Microsoft Word - Review of the strengths and weaknesses of risk matrices (1) 1 Review of the strengths and weaknesses of risk matrices Mustafa Elmontsri Department of Primary Care and Public Health, School of Public Health, Imperial College London St Dunstan’s Road, London, W6 8RP, United Kingdom E-mail: m.elmontsri10@imperial.ac.uk www.imperial.ac.uk Abstract Risk assessment and risk matrices are powered tools used in risk management and help guide in the process of decision-making in organisations. Nevertheless, risk matrices have their own weaknesses and strengths. This paper provides a critical overview of the development and use of risk matrices in different field with an example of the risk matrix used by the National Health Service (NHS) in England. Risk matrices are helpful tools for risk assessment as they use quantitative measures to ensure consistent method of determining risk but organisations should adjust the design and size of risk matrices to suit their needs. Keywords: Risk Assessment Matrix, Risk Matrices, NHS risk matrix, quantitative risk matrix 1. Introduction All over the world, nations and organisations are attempting to reduce risks, to improve safety and to extend lives. Indeed risk reduction has become a principle goal of modern governments and almost in every organisation. It is obvious that people, including government officials, often lack risk-related information. They often know little about the nature and magnitude of the risks at issue, and they often know little about the various consequences of risk reduction (Sunstein, 2002). Since risk cannot be eliminated, the main problems people face, individually and collectively, are how much risk they should live with and how they should go about managing the risk. If a set of strategies have been chosen that will allow the abatement of a particular risk, the question of what level of risk should be chosen arises. If abating the risk costs nothing, the obvious answer is zero, get rid of the risk. But risk abatement almost always does cost money and time (Glickman and Gough, 1990). To answer these questions, analytical tools and risk ranking schemes must be used to distinguish lower risk activities / incidents from higher risk activities / incidents. One of the risk ranking methodologies is known as the risk assessment matrix. 2. Risk Management Risk management is the process of assessing risks and taking steps to either eliminate or to reduce them (as far as is reasonably practicable) by introducing control measures. Risk management refers to the process of reducing the risks to a level deemed tolerable by society and to assure control, monitoring, and public communication (Morgan, 1990). There are more questions than answers when people talk about risks. The career of the term ‘risk’ is a rather recent phenomenon, however (Fischhoff et al., 1984), states that, “risk has always been part of human Journal of Risk Analysis and Crisis Response, Vol. 4, No. 1 (March 2014), 49-57 Published by Atlantis Press Copyright: the authors 49 willieb Typewritten Text Received 5 July 2013 willieb Typewritten Text Accepted 29 January 2014 willieb Typewritten Text Mustafa Elmonts existence and as human bei own deaths dangerous sit The Internatio developed a distinguishes risk – for wh – and decidi management 2005). Fig. 1.IRGC’s 3. Definition There is no c risk – neit understanding common, how and possibilit The definitio (1992) is “the occurs during particular ch statistical risk probabilities” Fischhoff et a like that of inherently co affect the ou resources am of political po 4. Risk Asse All of the pe have to be m hazard. It fo sri d the field of ings started to and conte tuations”. onal Risk Gov a framework between ana ich risk appra ng what to d is the key risk governanc n of Risk commonly acc ther in the g. All risk co wever: there is ty (Renn, 1998 on of risk acc e probability t g a stated per hallenge. As k obeys all th ” al (1984), sta any other ke ontroversial. 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Risk as efining the edominantly chnical risk a at stake, calc onsequences, ultiplying the ffects (Kolluru isk assessmen pproaches thro ore traditiona at constraints anagement pe e public, and which risk as ox (2005), sta pplied to risk oduce subject sk only. He nk the risk fro e greater tha enarios. Whe cluded in the ubjectively. Whereas quant Cox, 2005), th umerically, all sk relative to sk measured o hese determin umerical scal uantitative ris ainly in the onsequence lev 2008, Ton athematical p sk tool often r atrix or, more evices come being manage he sources of h Boyle, one of s that a large h some obvi tical tools f re than a ce performed on ssessment is components quantitative t assessments, t culating the p and aggrega e probabilities u and Brooks, nt techniques v ough a regime al quantitative such as time erceptions, ris political pres ssessment are ates that whe k assessments tive and very l argues that q om one scenar an some oth en all the sce ranking, the titative risk he risk from lowing the an all scenarios on whatever sc nations can b les. Jeffery k assessment form of boar vels. ny Cox wrot problems asso referred to as e simply, a “ri in the form ed it will not hazards (Boyl f the problem number of ha iously more for risk asse entury before n technical sy the scientifi s of risk terms. It is a this means sp probabilities fo ting both co s by the mag 1995). vary from pure e of semi-qua e. Altenbach ( , money, man sk result com ssures all affe carried out. en quantitativ s, it can be limited relativ qualitative ju rio or group o her scenario enarios from ranking can assessment, each scenario nalyst to determ in the system cale of units ar be made obje (2006) state t may use so rd ranges of te about ser ociated with a s a consequen isk matrix. Ge m of qualitat be possible to le, 2002). ms with hazard azards will be serious than essment were e actual risk ystems (Renn ic process o in precise argued that in pecifying wha for (un)wanted omponents by gnitude of the ely qualitative alitative to the (1995), argues npower, skills mmunication to ect the manner e approach is considered to ve sense of the udgments may of scenarios to or group o a system are only be done according to o is estimated mine not only m, but absolute re chosen. ectively using es the semi ome numbers frequency or rious techno a widely used nce probability enerally, these tive or semi o d e n e k n, f e, n at d y e e e s s, o r s o e y o f e e o d y e g - s, r - d y e - Published by Atlantis Press Copyright: the authors 50 Strengths and weaknesses of risk matrices quantitative instruments in which hazards are first identified and then allocated to a box on a two- dimensional grid for which one axis measures the likelihood of a specific incident and the other the potential severity of consequences. The issues identified by Cox are certainly not confined to the United States, and indeed usage of risk matrices has spread in the United Kingdom and Europe from industry to all manner of public and private agencies ranging from hospitals to small- and medium-sized enterprises, local and central government bodies, and professional institutions 4.1. Risk assessment matrix A common method used for risk ranking utilises risk matrices; these are typically 4x4 or 5x5 matrices, having event consequences along one axis and event frequency along the other. Each block on the risk matrix represents some level of risk, and blocks presenting similar risk are often grouped together into one of four or five risk regions (Altenbach&Brereton, 1998) Risk matrix is defined as “a mechanism to characterise and rank process risks that are typically identified through one or more multifunctional reviews (e.g. process hazard analysis, audits, or incident investigation” (Markowski and Mannan, 2008), and is also defined by Cox (2008) as “a table that has several categories of “probability,” “likelihood,” or “frequency” for its rows (or columns) and several categories of “severity,” “impact,” or “consequences” for its columns (or rows, respectively)”. In most cases, the frequency axis of the matrix has numerical values associated with it, typically spanning several orders of magnitude. Often, the consequence axis is based on a qualitative scale, where consequences are judgment based. However, the consequence scale generally has implicit quantitative values associated with it, which may or may not be recognised. Risk regions are often arbitrarily assigned (or assigned on the basis of symmetry). This presents a problem in that if the blocks of the risk matrix are incorrectly grouped, then incorrect conclusions can be drawn about the relative risk presented by events at a facility (Woodruff, 2005).Three types of risk matrices are commonly used for risk ranking. A purely qualitative risk matrix will have its blocks defined in descriptive or qualitative terms. A purely quantitative risk matrix has its blocks defined in measurable or quantitative terms. Relative or absolute numerical scales are used on quantitative matrices, whereas scales on qualitative matrices are relative but not numerical. The third type of risk matrix is a hybrid: a semi-quantitative matrix with one scale (usually frequency) expressed quantitatively, while the other scale is expressed qualitatively (Emblemsvag and Kjølstad, 2006).iNTeg-Risk (2008) clearly states the importance of using scoring systems in risk assessment and management which generally requires the application of specific scores or scales. They highlight that in practical use, conventions such as using 5x5 risk matrices and/or a colour-code can be beneficial 4.1.1.Qualitative risk matrix The qualitative risk matrix is basically task and or hazard analysis with some relative judgments made in order to categorise the hazards. When the 3x3 matrix is used, both the frequency and consequence of each accident scenario are then estimated on simple relative scales, such as low, medium and high. The risk for each scenarios is the product of the frequency rating and consequence rating, this indicates that the qualitative risk in this case falls into nine distinct regions or frequency x consequence pairs: Low x Low, Low x Medium, Low x High, Medium x Low, Medium x Medium, Medium x High, High x Low, High x Medium, High x High. Clearly Low x Low region has the lowest risk, while the High x High region has the highest risk. The intermediate regions are more difficult to interpret because some regions are directly comparable and others are not (Altenbach, 1995) In the Environmental Protection Agency in the USA (EPA) technical guidance for hazards analysis adapted by DOE-STD-3009-94, the risk levels from the 3 by 3 matrix are grouped into three categories: High (Major Concern), Medium (Concern) and Low (No Concern), as indicated in the Figure.1 below, and also Table 1 shows the risk groupings from the EPA. Published by Atlantis Press Copyright: the authors 51 Mustafa Elmonts Fig. 2.Qualitat regions connec It is notable designing dir risk regions. a numerical highest. Som are denoted indicate that w with respect t risk of these may in fact b only relative connected by is risk grade 2 is grade 1, an And it is note have risk gra implied equ information c risk than Me Medium. Table 1. Risk g 1994) sri tive risk matrix cted by arrows ( in the figure rections from The relative r grade, with 1 me regions wi by prime (’ while they ha to nearby regi regions is no be significantl e when appli y the arrows. F 2 and is highe nd lower risk ed that High x ade 3 and 3’ uivalence b common to b edium x Low grouping from x: risk levels a (Altenbach, 199 e above, that lower risk re risk of each re 1 being the lo th the same n ’) and doubl ve the same r ions connecte ot necessarily ly different. T ed to those For example, er risk than Lo k than High x x Low and Me respectively, between them both is that th and lower ri EPA (US Depa are relative to 95) t the arrows egions to high egion is given owest and 5 numerical gra le-prime (’’) elative risk le ed by arrows, y equivalent, a The risk grade regions direc Medium x L ow x Low wh x Low (grade edium x Mediu but there is m. The on hey have grea isk than High artment of Ener the are her by the ade to evel the and e is ctly Low hich 3). um no nly ater h x rgy, Th inc reg in eq eq sec wh No reg 19 It De ab ca an to 4.1 Fe qu eff res tri ev the (C It co ha ris Al 4.1 No qu co dim all req as qu lab Ba arg ea as an he EPA gro consistencies, gions of diffe the same g quates risk gr quates risk gra cond type o hich are not d ote the Conc gions which 994). is also argu ecember 15, bility to ran ategorising the nd severity or inherently am 1.2.SEMI-qua ew serious ri ualitative appr ffort to enhan sults, many ed. There are ven though the e frequency Cox, Babayev is argued be omparison, qu ave very limit sk groupings ltenbach, 1998 1.3.Quantitati ot all hazardo ualitative eve onsequences sc mensionless u l regions in quirement fo sessments is uantitative risk bels, or at lea abayev and gues that by u ach accident s sociated with nd ranked. Co uping presen , the first typ erent and dire group. Note t rade 4 and 5 ades 1, 2 and f logical inc directly comp cern group co are not direc ued by Cox 2008), that r nk quantitat e two axes of probability an mbiguous risk antitative matr isk assessmen roach, due to i nce the usefu semi-quantita often referred ere is a quantit axis, consequ and Huber, 20 cause of the ualitative and ted value, it m of the blocks 8). ive risk matrix ous situations nt tree/fault cale quantitat units, relative n the matri or qualitativ soundness, ks should rec ast, should no Huber, 2005 using a quantit scenario will h it, then all s ox (2008), al nts two type pe of inconsi ectly comparab the Major C 5, and No C one region of consistency p parable in the ontains two r ctly comparab (personal co risk matrices ive risks c f the matrix (e nd consequen classification rix nts actually its limited use ulness of the ative scheme d to as qualita tative foundat uences axis, 005). limitations in semi-quantita makes no sen s (Cox, 2008: x need to be an analysis. By ive, even if o e risk can be ix. A basic ve and quan which states eive higher q ot receive low 5).Simmons e tative risk ma have a relati scenarios can so states that es of logica istency places ble risk grade oncern group Concern group f grade 3. The places regions e same group risk grades 3 ble (US DOE ommunication have limited correctly and e.g. frequency nce) often lead . use a purely efulness. In an e comparative es have been ative methods tion applied to or even both n making risk ative matrices nse to attemp Brereton and nalysed with a y making the nly in relative calculated for c consistency ntitative risk s that higher qualitative risk wer ones (Cox et al (2005) atrix approach ive risk value be compared t, for the risk al s e p p e s p. 3 E, n, d d y d y n e n s, o h k s t d a e e r y k r k x, ), h, e d k Published by Atlantis Press Copyright: the authors 52 matrix to be discriminate r risks, so that to focus risk m 5. NHS Ris The Nationa Kingdom rec and it is a t control measu grade or ra assessment m management of risk assoc incident. Fig matrix that is Fig. 3.NHS risk According to possible, the a predicted outcome. If t assigned to given time fr patient care e numerical pr descriptions w (www.npsa.n 6. Discussio Organisations defensible ran based on com risk ranking i frequency ax frequency pro e most usefu reliability bet it can be used management a sk Assessmen al Health Ser cognises the u tool used for ures to be put anking that matrix. An process is to iated with a w gure 3 below widely used b k matrix availa o the NHS r score of the li frequency of this is not po the adverse rame, such as episode. If it i robability then will determin nhs.uk) on s are recomm nking system mpany’s defin is the risk ma xis. 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The basic consequence a onsequence a um, low ool ited rix, what risk risk risk evel ace ment , If ing erse be n a or a ne a lity ore ard, ng, for and and Co dif tha rat cle eq be inc an ap ac de jud reg an lev It gra by mi wa qu qu In in the cla nu hid ha sta are ris loc mi Th bo ac Al est sce co ad en wi Ac co on onsidering tha fferent risk r at groupings c ting are not ear, in gene quivalent risk e found. To o cident scenar nalysed and ppropriate re ceptance leve efine the risk dgments base gion. On th nalyst open to vels to suit som is argued that asp and comm y examples of isunderstandin all which bloc ualitative rea uantitative real my experienc Appendix A& e consequen assifications a umerical estim dden agendas ave. The risk andardise the e involved in sk matrix mak cate the risk ight put them he NHS risk m oard and all tion that shou lso, the quant timates of r enario is sa ost/benefit tra ddress the per nough?” The ill easily justif ccording to C onsultants bel nly rough appr Strength at groupings atings are no consisting of logically con eral, logical from the qua obtain the bes rio is evalua placed on egion withou els or judgme acceptance le ed on the sc he other han o criticism fo me hidden age t the concept municate, even f probability, ng and misuse cks the jump f alm to the lm. ce within the & B, shows t nces and th assist all NHS mates of the s that the staf k matrix in t process of gr n the assessing kes it easier f grading with off completin matrix also as risk-owners t uld be taken de titative appro isk instead o afe. It can adeoffs of a rplexing ques increased uti fy the extra bo Cox (2008), M ieve that, wh roximate tool hs and weaknesse consisting of ot logically co regions from nsistent, one groupings o alitative risk st use of risk ated for the the risk m ut any pre ents. Then the evels by maki cenarios that nd this proce or adjusting th enda. of probability n though we a such as lotte e of the princi from the fuzz e precise y NHS sector, the possible d he likelihood S staff to be ab risk, while ff member/dep the risk asse rading the ris g of risks, ha for them to un hout any com ng the risk ass ssists the risk to decide on epending on th ach can addr of some fee be used to risk reducti stion of “how ility of quant other in many Many decisio hile risk mat s for risk ana es of risk matrice f regions from onsistent, and the same risk conclusion is of regions o matrix canno k matrix, each system being matrix in the edefined risk e analysts can ing subjective fall in each ss leaves the he acceptance y is difficult to are surrounded ery picks, the iples provide a zy comfortable yet uncertain Table 2 and 3 descriptors for d that, these ble to allocate avoiding any partment may ssment forms sk, as all staf aving a simple nderstand and mplexity which essment form managers, the n the level o he score. ress numerica eling like the analyse the ion plan and w safe is safe titative results applications. on-makers and trices may be alysis, they are es m d k s f t h g e k n e h e e o d e a e n 3 r e e y y s ff e d h m. e f al e e d e s d e e Published by Atlantis Press Copyright: the authors 53 Mustafa Elmontsri very useful for distinguishing qualitatively between the most urgent and least urgent risks in many settings and are certainly much better than doing nothing, for example, than purely random decision making. Donoghue (2001) also supports the idea that, the risk assessment matrices are effective tools in making decisions in regard to the control of occupational health risks. He states that, the control measures can be applied in an iterative fashion until the risk has been reduced to an acceptable residual. The imagery of risk matrices is powerful, which may, along with their alleged and apparent simplicity, explain their popularity among agencies that are responsible for mainly lesser hazards,1 and therefore are likely less qualified in risk, but who nonetheless feel the need to be seen to be proactive in managing risk. Inter alia, and as observed, though not sanctioned, in the new international guidance on risk assessment (ISO 31010), it is said that matrices are also widely used to determine if a risk posed by a given hazard is or is not acceptable. Ball and Watt (2013) also concur with Cox (2008) that one of the leading arguments in support of risk matrices, which is that they are simple to use and transparent, is false. As determined here, all positionings of hazards on the matrix are subject to innumerable considerations, some of which even the rater may not be wholly aware. Yet, and it is another serious matter, requisite explanations and justifications are seldom, if ever, attempted. It is this latter issue, of the consistency of use of risk matrices as applied to what are normally seen as beyond-the-workplace hazards. A growing number of authors, highly experienced in risk assessment, have questioned or had cause to investigate alleged shortcomings of risk matrices, mainly on technical grounds. In addition, standards-setting institutions have warned of the potential for subjectivity and inconsistencyas have researchers in occupational safety (Ball and Watt, 2013). 7. Conclusion Risk assessment and risk management techniques are being developed in many fields as an aid to safety investment decision making. Expanding responsibilities and limited resources compel policy makers to make difficult choices about the prioritisation of risk reduction measure and what safety standards to aim for. The need for mechanisms to help policy makers set priorities has been increasingly felt, and during the last few decades techniques of risk assessment and philosophies of optimisation have been developed. Risk matrices are very effective and widely used tool in making and improving risk management decisions, however the question of how ideally risk matrices should be constructed to improve risk management decisions is ongoing. It is not easy to answer, because risk matrices are typically used as only one component in informing eventual risk management decisions and also because their performance depends on the joint distribution of the two attributes probability and consequence. A risk matrix can be a useful tool to present the results of simplified risk analysis, helping one to gain insight into the relative risk of various scenarios that might be encountered in a given system. When developed quantitatively with axes constructed to be relevant to the facility and operations being studied, risk evolutions can be defined logically. Logic based risk evaluations can facilitate management decisions such as the authorisation of operations. It can also help optimise resources by showing where to concentrate efforts for more detailed analysis or for risk reduction activities. Using 3x3, 4x4 or 5x5 matrix, will be useful to some organisations and might not be for others i.e. when 5x5 matrix is used, the matrix will have 25 blocks (risk grades), the more blocks for representation, the more likelihood of the risk matrix producing different levels which would produce more risk ranking grades. Therefore, organisations would be able to allocate the low, moderate, high and extreme risk groups to the appropriate levels of responsibilities within the organisations. The wider options for the probability and consequence scores on a risk matrix should give more scope to differentiate within the risk group the probability of a certain risk occurring and the consequence of the risk occurring within the low, moderate, high and extreme groups for the different levels of responsibility. whereas by having 3x3 matrix, there will be only 9 blocks for the risk grades, which in some cases might not be useful when making decisions or allocating resources. However, if the descriptions of the consequence and likelihood scores are difficult to classify then the scores cannot always be well interpreted. For example, Table 1 Published by Atlantis Press Copyright: the authors 54 Strengths and weaknesses of risk matrices (appendix 1 NHS Risk Matrix), where it shows the consequence scores, by looking at the column where it says; Service Business Interruption; the difference between Major and Catastrophic scores; Catastrophic score leads the Business to a permanent loss of the business while Major score can only cause the business to be interrupted for one week. In such a case, the extreme description should be more than one week and permanent loss. Cox (2009) argues that risk priority scoring systems, although widely used (and even required in many current regulations and standards), ignore essential information about correlations among risks. This information typically consists of noting common elements across multiple targets (e.g., common vulnerabilities).These common features induce common, or strongly positively correlated, uncertainties about the effectiveness of different risk-reducing measures. It is easy to use this information, in conjunction with well-known decision analysis and optimization techniques, to develop more valuable risk reduction strategies, for any given risk management budget, than can be expressed by a priority list. Thus, there appears to be abundant opportunity to improve the productivity of current risk-reducing efforts in many important applications using already well-understood optimization methods.To sum up, risk matrices are a useful way of ranking risks, but organisations should adjust the design and size of risk matrices to suit their needs. References 1. 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The design of hazard risk assessment matrices for ranking occupational health risk and their application in mining and minerals processing. Society of occupational medicine. Vol. 51 No. 2, pp 118-123 8. Dietz, T., Scott Frey, R. and Rosa, E. (1996) Risk, Technology, and Society, in: R.E. Dunlapand W. Michelson (eds) Handbook of Environmental Sociology, Westport: Greenwood Press. 9. Emblemsvag, J. &Kjølstad, L.E. (2006), qualitative risk analysis: some problems and remedies. Management Decision, Vol. 44 No. 3 10. Fischhoff, B., Watson, S.R. and Hope, C. (1984) Defining risk, Policy Sciences 17, 123–29. 11. Glickman, T, S & Gough, M. (1990). Readings in risk. USA: Resources For the Future 12. Kolluru, R.V & Brooks, D.G. (1995) Integrated Risk Assessment and Strategic Management, in: R. Kolluru, S. Bartell, R. Pitblade and S. Stricoff (eds) Risk Assessment and Management Handbook. For Environmental, Health, and Safety Professionals, pp. 2.1–2.23, New York: McGraw-Hill. 13. Morgan, M.G. (1990) Choosing and Managing Technology-Induced Risks, in: T.S. Glickman and M. Gough (eds) Readings in Risk, pp. 5–15, Washington: Resources for the Future. 14. Markowski, A, S. &Mannan, M, S. (2008). Fuzzy risk matrix. Journal of hazardous materials. pp 152-157 15. Mcilwain, J, C. (2006). A review : a decade of clinical risk management and risk tools. Journal of Clinician in Management, Volume 14, No 4, pp. 189-199(11) 16. Renn, O. (1998) Three decades of risk research: accomplishments and new challenges, Journal of Risk Research 1 (1), 49–71 17. Simmons, J.Dwyer, S &Pfitzer, T. (2005). The RAC matrix: a universal tool or a toolkit. Journal of system safety. Vol. 41 No. 2. pp 14-19 18. Sunstein, C, R. (2000). Risk and reason: safety, law and the environment. England: Cambridge University Press 19. The Royal Society. (1992). Risk: analysis, perception and management. England: The Royal Society Published by Atlantis Press Copyright: the authors 55 Mustafa Elmontsri 20. The National Patient Safety Agency (www.npsa.nhs.uk) accessed on 26th August 2013 at 17:40hrs 21. The International Risk Governance Council. (2005). White paper on Risk Governance: towards an Integrative Approach, can be downloaded from www.irgc.org 22. US Department of Energy. (1994). DOE Standard preparation guide for US Department of Energy Nonreactor Nuclear Facility Safety Analysis Reports, Washington, DC, DOE-STD-3009-94 23. Woodruff, J, M. (2005). Consequence and likelihood in risk estimation: A matter of balance in UK health and safety risk assessment practice. Safety Science. 43, 345–353 24. Ball, D. J. and Watt, J. (2013), Further Thoughts on the Utility of Risk Matrices. Risk Analysis, 33: 2068–2078 25. Cox, Jr., L. A. (2009), What's Wrong with Hazard- Ranking Systems? An Expository Note. Risk Analysis, 29: 940–948 26. ISO (International Standards Organisation). Risk Management:Risk Assessment Techniques, 2009. ISO/IEC 31010.Geneva: ISO. 27. iNTeg-Risk (2013). iNTeg-Risk-Early Recognition, Mointoring and Integrated Management of Emerging, New Technology Related Risks (2007-2013), www.integrisk.eu-vri.eu Published by Atlantis Press Copyright: the authors 56 Strengths and weaknesses of risk matrices Table 2. The Consequence scores used by the (National Patient Safety Agency in England) Table 3. The Likelihood scores 1 2 3 4 5 Descriptor Rare Unlikely Possible Likely Almost certain Frequency Not expected to occur for years. Expected to occur at least annually. Expected to occur at least monthly. Expected to occur at least weekly. Expected to occur at least daily. <1% 1-5% 6-20% 21-50% >50% Probability Will occur in exceptional circumstances. Unlikely to occur. Reasonable chance of occurring. Likely to occur. More likely to occur than not. 1 2 3 4 5 Descriptor Insignificant Minor Moderate Major Catastrophic Objectives / Projects Insignificant cost increase/schedule slippage. Barely noticeable reduction in scope or quality <5% over budget/schedule slippage. Minor reduction in quality/scope 5-10% over budge/schedule slippage. Reduction in scope or quality 10-25% over budget/schedule slippage. Does not meet secondary objectives >25% over budget/schedule slippage. Does not meet primary objectives Injury Minor injury not requiring first aid Minor injury or illness, first aid treatment needed RIDDOR/Agency reportable Major injuries or long incapacity/disability (loss of limb) Death or major permanent incapacity Patient experience Unsatisfactory patient experience not directly related to patient care Unsatisfactory patient experience – readily resolvable Mismanagement of patient care. Serious mismanagement of patient care Totally unsatisfactory patient outcome or experience Complaint/Cla ims Locally resolved Justified complaint peripheral to clinical care Below excess claim. Justified complaint involving lack of appropriate care Claim above excess level. Multiple justified complaints Multiple claims or single major claim Service business interruption Loss/interruption > 1 hour Loss/interruption >8 hours Loss/interruption >1 day Loss/interruption >1 week Permanent loss of service of facility Staffing & competence Short-term low staffing level temporarily reduces service quality (< 1 day) Ongoing low staffing level reduces service quality Late delivery of key objective/service due to lack of staff. Minor error due to poor training. Ongoing unsafe staffing level. Uncertain delivery of key objective/service due to lack of staff. Serious error due to poor training Non-delivery of key objective/service due to lack of staff. Loss of key staff. Critical error due to insufficient training Financial Small loss Loss >0.1% of budget Loss >0.25% of budget Loss>0.5 of budget Loss >1% of budget Inspection/aud it Minor recommendations Minor Non-compliance with standards Recommendations given. Non- compliance with standards Reducing rating. Challenging recommendations. Non-compliance with core standards Enforcement Action. Low rating. Critical report. Major non- compliance with core standards Prosecution. Zero rating. Severely critical report Adverse publicity/reput ation Rumours Local media – short term. Minor effect on staff morale Local media – long term. Significant effect on staff morale National media <3 days National Media >3 days. MP concern (question in House of parliament) Published by Atlantis Press Copyright: the authors 57