Patient identification. Telehealth

Transcription

1 Patient identification Telehealth

2 2 Tunstall healthcare advisory services

3 Return on investment (ROI) indicators To ensure the telehealth service meets or exceeds the business case upon which it was procured in an appropriate time period, appropriate patient selection is crucial. If the wrong patients are selected against the defined business case then clearly this will put the return on investment (ROI) for telehealth at risk or extend the time frame to which the ROI is realised. Traditionally the business case for telehealth is based upon reducing secondary care activity, particularly non elective episodes, improving operational efficiencies within Primary and Community Care and increasing patient quality of life. In summary, the broad criteria for suitable patients can be classified into: Clinical appropriateness Risk of secondary care admission High cost user of all health care services The following markers are generally used to help support initial patient selection although this will be guided by the time frame for realising the ROI. They may also be used in a scoring/weighting type system i.e. if the patient has a number of the below markers they score enough points to qualify for telehealth. This is particularly useful if the telehealth service doesn t have enough units to cover all eligible patients or local prevalence figures. Criteria: Has a relevant LTC i.e. COPD, CHF, diabetes or combination of. This may also be based upon on severity classification of condition e.g. NYHA III and above or MRC dyspnoea score The number of emergency admissions within a defined time frame, usually 12 months i.e. patients would be eligible for telehealth if they have had 2 relevant emergency admissions in the last 12 months Frequent user of OOH services i.e. more than three contacts in a month High intensity practice patients (who have one of the conditions telehealth is being used to support) i.e. Patients requesting regular practice visits or home visits to support them in the management of their condition Patients who live alone Patients who live in very rural areas Patients who frequently access OOH or acute services due to anxiety concerns (related to their COPD, HF condition etc) Patients who have difficulty following medication regimes 3

4 Weighting system An example of a points based scoring system as mentioned above could be as follows: 2 hospital admissions either to ACU or ward per year Patient lives alone Patient lives in a rural area Patient requiring 2 courses of antibiotics or oral steroids for an exacerbation within 12 weeks Patient requiring an increase in diuretic treatment for decompensated heart failure Patient requiring weekly visits by the Community Respiratory Team Patient requiring weekly visits by heart failure nurse 2 points (per admission) 1 point 1 point 2 points 2 points 2 points 2 points *The higher the score the higher the priority for referral to telehealth. In addition to overall patient selection criteria, there are additional clinical parameters related to the patient s condition which may help clinicians to select appropriate patients for telehealth. These could be as follows: 4 Tunstall healthcare advisory services

5 Key clinical parameters for COPD patients Clinical parameters Based on 2010 NICE clinical guidelines 101: Post bronchodilator FEV1/FVC < 0.7 < 0.7 < 0.7 < 0.7 FEV1 % predicted > 80% 50-79% 30-49% < 30% Post bronchodilator Stage 1 - Mild * Stage 2 - Moderate Stage 3 - Severe Stage 4 - Very severe ** *symptoms should be present to diagnose COPD in people with mild airflow obstruction. ** Or FEV1 <50% with respiratory failure. FEV1 results: Illustrates the severity of airflow obstruction (searched via GP clinical system/case finder search) GMS register for COPD, Emphysema, Pneumoconiosis Community matron/nurse/copd nurse specialist patient caseloads Frequent admissions with COPD as the primary diagnosis Patient requiring 2 courses of antibiotics or oral steroids for an exacerbation within 12 weeks Medication search related to COPD plus medication e.g. short or long-acting bronchodilators (beta2 antagonists), corticosteroids and long or short-acting muscarinic antagonists, nebulisers, Anti-cholinergic Tiotropium Bromide (Spiriva); Ipratropium Bromide (Atrovent), Steroidal inhalers e.g. Becotide, Oral steroids: Prednisolone; Xanthines e.g. Theophylline (a key word search for COPD would be useful to avoid asthma patients) Patients on LTOT (long-term oxygen therapy) COPD - confirmed diagnosis 18 years Nursing home or residential home residents with COPD. Consider MyClinic Mild MRC Scale I Moderate MRC Scale II Moderate Severe MRC Scale II/III Severe MRC Scale III/IV FEV1 80% FEV % FEV % FEV1 < 30% if > than 2 exacerbations in last 12 months. OR, Consider if ACG RUB change from 3 to 5. Frequent GP appointments. Frequent admission to hospital and/or A&E attendances. Check patient on optimum treatment (NICE guidelines). Consider case management services if available. Frequent GP appointments. Frequent admission to hospital and/or A&E attendances. Check patient on optimum treatment (NICE guidelines) Refer to case management services if available. Frequent GP appointments. Frequent admission to hospital and/or A&E attendances. Check on optimum treatment (NICE guidelines). Refer to case management services if available. Remember - Unstable asthmatics may be suitable for telehealth * Possible contraindications: palliative end stage illness, unsupported mental health issues, known inability to cope with telehealth kit, cancer patients receiving treatment MRC - Medical Research Council Dyspnoea symptom severity scale 5

6 Clinical parameters Key clinical parameters for heart failure patients Based on 2010 NICE clinical guidelines 108: Peptide NT ProBNP NT ProBNP Or, BNP BNP Result Range (pg/ml; pmol/l) pg/ml or pmol/l 2000 pg/ml or 236 pmol/l pg/ml or pmol/l 400 pg/ml or 116 pmol/l Severity Modorate / high risk Very high risk Modorate / high risk Very high risk NT ProBNP or BNP Results: These are natriuretic peptides that are released in the heart which illustrate severity of heart failure/ damage. NT ProBNP has a longer half life in the bloodstream so often reflects a more accurate result ECHO search results : Moderate, Moderate - Severe, Severe LVSD (left ventricular systolic dysfunction) (Searched via GMS contract: HF register/clinical system case finder) Patient requiring 1 or more increases in diuretic dose within one month GMS register search for heart failure, left ventricular systolic dysfunction, right ventricular failure, severe aortic or mitral stenosis/regurgitation Medication search i.e. Spironolactone*, Epleronone, Metolazone*, ACE Inhibitors/ARB, Beta-Blockers, those on Digoxin* in addition to above. ACE/ARB & BB should be searched with key word - heart failure to avoid all hypertensive, post MI and angina patients being identified. (*often sicker patients) HF nurse specialist/community matron/nurse caseloads Heart Failure Confirmed diagnosis 18 years Nursing home or residential home residents with HF. Consider MyClinic Mild NYHA Class I Moderate NYHA Class II Moderate Severe NYHA Class II/III Severe NYHA Class III/IV Not suitable Unless had 2 or more episodes of peripheral oedema. and/or Unless intensive treatment titration required (NICE guidelines). Frequent GP appointments. Frequent admission to hospital and/or A&E attendances. History of peripheral oedema and/or breathlessness. Other comorbidities (e.g. COPD, Diabetes). Check on optimum treatment (NICE guidelines). Frequent GP appointments. History of peripheral oedema and/or breathlessness. Frequent admission to hospital and/or A &E attendances. Other co- morbidities (COPD, diabetes) Check on optimum treatment (NICE guidelines). Refer to heart failure nurse specialist services if available. Frequent GP appointments. Frequent admission to hospital and/or A&E attendances. History of peripheral oedema and/or breathlessness. Other co- morbidities (e.g. COPD, diabetes). Check on optimum treatment (NICE guidelines). Refer to heart failure nurse specialist services if available. Remember patients with valvular heart failure with oedema can use telehealth * Possible contraindications: palliative end stage illness, unsupported mental health issues, known inability to cope with telehealth kit, cancer patients receiving treatment Remember those with a moderate-high NTPro- BNP/BNP result convey a worse prognosis/risk of event NYHA - New York Heart Association Classification of symptom severity 6 Tunstall healthcare advisory services

7 Key clinical parameters for diabetes Mellitus Type II patients Clinical parameters Based on 2009 NICE clinical guidelines 87: Molecule HbA1c Result Range 6.5mmol/L HbA1c results: these results illustrate the average glucose reading over the proceeding three months. A glucose molecule attaches itself to a haemoglobin molecule within a red blood cell GMS register for diabetes mellitus type II, diabetic neuropathy, diabetic complication Seach using case finder on clinical system for above key words Diabetes nurse specialist/community matron/nurse patient caseloads Retinal screening, neuropathy check, podiatry check lists Medication searches linked with diabetes code e.g. Metformin + Sulfonylurea, Metformin + Sulfonylurea + Insulin, additional therapies/alternatives e.g. Thiazolidinediones, Gliptins, Exenatide Diabetes Mellitus Type 1 or 2 Confirmed diagnosis 18 years Nursing home or residential home residents with diabetes. Consider MyClinic Diet controlled Medication 1 Patients taking following medication HbA1c Not usually suitable. Consider monthly educational interview to support educational programme. Check on optimum treatment (NICE guidelines). Biguanide only. Not suitable unless patient has unstable periods requiring educational and monitoring support. Check on optimum treatment (NICE guidelines). Diabetes related admission in last 12 months. Acarbose Prandial glucose regulators Thiazolidinediones (glitazones) Incretin mimetics/ GLP-1 analogues. DPP-4 inhibitors (gliptins) insulin. Consider other Co-morbidities (e.g. COPD, Heart Failure). Refer to diabetes nurse specialist if available. Check on optimum treatment (NICE guidelines). HbA1c 6.5mmols. Diabetes related admission in last 12 months. If HbA1c continues to be high refer to diabetes specialist nurse if available. Consider other Comorbidities (e.g. COPD, Heart Failure). Check on optimum treatment (NICE guidelines). * Possible contraindications: palliative end stage illness, unsupported mental health issues, known inability to cope with telehealth kit, cancer patients receiving treatment Remember that patients and carers need to be able to perform glucose monitoring 7

8 Risk stratification (predictive risk modelling) Predictive risk modelling has been around for sometime, in its most basic form they use mathematical models to predict events such as the risk of a patient requiring a hospital admission in the next 12 months. Predictive risk modelling has a preventative role in helping to identify patients who maybe at risk of admission to hospital with interventions that improve/ save lives, thereby reducing costs to the overall health system. There are a number of different methods which are available: PARR++ Combined Model Adjusted Clinical Groups (ACGs) BUPA Health Dialogue Prior year Secondary Care admission data 8 Tunstall healthcare advisory services

9 Patients at risk of rehospitalisation (PARR) In 2005, Essex Strategic Health Authority commissioned The King s Fund to develop a casefinding algorithm on behalf of the Department of Health, the NHS Modernisation Agency and England s strategic health authorities. The Department of Health took over management of this contract in May The project team developed the Patients at Risk of Re-hospitalisation (PARR) tool by using five years of Hospital Episode Statistics (1999/2000 to 2003/4). The team examined admissions in 2003/4 to identify a triggering admission for each patient. Then they looked back at data relating to them from the previous three years to see what factors might predict the triggering admission and any further admissions in 2003/4. The PARR tool used a broad range of variables to establish this risk. This included the patient s previous use of hospital services, demographic data including location to pick up different practice styles and the hospital of current admission to pick up on different practice styles and admission thresholds. A final version, PARR++ was issued in November

10 Combined model To meet national goals for reductions in emergency bed days and effective administration of practicebased commissioning, National Health Service (NHS) organisations have highlighted the need for tools to assess patient needs across the continuum of care. A risk stratification tool called the Combined Predictive Model (the Combined Model) has been developed to provide a rich segmentation of patients at each section of the continuum. The model is based on a comprehensive dataset of patient information, including: inpatient (IP) outpatient (OP) accident & emergency (A&E) data from secondary care sources as well as general practice (GP) electronic medical records All patients in the validation sample were ranked based on their risk for emergency admission and placed into segments. Relative utilisation rates are shown for patients in each segment for the year following prediction compared to average utilisation rates across the entire population. For example, patients in the top 0.5% predicted risk segment were 18.6 times more likely than the average patient to have an emergency admission in the year following prediction. The Combined Model enables targeted intervention across patient segments. Adjusted Clinical Groups (ACGs) The Johns Hopkins Adjusted Clinical Groups (ACG ) system offers an approach to measuring morbidity that improves accuracy in identifying patients at high risk and forecasting healthcare utilisation. The ACG system measures the morbidity burden of patient populations based on disease patterns, age and gender. It relies on the diagnostic and/or prescribing information found in medical records. This provides the user with a more accurate representation of the morbidity burden of populations, subgroups or individual patients - as a constellation of morbidities, not as individual diseases. ACGs do not use admissions or procedures to categorise people. At times, many clinicians and health analysts think of patients as fitting into single diagnostic classes asthmatics, diabetics, patients with heart failure. But measuring the health of a population using indicators of specific diseases or episodes-of-care fails to capture the entirety of morbidity and health care that a person experiences over time and across service settings. Information on the totality of a patient s healthcare needs is required to anticipate resource consumption adequately. The distinguishing feature of the ACG system is its personfocused approach which allows it to capture the multidimensional nature of an individual s health over time. Prior year secondary care admission data This is a simplistic approach to risk modelling that only utilises prior year secondary care data to identify patients who consume the greatest health resource based upon the number of admissions in that year and their health diagnosis. The Whole System Demonstration (WSD) sites used this approach along with a clinical diagnosis (as determined by the Quality Outcomes Framework (QoF) of; Heart Failure; COPD and Diabetes. The approach is quick to identify potential patients at risk of hospitalisation based upon prior year admissions and given the nature of the longterm condition. This would dictate that a progressive worsening of the disease would occur over time resulting in more frequent and severe requirements for hospitalisation Understanding the different methods for risk stratification helps support the decision as to whether an organisation procures a risk stratification tool to facilitate their deployment of telehealth or their overall targeting of patients at risk of admission. This may support recent QP QOF targets (QP QOF 9 and 10) which are based around the identification of patients at risk of admission. 10 Tunstall healthcare advisory services

11 Patient selection through clinical objectives for telehealth Finally, it is useful for a clinician to understand the clinical objective of monitoring a patient on the telehealth service. Example clinical objectives are outlined below: Extra vigilance during a period of medication titration Beta blocker titration in heart failure checking blood pressure, heart rate and rhythm Monitoring of hypotension and hypertension in ACE inhibitor titration (Hypertension and heart failure medication titration). Building a better picture of an anxious or fluctuating patient Some patients using multiple courses of antibiotics and steroids, with anxiety as a predominant feature can consume significant resources. A clinician can struggle to get a handle on such patients and may find using telehealth as a tool would give them more information to build a picture of how to plan care with that patient. Patients who can regularly decompensate/ exacerbate use telehealth to identify episodes of decompensation or exacerbation early Oxygen sats (SpO2) dropping in patient with COPD Significant unexpected weight gain in heart failure patient. Aid to compliance with medication or advice Daily reminders can informally act as a reinforcing tool to the importance of advice given by their lead clinician. There are options to remind patients to take their medication at a particular time. Stable but significant illness use as a tool to reinforce self management and observation Trend monitoring for example weight in heart failure patient. Symptom monitoring for example change in sputum colour and volume. Ability to manage the patient at a distance (not face to face) The clinician can develop a confidence to manage their patients without always needing face to face contact. A combination of telephone contact and the information from the telehealth system allows clinicians to identify with more confidence those patients they need to invest their time in. The potential to reduce visiting is a real and tangible benefit to all clinicians working in primary care. 11

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