Houston, we have a problem! Using live data to warn of current and upcoming issues in hospitals
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Using live data to warn of current and upcoming issues in a busy tertiary hospital. Presented by Kenny Daly & Cath OโNeill, Canterbury DHB, at HINZ 2014, 12 November 2014, 11.15am, Marlborough Room
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Houston, we have a problem! Using live data to warn of current and upcoming issues in hospitals
- 1. Using Live Data to Warn of Current or Upcoming Issues in a Busy Tertiary Hospital Kenny Daly โ Information Analysis Manager, CDHB Cath OโNeill โ Business Systems Analyst, CDHB HINZ 2014
- 3. Weekly Extracts from 2 PMS into a flat table 2007 Dimensional model 2009 Weekly Extracts from 3rd PMS added 2011 Live messages from PMS 2014 Operations Centre Static Reports Cubes Dashboards Warn of current & upcoming issues
- 4. o Complexity of 3 main PMS o Ensuring we have an accurate copy of the PMS data o Missing records o Processing order: o New records o Updates o Deletes o Source system stalls o Processing time
- 5. 150 100 50 300 200 100 0 Count of Messages from Emergency Department module Count of Messages from Inpatient Module 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 Monday Tuesday Wednesday Thursday Friday Saturday Sunday 1200 1000 800 600 400 200 0 Count of Messages Patient Demographic Module 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 Monday Tuesday Wednesday Thursday Friday Saturday Sunday 0 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 Monday Tuesday Wednesday Thursday Friday Saturday Sunday
- 6. The basic function of monitoring live data is to provide information that allows decision makers to reduce current and short term โpressureโ on the system. โPressureโ results from insufficient capacity, blockages in flow or high demand, a variety of measures can be identified for each area. ๏ CAPACITY ๏ FLOW ๏ DEMAND
- 7. We are looking to identify issues that create โpressureโ on the system ๏ RECENT TRENDS ๏ CURRENT STATE ๏ FUTURE STATE (next 24-48 hrs) ๏ All of these should be compared to an expected level, plan, capacity or โtriggerโ
- 8. The basic premise is that interested parties should not have to hunt for issues, therefore delivery where possible should be targeted and automated ๏ TARGETED ๏ AUTOMATED
- 12. ๏ Nice dashboards that identify issues together with effective communication routes are not enough. ๏ The issue must be delivered to someone who has the authority or ability to be able to alter the situation. ๏ There should be a remedial action plan, how would someone not familiar with the workings of the hospital know what to do? ๏ All users must know that the issue is being addressed, this needs to be communicated.
- 13. ๏ Expanding the list of measures monitored. ๏ More science applied to when an issue is triggered, especially relating to combinations of measures. ๏ Improved usage of delivery such as texts. ๏ Enhanced predictive models for short term forecasts. ๏ Creating a โHospital at a Glanceโ view specifically for Duty Nurse Managers. ๏ Expand datasets to cover โwhole of systemโ.
- 14. Interested in finding out more?, please contact; kenny.daly@cdhb.health.nz cath.oโneill@cdhb.health.nz
Editor's Notes
- Originally the title of this presentation had an ! But this picture represents Tom Hanks very calmly saying โHouston, we have a problemโ. When we started to prepare this presentation, we realised that in fact the work carried out by our department at the Canterbury District Health Board actually helps to maintain control.
- Operations Centre. The room is full of electronic reports Empower: informed decisions :efficiently. frequently automatically refreshing with near-real-time data from a data warehouse. These near-real-time reports have enabled our hospital management to pre-empt and mitigate problems in our hospitals rather than just reacting when a problem already exists. Many departments contribute content The data warehouse is maintained and some of the reports are created by our department I will present on What changes the data warehouse has gone through to enable this Kenny will then take over and tell you Why we wanted to provide this information What we monitor And how we deliver it.
- Only recently able to produce near-real-time reports DW major transformations to enable this Complexity of more than one main source system Weekly extracts Flat table Static reports Good butโฆ Slow โ 2003 LOS 5 steps 1-15mins React to what happened a week ago Dimensional model Key measures precalculated. Access LOS in split seconds Sliced and diced by many attributes e.g department, ageโฆ Live data Frequently automatically updating reports Operations Centre Tools for hospital management Pre-empt and mitigate problems
- Llive data from the Source Systems into the data warehouse via messages These messages are triggered when a new record is created, changed or deleted. many challenges in receiving these messages and how to process highlight a couple what order to process the messages in. types of xmls are received : ADD, UPDATE and DELETE. If processed in the wrong order: too many records, not enough records or out of date records. bunch of different scenarios: wrong order or at exactly the same time. Compared to source system. Time and time again we discovered scenarios where we would have to rearrange the messages in a particular way. Every time we solved the problem for one scenario we would find another! Note about why not HL7 Homer does have an HL7 interface but not all systems are HL7 compliant and we needed to have a common solution.ย XML was easy to setup for Homer, not to mention free, and it is very flexible to change and finally not all our messages needed to mimic an HL7 message.
- processing time When running normally: processing time good. processes that occur in the source system:spikes of messages processing: delayed. nice new shiny server Pass over to Kenny how much we have had to change to enable near-real-time reports Kenny: why, what, how
- Thanks Cath, Cath has talked around some of the technical aspects of how we collect live data but why do we want to monitor this? Read slide paragraphs Examples of CAPACITY are; - Beds - Theatres - Staff Capacity measure examples; FREE BEDS PATIENTS NOT IN HOME WARDS PATIENTS IN ED ED PREDICTED ADMISSIONS STAFF (NURSES, DOCTORS, ALLIED HEALTH) Examples of FLOW are the numbers and time; Patients waiting to enter the system (e,g. in ED waiting for a bed) Waiting for a procedure (e.g. in the acute theatre queue) Waiting to leave (allied health input, final discharge decisions) Flow measures examples; WAIT TIMES IN ED PATIENTS > 10 DAYS IN HOSPITAL ACUTE THEATRE QUEUE EXPECTED DATE OF DISCHARGE Examples of DEMAND; Persons turning up at ED Referrals coming in for FSA Demand measure examples; ED ARRIVALS
- Once we have established the measures we then need to look at what are we interested in seeing in relation to that measure Recent Trends How is our recent activity trending?, even though our current state is fine we may have increasing activity or highly variable activity. Current State Are we currently managing the system efficiently? Future State Given the current situation what is the immediate future looking like?, are we heading into trouble? Comparisons provides some context and an indication of whether we should be concerned at the level the measure is at; Expected level โ what the normal level for this time of day and day of week based on historical data Plan โ do we have a plan that we need to meet e.g. ED 95% < 6hrs Capacity โ this is our upper limit and may be physical or resource. Trigger โ Capacity notification is too late. At what level do we start indicating a problem e.g. 5 free beds?, 3 free beds?, 1 free beds?
- Just as important as working out measures and how to identify when there is an issue is the communication of this to relevant people. Different audiences have different requirements in terms of delivery and detail, we have attempted to instigate a tiered approach to issue delivery; TARGETED CENTRAL LOCATION FOR ACCESS TO DATA VIEWS DASHBOARDS SHOWING DIFFERENT VIEWS RELEVANT TO AUDIENCE AUTOMATED EMAIL TEXT
- Now I will show you some examples of the content we have created. This is a high level triggers dashboard produced for the Operations Centre showing some measures relating to capacity and flow for some of our high activity departments. The advantage of this view is you donโt have to hunt for issues, it tells you in plain English what the issue is. There are two trigger levels, one set a lower level that indicates a potential issue could be arising, the other an immediate action required trigger meaning we have to address the issue now. With adequate management of potential issue arising there should not be many triggers that get classified as immediate action. I mentioned a tiered approach to delivery , we are currently developing a view above this for a service manager that has a simple dial as to whether everything is working according to plan. The detail should be greater the further you get to the โshop floorโ. I would like to show you some examples of what we produce for ED at greater operational levels.
- This view specifically for ED is a level down and shows; patient arrivals for the day by their referral source, it then shows which of these patients are still waiting in ED and at what stage of the process they are at together with wait times. then details are given on where patients have gone and how long they waited. A 48 hour history is displayed to provide some trend data. ED themselves have shown an interested in this as a summary as well as senior management and other operational staff. We are currently working on forecast of admission expected from current ED cohort to give a view of anticipated activity in the hospital to come from ED.
- This ED view is in even more detail and at first glance this view seems complicated but for those used to reading it they know what to look for. This view is basically for ED operational staff and operations manager, it is most useful on the โshop floorโ. It shows current patients by ED location together with wait times and it also breaks down in detail the patients referred to another specialty with wait times. Note the context on the bottom left trend graphs. Included in this view is detail of the ED overload score which uses an algorithm to calculate how much pressure the ED is under. The ED Department has an EDOD plan which involves at its very basic level moving staff and closer contact with DNMs around beds. We are currently working on a version of this which contains many of the same metrics but viewing by shift allowing clinicians can take greater ownership of the performance during their period of clinical responsibility.
- I mentioned earlier around delivery of message options. Now letโs assume we have a suite of dashboards with useful information and good communication of issues. These is not of use unless someone acts on it. Therefore we need to deliver to the right person. This is someone who can do something about the issue, this person ideally would be involved in the development process so they have has a say in what measures are useful and optimum delivery method for them. A lot of information around actions is held in peoples heads, need to get that down on paper so in theory anyone can go into the Ops centre or DNMs office and fulfil the basics of that function. The EDOD plan is an example of a remedial action plan. How do interested parties know the issue is being addressed, its good to know what is happening, together with an update of latest action and expected resolution is useful for everyone in the system.
- The previous discussion has given you an idea of what we currently provide and how we do it. This type of development can be never ending as refinement is always possible, so what's next for CDHB in terms of continuously improving what we present Science; Measure combinations - X people in ED waiting for Gen Med Dr, X people in ED waiting for a GM bed, X free beds in AMAU, X people over 10 days LOS, X people due out today = a problem? Predictive Models; I have already talked about forecasting of ED admissions Acute queue related to time left in session Spotting times when we consistently get into trouble using the data were are already reporting Patient movements HaaG; Targeted to audience Whole of System; Primarily hospital currently. Technically challenging but if we could get some 24 hrs or drop in surgery data this would be of significant use.