Managing information health

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The slideshare is the first lecture in a series on Managing Information in Health by the Author at Kingston University London on the MSc Course. The topic of the first lecture was the management of information and the way data is presented.

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Managing information health

  1. 1. Royston E MorganBHM 303 Managing Information in Health Crosslight Management Slide 1
  2. 2. Module Objectives to develop an ability to understand and use information as a strategic resource in supporting the delivery of health and social care services. to provide students with an understanding of the changing role of information and communications technology (ICT) in the light of structural changes in the NHS and social care. to examine the enabling role of IT in facilitating communication and collaboration among professionals and patients in the health and social care sectors. Crosslight Management Slide 2
  3. 3. Session objectives Outline the module and rationale Discuss the use of information as a strategic resource in health Evaluate the role of information and related systems in health services Crosslight Management Slide 3
  4. 4. Can we distinguish Data Information or Knowledge? Data as a collection of facts Information as facts used to plan or to take an action Knowledge?Can it be also? Gossip? Intuition? Spreadsheets? Surveys? News reports? Crosslight Management Slide 4
  5. 5. Why do we need information? Advances our understanding of complex situations Provides warning signs Reduces uncertainty Helps us to provide appropriate solutions Offers historical evidence Aids communication Crosslight Management Slide 5
  6. 6. Reflect: what is meant when we say data is useful? Is it Can we discuss  accurate, some examples of  reliable, when these things do/don’t happen  relevant, and what the impact is?  timely,  Accessible,  etc?Let’s consider different perspectives ondata… Crosslight Management Slide 6
  7. 7. Much of the information managed by professionals is contextualised In the specific context of use In the actual case or problem being addressed The same terms in a different context can carry a different meaning In the specific mode of practice (can vary across countries for example) In the use of assumed (implicit) knowledge of the creator and user of the information Much recorded data by professionals assumes a background knowledge by the reader so that comprehensive exposition is not needed This can mean the use of the data is local to the situation and it can be difficult to use the same data for other things By using a constrained (understood) vocabulary Seen by acronyms or codes which can be very localised Crosslight Management Slide 7
  8. 8. Much of the information needed by IT professionals is specified Must be as far as possible generic Covering a broad spectrum of uses The same terms must be used in the same way (may imply practice change) but … …Different modes of treatments must be acknowledged The use of assumed knowledge by users makes ‘its’ use in systems complex For interpretation or use outside the specific clinical context the assumed knowledge may have to be declared Data must be comparable across the organisation so that meaningful analysis and comparison can be made (so how and who?) By using a open (codified) vocabulary Data elements (codes) are defined in data dictionaries for example to avoid ambiguity Generic free-text entries are a ‘no-no’ to most IT developments Crosslight Management Slide 8
  9. 9. Much of the information managed by large organisations created at A is needed at B Health Care workers create and manage information for their use at the point of use Managing the clinical trajectory through diverse departments (for example using the Patient Record) To coordinate the professional task… …and is heavily contextualised and collective For other consumers of the information Supplemental data is needed to make ‘it’ understandable and useable Information can only be added at front end by people who may find no value from doing so A core issue in managing information in organisations is getting ownership of data Crosslight Management Slide 9
  10. 10. So what do we think is information in Health Care? Lets first discuss and draw-up a list in two groups first then plenary. You are a clinical practitioner or a manager at an acute hospital What information do you think you might need to manage care? What information do you think you need to manage the organization? Crosslight Management Slide 10
  11. 11. Benchmarking in the health sector is a structured approach to sharing and comparing practice…. Figures on their own are often not informative How good or bad are we compared with others? If others are doing better, can we find out why? If we are doing really badly relative to others, can we change? What might inhibit us from improving further? National standards may seem ‘imposed’ but mostly aim to improve quality Crosslight Management Slide 11
  12. 12. A benchmarking process1. Agree focus2. Set baseline3. Describe best practice4. Assess current position5. Compare (and share to reach consensus on target)6. Determine Action Plan 1. Review and revise7. Tell ‘everyone’ Crosslight Management Slide 12
  13. 13. SCORING A BENCHMARK WORST BESTPRACTICE PRACTICE Worst STEPS TOWARDS BestPractice BEST PRACTICE Practice E D C B A 1 2 3 4 5 Crosslight Management Slide 13
  14. 14. But where did the information come from? Accuracy and precision of data sources? How up to date is the data? Are the samples similar to your organisation? Are different types of organisation benchmarked or is it across the board? Is there sufficient information about data collection, sampling etc., for you to know? Are other sources and/or references cited? Who has assessed data quality? Crosslight Management Slide 14
  15. 15. National Service Frameworks – information only part of the story Information Expectations through & skills Role of care technology Attitudes to professions private care Older OlderPeople NSF People 2001 2011 Public Medical Assistive attitudes Developments technologies to age Government policies Crosslight Management Slide 15
  16. 16. Is presenting (or collecting) data a neutral act?Presentation of data is concerned with three parts: Selection of relevant data Representation of data Purpose of presentation Crosslight Management Slide 16
  17. 17. Example: CHD NSF information processes  Obligation is for (virtual) registers established CHD evidence of non-cardiac arterial disease Heart failure plus CHD risk factors  Information Strategy addresses patients, carers and the public health professionals delivering care clinical governance, performance mgt, service planning, public healthCHD NSF : national service framework for coronary heart disease Crosslight Management Slide 17
  18. 18. Primary Care Trusts (now GP’s I think) and CHD What information do GP’s need? What do they need to know about CHD? From where can they get this information? How do they know if it is reliable? http://www.chd.org.uk/intro-nsf-intro.htm And why is it needed what is the purpose? Crosslight Management Slide 18
  19. 19. Dental Survey Statistics 2007 versus 2006 12% brush a few times a week or never Only 30% say they brush for two minutes 17% cant remember when they last changed their brush 60% of people would share their brush with their partner, child, friend or favourite celebrity … And13% of respondents from Newcastle from East Enders compared to 76% in Nottingham brush for the 2 mins recommended! Crosslight Management Slide 19
  20. 20. And the strange things people floss with: Drill bit How should Saw you interpret Shoelaces this? Hammer Fish bones Fork Twig Safety pin Toe nails Crosslight Management Slide 20
  21. 21. Survey concerns include Response rates Sample and respondent bias Validity Reliability Imposing concepts on to the subject Assumptions around participant interpretation Their desire to find meaning and either help or outfox the researcher Scales and measurements The power of reporting statistics… Crosslight Management Slide 21
  22. 22. Validity in surveys Construct validity vital (do we really measure what we mean to) Wording (avoidance of leading, loaded, double-barrelled or confusing questions) Response bias Social desirability Respondent interpretation of questions ‘Face’ Validity also important for responses Ordering of questions (can randomise with online versions) Predictive validity – hardly every discussed! Crosslight Management Slide 22
  23. 23. Reliability in surveys Pilots (with full feedback and modify) are vital Test-retest (but, time and experience of previous survey may have changed) Split half (can only be done with some types of instruments). Internal scales (Cronbach’s alpha) but remember this only means that each scale is measuring a similar thing… Crosslight Management Slide 23
  24. 24. Beware of how data is presented70605040 East30 West North2010 0 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr Crosslight Management Slide 24
  25. 25. Check the scales etc. 70 65 60 55 50 East 45 West 40 North 35 30 25 20 1st Qtr 2nd Qtr 3rd Qtr 4th QtrWhat else is wrong with this chart? Crosslight Management Slide 25
  26. 26. Be aware of relevant propositionsConsider the statement ‘Chimpanzee DNA is 99.7% the same as Human DNA’What does this statement mean what inferences can be drawn? Crosslight Management Slide 26
  27. 27. Be aware of relevant propositionsDo chimpanzees make cars/houses/PCs/ or give lectures in Information Management that are 99.7% as good as those made by humans? Or…A lot of DNA is not involved in the development process and this isbeing included in measurements Or …A small change in DNA has a large impact on what is produced Crosslight Management Slide 27
  28. 28. Be very aware of Statements of the form: A is the greatest cause of B In the UK car crashes are the single greatest cause of deaths among males in their 20s and 30s This is meaningless as there is no reference with which the scale the statement The purpose of the statement is to create an atmosphere of severity – and something must be done! It is at best not justified or at worst incorrect The Data… Crosslight Management Slide 28
  29. 29. What does the data tell us? The underlying life expectancy data shows that young people have very little chance of dying and death rates are uniformly very low after the first year of life until about age 50. So a statement such as ‘Car crashes are the greatest cause of deaths among males in their 20s and 30s will inevitably be true because nothing else really kills young males. Death due to illness is uncommon among this group so any other cause will dominate. Crosslight ManagementWith acknowledgement to Alan McSweeney alan@alanmcsweeney.com Slide 29
  30. 30. When thinking about how data is presented in the form of statistics Correlation is not causality Number of drunks in a town and number of Conservative Party members Significance tests generally flawed Look carefully at sampling and method You will learn much more about this in research methods too – but it is not only about your own research – we are bombarded with statistics these days…critique them carefully and remember our session on risk! Crosslight Management Slide 30

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