• of Imaging delays were due to Portering

    PORTERING MODULE: To optimise the co-ordination and delivery of a Portering service

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    For those who are very sick and require a CT scan the turnaround time is key for diagnosing critical conditions that require the correct treatment. The longer the CT delay the worse the patient outcome can become.

    Of Patients
    That enter ED have a CT.

    Of Patients
    often wait Longer than 3.5hrs for CT results.

    Case Study: Using the Portering service module to optimise portering operations, significantly reducing delays

    Medical staff at an NHS hospital felt that images were taking too long for some very ill patients, particularly during the busiest periods. It was difficult to predict how long images would take to turn around.

    There are nine steps involved in getting a CT scan in readiness for review by the treating Medical Officer. Over 20,000 CT’s were used to measure the time taken between each step.

    On interview the staff perceived the imaging department to be accountable for the big delays. However the data identified the transfer of the patient to the imaging department as being the largest delay. Deeper analysis of the transfer delays revealed poor processes for ordering, managing and distributing porter tasks in general, not just for CT’s.

    Our solution: How did we fix it?

    Patienteer provided a view of all porter tasks which;

    • Significantly automated the porter ordering process in the form of an electronic task list built within Patienteer, coupled with a touch screen for ease of use by porters
    • Tracked porters so staff know what task is being completed, and where they are.
    • Provided analysis that drove a new staffing model based on supply demand data.
    • Enabled Medical Officers to clinically prioritise which patient goes to imaging next.

    Outcome: How did we improve efficiency

    Within two weeks of implementing the Patienteer portering module an average reduction of 44% in the transfer task within the CT process was recorded. During busiest times the wait for CT reduced from 2.5hrs to 2hrs.

    Staff are empowered and enabled to keep track of and prioritise very sick individual patients, allowing quicker decision making for ED patients. They know at a glance where the patient is at all times and which porter is completing the task.

    Better communication between staff, coupled with real time data has improved the safety and experience of patients requiring imaging.

    The Portering module is so great. We don’t have to chase the porters anymore as we know Patienteer is tracking the porter workload and escalating when need be.

  • of COVID patients are optimally cohorted.

    BED & SITE MODULE: To facilitate faster bed allocation and patient moves

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    COVID 19 has imposed a new need to cohort patients more quickly and dynamically. Existing systems struggle to cope.

    Some patients deteriorate very quickly, making at a glance clinical stats and cohorting esential.

    Of COVID Patients
    Optimally located after day one of going live with Patienteer.

    Of COVID Patients
    Optimally located after week one of going live with Patienteer.

    Cohorting & Clinical Management of COVID 19 Patients

    The Head of Respiratory Medicine at an NHS hospital was tasked with leading the cohorting of COVID patients within the hospital. The immediate challenge was being able to visualise in realtime both the amount of suspected and confirmed COVID patients and their locations in relation to each other.

    The existing system was unable to cope with this new need to cohort patients dynamically. Given this challenge, some patients deteriorated much more quickly than would normally be seen with common pneumonia.

    The system needed to show clinical criteria info at a glance to allow cohorting and clinical management to be optimal.


    • To reduce patient mortality by optimising cohorting and clinical management of COVID patients
    • Enable the site team to make rapid and optimal bed allocation of suspected and COVID confirmed patients
    • Enable “eye in the sky” capability at a glance to inform teams of patients with worsening clinical parameters
    • Compile analysis of systemwide data to rapidly optimise patient flow


    Method: Our solution

    Patienteer worked with the Respiratory team and COVID ward doctors to develop a data driven solution for dynamic bed allocation coupled with real time clinical stats at a glance. The steps to achieve this were;

    • Digitise the existing whiteboard based solution to colour code bed allocation reflecting COVID status of patients based on Excel database
    • Collate microbiology results and populated the system to show the exact location of every suspected and confirmed COVID patient
    • Identify all of the clinical criteria related to COVID 19
    • Enable Medical Officers to clinically prioritise which patient should go where by presenting real time clinical and logistical data 

    Outcome: How did we improve efficiency

    The Respiratory team and COVID ward doctors have been able to carry out “eye in the sky” virtual reviews of real-time patient observation data enabling more timely interventions. These interventions have included recommendations regarding increasing the frequency of patient observations by nursing staff and, where necessary, direct referral to the Clinical Care Outreach Team. This function proved vital at a time when nursing skill-mix and numbers became sub-optimal due to workforce challenges.

    By the end of day 1 of our system going live 54.7% f patients were optimally located in our COVID wards. Exactly 1 week later 79.9% of patients were optimally located.

    The major benefit of this service module is early at a glance awareness of patient deterioration, helping keep them safe and to avoid further deterioration that can lead to ICU.

  • of admissions reduced average length of stay by 15.5 hours.

    MEDICAL TAKE LIST MODULE: Identifies urgency and tracks key workflows, ensuring timely reviews and prioritisation of patients.

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    The Acute Medical Take process was manual, time consuming and inefficient, resulting in delays to the patient journey

    the Acute Medical Take  process and workflow was inefficient and hard to track in real-time, preventing Site Management and ED from knowing the progress of the Medical patient journey at any one time. This created a lack of trust and transparency resulting in frustration amongst many departments and services.Junior Doctors felt this frustration often passed onto the Junior medical workforce, resulting in lower staff satisfaction, which also reflects in national literature and local GMC survey reports.

    found Patienteer to improve efficiency

    found that the Patienteer Medical Take List helped prioritise on clinical need

    Case Study: Using the Referral Take List Module to improve Urgency Identification and referral tracking

    • Referrals were entered manually into a spreadsheet, generating the medical take list (MTL), which was time-consuming, often resulting in transcription errors and further inefficiencies.

    • Lack of real-time location data also meant time was spent looking for patients.

    • The spreadsheet could only be opened one-at-a -time, with no access from other stakeholders, adding to frustration at not knowing the real-time status of patients.Patient Safety:

    • Patient triage was based on a brief entry in the spreadsheet, with no objective way of triaging based on clinical need

    • Patients could potentially be missed off the list due to transcription errorsResourcing:

    • A small-scale audit was performed which suggested the current roster was not aligned to the fluctuations in referral demand seen throughout the day

    • It was felt large scale data would consolidate the business case for staff rostering, which was difficult to perform with the current spreadsheet.

    How did we fix it?

    Over 3 months doctors involved in the medical take were consulted to discuss problems and potential solutions. A bespoke electronic MTL was refined and finalised using Patienteer. Junior doctors were asked to test the template and suggest changes. The final product allowed us to overcome previous challenges listed above.

    The new Real-time Medical Take List (MTL) in Patienteer extracts data from existing electronic health records and when each patient was referred; the data includes patient details, live location, live early warning scores, and captures the time at which the patient is assessed by the medical team, and when the patient is finally seen by a Consultant.

    The new MTL not only tracked patients through the AMT process it also presents live workflow data that identifies delays in the AMT workflow. This allows the Manager and lead Clinician on-call to have oversight of the pressures and wait times for each step along the AMT workflow. The result has been that the lead Clinician can re-distribute resources to remove delays in the AMT workflow for the sickest patient.

    The system pulled in many of its ‘Lean’ principles to track and prioritise patients and distribute key tasks that impacted delayed clinical decision making and flow.Prior to launch, several teaching sessions were held for relevant stakeholders.

    Outcome: How did we improve efficiency?

    To identify the impact of the new MTL, 6 months’ data was analysed (6344 patient encounters), with the following results:

    •High risk Patients are automatically prioritised by the system and seen quicker: The new MTL visibly shows live National Early Warning Scores (NEWS) and, patients flagged as possible sepsis, critically high NEWS shaded red. Patients presenting with higher NEWS are assessed more quickly (p<0.0002). There was also a trend towards patients with suspected sepsis being seen more quickly.

    •Insightful data that is driving change for future resourcing and AMT workflow management to reduce LOS: The data identified the need to review future consultant post-take ward rounds (PTWR). Only 34% of all admissions received a PTWR on the same day as being assessed by a junior doctor. Patients who waited overnight for PTWR had an average length of stay that was 15.5 hours longer.

    •Better resource management and planning: Supply matches demand during the day, including extra ‘twilight’ staff matching the evening surge in referrals. Demand exceeds supply overnight, however. As an ongoing pilot, on some weeknights, 2 clerking doctors were used instead of 1. The data indicates the time taken to see patients has been reduced from 164 mins to 94 mins as a result, a new rota is being designed to reflect this.

    •Improved working lives for Doctors doing the ATM:SHOs/Registrars and Consultants were surveyed, with 27 respondents. Overall, 89% found Patienteer to improve efficiency. Two-thirds found it helped prioritise on clinical need; within the subgroup junior doctors this increased to 92%.Overall 24/26 respondents would recommend using Patienteer.

    An unexpected benefit was it was possible to track how often individual doctors were involved in patients care and this can be used for portfolio and revalidation purposes.

    The new MTL has allowed us to track the exact location down to the bed number of a patient, and observe any changing NEWS to manage unwell patients.

  • Improvement in ED 4 hour performance

    RETROSPECTIVE ANALYTICS: Tools for Analysis of Workflow Delays

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    on a medical ward have an FBC each day

    on a medical ward have an LFT each day

    Improve efficiency of Phlebotomy resource Utilisation: Prioritise by Patient Care or Flow requirements.

    Phlebotomists are provided a roster of wards to attend each morning. The roster is driven by logistic location of the ward and order of patient bed number rather than by patient care or flow requirements.

    Scenario modelling identified a secondary cause in that there are not enough phlebotomist resources to take all the pathologies required in time for each ward round.

    If key pathology results were returned prior to THE ward round then earlier decisions could be made for the patient’s treatment, or whether the patient is clinically stable to be discharged.

    How did we fix it?

    Increasing the resource time of the Phlebotomists pre-ward round, through either an earlier start or an increase in staff, wasn’t identified as a viable option.

    The key issue was that some patients required blood results to be back prior to ward rounds compared to others; Those for which the result was returned satisfactory, meaning the patient could be discharged, or those for which results required high priority for patient care and a decision on the next stage of treatment.

    It has been agreed that, during the ward round, the Specialist would identify the high priority pathologies, and the phlebotomist would take blood from those patients first thing in the morning, before moving onto the less urgent patients.

    Improve efficiency of patient discharges over weekends: Prioritise by Outstanding task status.

    16 % of all patients discharged on a Monday had not seen a doctor or had an assessment over the weekend.
    A further 33% had no further diagnostic orders post the Friday and 50% had an order created & completed on Monday before discharge.

    How did we fix it?

    Introduced a Registrar on the weekend focussing on patients for discharge.

    Patienteer provides a list of all patients over the weekend that have no outstanding tasks, or should be prioritised for discharge on the weekend.

    Minimise patients in ED for 4 hours or longer: Optimise resources to prioritise likely discharges during high demand periods

    The resource mix of medical staff allocated to sub-acute areas was not optimised for patient demand. Over 30% of patients had additional diagnostics following a senior medical review of first round diagnostics.

    How did we fix it?

    The strategy was to introduce a senior consultant during hours of high patient demand to identify, diagnose and quickly treat those patients that are likely to be discharged home.

    Outcome: How did we improve efficiency

    Throughout the pilot period only 8% of patients under the care of the senior consultant were in ED over four hours. Across the same period for patients treated within the low acuity area only 22% were in ED over four hours.

    The 4 hr performance improved by 13%

    Now I’ll be able to send the Phlebotomist to those patients that require urgent bloods to be back before the ward round

  • of schedulers time spent on manual tasks which could be automated

    SURGICAL MODULE: Optimises every service involved in getting the patient to the operating table.

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    Automating Scheduling based on supply vs demand data can double the productivity of schedulers.

    Of Scheduling time
    Could be saved

    Increase in scheduling efficiency

    Case Study: Using the Surgical service module to optimise all aspects of scheduling, reducing delays by up to 50%

    Employing data from Cerner, we are able to optimise surgical scheduling so that patients’ surgery scheduling is within their CPC dates, synchronised with surgeon avaiability, optimised in terms of theatre utilisation and with timely information to the patients.

    Our solution: How did we fix it?

    Provides task/workflow management algorhythms ensuring surgeries are scheduled based on goals and data.

    • Ensure pre-requisites for surgery are done. Includes patient portal.
    • Automate referral to services.
    • Theatre utilisation based on supply and demand, including future-proofing, to per consultant or surgeon level depending on requirements.
    • Analysis and reallocation based on a real-time view of the impact on demand vs supply.

    Outcome: How did we improve efficiency

    Schedulers don’t have to waste time looking for slots to allocate to patients as Patienteer provides these based on supply and demand. Prerequisites are logged and displayed, saving schedulers time.

    Schedulers productivity doubles


  • of Long stay patients are…

    LONG STAY PATIENT LIST (TOC): To track and manage Long Stay patients within the hospital

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    The existing TOC list had been amended to include further information required to track and manage long stay patients during the Covid-19 crisis..


    Of Long Stay Patients
    Optimally located after day one of going live with Patienteer.

    Of Long Stay Patients
    Optimally located after week one of going live with Patienteer.

    Tracking and Management of Long Stay Patients 




    Method: Our solution


    Outcome: How did we improve efficiency


    The major benefit of this service module is early at a glance awareness of patient deterioration, helping keep them safe and to avoid further deterioration that can lead to ICU.