Practice Finance Workshop 1. Dr. Jay Verma Dr. Sukin Natarajan Dr. M. T. Fazal Data Care Solutions Ltd.

Transcription

1 Practice Finance Workshop 1 Dr. Jay Verma Dr. Sukin Natarajan Dr. M. T. Fazal Data Care Solutions Ltd.

2 Agenda Time Content Speaker 9.00 Registration 9.30 Welcome/Introduction Jay Verma 9.35 Importance of good data quality (medico legal) Maximise QOF payments (importance of prevalence) Jay Verma Tariq Fazal Break SNOMED coming soon Efficient use of clinical systems (EMIS Problem titles) Jay Verma Tariq Fazal Break Workshop session maximising enhanced service Contract, KPI, process, service delivery, data capture, reporting, claim, validation Tariq Fazal Close

3 About Us Welcome

4 Is good data quality important?

5 What is data quality? Confidence in the quality of the data Help create useful information to shape healthcare Correct and up-to-date data is critical to help coordinate care for patients Correct data can be used towards research, strategic and/or management of health services

6 Benefits from having good data quality? Provide an accurate picture of care Support decision making within your organisation and nationally Provide data for health care research which may lead to improved outcomes Claims!

7 Factors determining data quality: ACCURATE e.g. right code used for clinical condition or management plan RELEVANT e.g. right code used for whatever you are measuring VALID e.g. not use redundant codes and/or use at the wrong time RELIABLE e.g. consistency use within your organisation TIMELY e.g. immediate coding of letters as it enters your organisation

8 Importance of data cleansing and the potential for medico-legal claims Patient with HbA1C of 110mmol/mol for over three years but never coded as having diabetes Patient with asthma and requesting several inhalers every month but never had any regular follow up Patient with high blood pressure on medication but never recalled for annual reviews Patient with back pain and missed diagnosis of TB - importance of documenting Non-QOF codes

9 Thank you Questions?

10 Prevalence - why bother?

11 What is prevalence? The degree to which a condition is prevalent; especially : the percentage of a population that is affected with a particular condition at a given time. Prevalence of asthma in UK = Number of patients with asthma diagnosis in UK / total population in UK. Practice prevalence of asthma = Number of patients with diagnosis of asthma / total practice population

12 Why is it important? Adjustment Disease Prevalent Factor (ADPF) ADPF = Practice prevalence of the disease / national prevalence of the disease Contractor population index (CPI) = practice list size / national average list size Achievement Points (AP) - number of points achieved in the domain up to the maximum allowed (e.g. asthma 45 points) Value per QOF point (2018/2019) (VP) = QOF earnings for the domain = ADPF x CPI x AP x VP

13 Constant ADPF x CPI x AP x VP For example asthma National prevalence = 6% If practice prevalence is 5.5% then total payment for asthma = (5.5/6 ) x (5500 / 8096) x 45 x = 5, If practice prevalence is 6.5% then total payment for asthma = (6.5/6 ) x (5500 / 8096) x 45 x = 5,936.80

14 Increase in Prevalence = Increase in Earnings Prevalence of 5.5% in a practice population of 5500 is 302 Prevalence of 6.5% in a practice population of 5500 is 358 Having 56 more patients on the asthma register increases practice income by or increase of about per patient added to register. This is independent of how many points you achieve in each domain.

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16 Anticipated increase in QOF R 2 = 1.22

17 QOF earnings when compared with CQRS Heathrow Medical Centre Shakespeare Health Centre 75,000 70,000 Operational /clinical efficiency? 50,000 45,000 65,000 40,000 60,000 35,000 55,000 30,000 50, , QOF Acheivement QOF Max QOF Acheivement QOF Max

18 Asthma Register

19 Asthma Register

20 Asthma Register

21 Asthma Register

22 Asthma Register Patients with asthma resolved code as the final code are excluded from the register

23 Asthma Resolved

24 Search Algorithm

25 Statistics

26 Statistics Asthma UK Asthma affects 5.4 million people in the UK UK population is 66 million Prevalence is around 8%? (CQRS data suggests only 6%) Every 10 seconds someone is having a potentially life-threatening asthma attack in the UK. Everyday, the lives of three families are devastated by the death of a loved one to an asthma attack; tragically two thirds of these deaths are preventable.

27 Thank you Questions?

28 About Us Break

29 About Us SNOMED CT in Primary Care Why, what and when?

30 Why? Health Care Assistant (HCA) Clinical Correspondence Administrator General Practitioner (GP) Care Navigator (CN) Patient Advanced Paramedic Practitioner (APP) Administrative Clinical Pharmacist (CP) Advanced Nurse Practitioner (ANP) & Nurse Clinical

31 Acute Care Secondary Care Mental Health Dentistry Community Settings

32 What is SNOMED CT? International vocabulary of clinical phrases for use in healthcare systems Most detailed and multi-lingual clinical healthcare terminology in the world Mapped to other international standards Standardised

33 When? Read V2 was retired on 01 April 2016 CTV3 retired on 01 April 2018 Primary Care Secondary Care, Acute Care, Mental Health, Community settings, Dentistry etc. Social Care? 01 April April 2020

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35 Mapping from Read code to SNOMED Courtesy of NHS Digital

36 Thank you Questions?

37 Making IT systems work for you

38 Good Data = Good Clinical Care Medical Summary Time Clinical Alerts Data Quality Payments Sharing (SCR/HUB) Referral Audits

39 Understand Problem Titles Linked to many other EMIS features Good problem titles create accurate high quality records Important for Sharing with Colleagues/patients Audits Commissioning services Alerts Med3 Referral Clinical Summary Problem Title Connected Consultations Linked Medication Patient leaflet

40 Tidy Medical Summary Gives a quick overview of the patient Ideal when you are not familiar with history Automatically maintained

41 Problem title Ask yourself what is the underlying problem? Condition/Diagnosis/Working Diagnosis/Symptom Avoid all others problem titles STOP and Check Active or Past? Significant or Minor? New episode or Review? Duration indefinite or expected end Remember NO title is better than wrong title

42 Stop Check Problem Type

43 Multiple problems and linked medication

44 Consultation synonyms

45 Consultation Quick picks

46 Where is that document? STANDARD DOCMAN FILENAMES Efficient workflow for incoming documents Simple naming convention for documents Short names as possible Use of standardised department names

47 Examples of structured filenames [Date] [Description] [Organisation] [Department] 16 Jan 2017 Letter Harefield Hospital Cardiology 06 Mar 2017 Discharged Hillingdon Hospital ENT 17 Aug 2016 Unscheduled Hillingdon Hospital A&E 16 Sep 2017 DNA Community Physiotherapy

48 Document Naming Rules Template Name Description Organisation Department Folder Code Description 01. Hospital Letter Letter {Hospital} {Department} {Department} Seen in {department} 02. Community Letter Letter Community {Department} {Department} Seen in {department} 03. Discharge Summary Discharged {Hospital} {Department} {Department} 9b0B Discharge summary 04. Referral Letter Referred to {Hospital} {Department} {Department} 9b0g Referral letter 05. Request Request {Hospital} {Request/Result} {Request/Result} 8HU Referral for other investigation 06. Script Request Medication Script request 8B3H Medication requested 07. Results Result {Hospital} {Request/Result} {Request/Result} 9b0j Investigation result 08. A&E Unscheduled {Hospital} A & E A & E 9N19 Seen in accident and emergency department 09. OOH Unscheduled CareUK {Unscheduled} {Unscheduled} 9b0w OOH Report 10. Walk-in-centre Unscheduled {Hospital} Walk In Centre Walk In Centre 9N0x Seen in walk in centre 11. DNA letter DNA {Hospital} {Department} {Department} 9H4 Failed encounter 12. Admin Letter Admin {Admin List} {Admin List} 13. Emergency Admission Admitted to {Hospital} {Department} {Department} 8H2 Emergency Hospital Admission

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53 Quick consultations Standard procedures F12 Managed protocols by Staff Groups Quick Nurse codes (Smears, Prostap, Zoladex, B12, Insulin, GLP, DMARD, LHRH, TB etc) Quick Reception codes Quick GP codes Shortcut desktop Online calculators e.g. Frax tools

54 Good Data Quality = Good Clinical Care

55 Thank you Questions?

56 About Us Break

57 About Us Workshop