 35:13
    And I guess it's helped workflows
    and workload from that
  • 35:13 - 35:16
    perspective for both nursing
    staff not having to keep
  • 35:16 - 35:21
    the the food carts
    and the dietician not having to come back
  • 35:21 - 35:25
    and put those those percentages
    into the calorie counts.
  • 35:26 - 35:28
    So that's a bit of an advantage I see.
  • 35:28 - 35:31
    So malamute with me I would say that it
  • 35:31 - 35:33
    doesn't help all of them but as I said.
  • 35:34 - 35:36
    Some dieticians to.
  • 35:40 - 35:41
    >> Jewish patients.
  • 35:42 - 35:44
    So someone just.
  • 35:45 - 35:50
    >> Thinks that to me I as a clinician
    I have found it very valuable
  • 35:50 - 35:52
    and useful because you can use trained
  • 35:52 - 35:54
    well I tend to get that intake data
  • 35:54 - 35:56
    but myself I'm quite a visual person
  • 35:56 - 36:00
    so I like the the graphs and the picture
  • 36:00 - 36:02
    tutorial patients with the intake to
  • 36:02 - 36:04
    investigate the trainings of what's
  • 36:04 - 36:09
    happening and I think workflow wise
    maybe it hasn't completely helped
  • 36:09 - 36:14
    me because it does take more time
    talking but like I'm quite enjoying
  • 36:14 - 36:16
    investigating the point
    of what's happening
  • 36:16 - 36:17
    and trying to understand what's
  • 36:17 - 36:19
    happening with this admission and just
  • 36:19 - 36:20
    do a more comprehensive assessment.
  • 36:23 - 36:24
    >> Thank you.
  • 36:24 - 36:27
    I think just from my past
    experience and using it
  • 36:27 - 36:30
    it's not providing you all
    the information that's
  • 36:30 - 36:32
    providing a part of the puzzle to start a
  • 36:32 - 36:35
    conversation to look maybe
    in a different direction.
  • 36:35 - 36:36
    And I think anything you.
  • 36:36 - 36:39
    Add to that making the assessment more
  • 36:39 - 36:41
    comprehensive I think is valuable.
  • 36:42 - 36:45
    Just up on the screen now
    is the contact details
  • 36:45 - 36:48
    of the position so we're
    coming up to the time.
  • 36:48 - 36:52
    So I really like to thank again
    every presenters for their talk.
  • 36:52 - 36:55
    If it is apparent today I'd
    also like to thank all of
  • 36:55 - 36:58
    these innovations or practices
    that you'd like to share.
  • 36:58 - 37:01
    Please be in touch with me and we can
    work out a way of disseminating
  • 37:01 - 37:04
    it through the network because
    I think there's lots of gold
  • 37:04 - 37:08
    everywhere out there and what you
    would do it might really help some
  • 37:08 - 37:12
    colleagues improve a process or do
    something better somewhere else.
  • 37:12 - 37:14
    So thank you to everybody.
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