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The British Journal of General Practice logoLink to The British Journal of General Practice
. 2020 Jun 16;70(697):e563–e572. doi: 10.3399/bjgp20X711749

Health service use by patients with heart failure living in a community setting: a cross-sectional analysis in North West London

Dani Kim 1, Benedict Hayhoe 2, Paul Aylin 3, Martin R Cowie 4, Alex Bottle 5
PMCID: PMC7299549  PMID: 32540872

Abstract

Background

The complex nature of heart failure (HF) management, often involving multidimensional care, is widely recognised, but overall health service utilisation by patients with HF has not previously been described.

Aim

To describe overall health service use by adults with HF living in a community setting.

Design and setting

Cross-sectional analysis of prevalent HF cases from January 2015 to December 2018 using an administrative dataset covering primary and secondary care, and ‘other’ (community, mental health, social care) services in North West London.

Method

Healthcare use of each service was described overall and by individual components of secondary care (such as, outpatient appointments), and ‘other’ services (such as, nursing contacts). Usage patterns were identified using k-means cluster analysis, using all distinct contacts for the whole study period, and visualised with a heatmap.

Results

A total of 39 301 patients with a prevalent diagnosis of HF between 1 January 2015 and 31 December 2018 were found. Of those, approximately 90% used health services during the study period, most commonly outpatient services, GP consultations, unplanned accident and emergency visits, and community services. Use of cardiology-specific services ranged from around 3% (cardiology-related community care) to around 20% (outpatient cardiology visits). GP consultations decreased by 11% over the study period. Five clusters of patients were identified, each with statistically significantly different care usage patterns and patient characteristics.

Conclusion

Patients with HF make heavy but heterogeneous use of services. Relatively low and falling use of GP consultations, and the apparently low uptake of community rehabilitation services by patients with HF, is concerning and suggests challenges in primary care access and integration of care.

Keywords: Heart failure, primary care, secondary care, outpatient services, cluster analysis, London

INTRODUCTION

Heart failure (HF) affects >900 000 people in the UK1 and results in significant morbidity and mortality, frequent hospitalisations, and reduced quality of life. Patients with HF are usually older with comorbidities, and may have complex and highly heterogeneous medical and social needs.1 A multidisciplinary team (MDT) approach is considered the gold standard model for HF management2 and is recommended for high-risk patients in the Health and Social Care Act of 2012,3 and other national4–6 and international guidelines.7,8 Despite this, there is currently little understanding of the nature of HF care beyond the hospital setting in the UK. Therefore, this study aimed to describe overall health and social service use and care usage patterns by patients with HF in North West London (NWL).

METHOD

Data

Whole Systems Integrated Care (WSIC) data were used: a linked de-identified dataset of individual-level patient records of events from primary, secondary, community, mental health, and social care services in NWL, covering >2 million patients across 400 GP practices.9,10 It has some similarities with primary care-based research databases like Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN),11,12 but with the addition of community, mental health, and social care service records.

Definitions

Patient characteristics

Sex, age, ethnicity, deprivation level, comorbidities, care status, blood pressure, body mass index (BMI), and smoking and alcohol drinking statuses were defined using primary care data at the start of each 1-year period, looking back 5 years to retrieve data. Socioeconomic status was based on the 2015 Index of Multiple Deprivation (IMD)13 and divided into quintiles (1 = most deprived; 5 = least deprived). Comorbidities were defined as per the Charlson Index in Khan et al,14 with some extra ones defined by the authors (see Supplementary Table S1 for details).

Health service use

Health service use was described for each cohort using data for that 1-year period. Primary care use was defined as having a consultation with a GP. Secondary care use included emergency admissions, elective admissions, unplanned accident and emergency (A&E) visits, and outpatient visits. Use of ‘other’ health services (community, mental health, and social care services) were described overall and by individual components. Variables in these ‘other’ health service tables were often not in coded form, so contacts were first indexed with keywords (Box 1) arrived at iteratively by manually searching for the most common terms in each table.

Box 1.

Key terms used to index individual components in ‘other’ services to describe the types of services used by patients with HF in respective settings

Service and component Index terms
Community
Nursing nursing
Rehabilitation rehaba
Urgent rapid, acute, urgent, emergency, A&E, unplanned, admita, hospital adma, inpatient, ambulance
Intermediate intermediate, CIS
Cardiology heart, cardia, stroke
Diabetes diabeta, endocria
Physio- or occupational therapy occupationa, physioa
Podiatry foot, poda
Respiratory pulmona, respira, COPD, TB, tubercula, thoraca
Neurology musculoa, MSK, neuroa, parkinsona
Urinary genitoa, bladder, bowel, continenca, urinary
Speech language therapy SLT, speech, language
Falls falls
Diet and nutrition diet, nutritiona
Memory and cognition memory, cognition
Home home
Phone phone
Unknown (none of the above key terms)
Mental health
Outpatient outpatient, day case
Community community
Urgent rapid, acute, urgent, emergency, A&E, unplanned
Specialist speciala, nursa,aist
Dementia dementia, memory, cognia
Learning disability learning
Eating disorder eating, anorexia
Psychiatric psya
Review reviewa
Consultation consulta
Treatment treata
Assessment assessa
Unknown (none of the above key terms)
Social care
Nursing nursing
Rehabilitation rehaba
Urgent rapid, acute, urgent, emergency, A&E, unplanned
Personal care personal care, home care, day care, bathing, extra care, reablea, care service
Food food, meal
Domestic domiciliary, domestic, housework, laundry, shopping, cleaning, washing
Transport transfer, transport, migration, trip, mobila
Disability dis’y, disabilita, disablea
Occupational therapy occupational therapy
Memory and cognition dementia, memory, cognition
Assisted equipment technology assistive techa, assisted equipment, equipment, technology
Nursing home nursing home, residential home, residential care, care home
Mental health mental, CMHT
Community community
Social social
Carer carer
Housing and living housing, living
Unknown (none of the above key terms)a
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a

Superscript used as a wildcard character during key term search. When used, search returns results containing text preceding ‘a’. A&E = accident and emergency. CIS = community independence service. CMHT = community mental health team. COPD = chronic obstructive pulmonary disease. HF = heart failure. MSK = musculoskeletal. SLT = speech language therapy. TB = tuberculosis.

How this fits in

Heart failure (HF) prevalence is increasing and requires multidisciplinary management, including within primary care. Using a linked database for North West London’s 2.2 million population, this study found that in 39 301 patients with HF, only 60% had seen their GP and 20% had been referred for cardiac rehabilitation during the study period, while overall use of unscheduled care by patients with HF was high, with >40% using accident and emergency services. Findings from cluster analysis, highlighting groups of patients with HF that are particularly high and low users of elements of care, may facilitate active case finding and provision of more supportive and preventative care to improve outcomes for these patients.
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Cluster analysis

The authors sought to discover patterns of healthcare utilisation via k-means cluster analysis.15 Nine healthcare utilisation count variables, reflecting total usage from 2014 to 2018, were used to define clusters: emergency admissions; elective admissions; unplanned A&E visits not ending in admission; outpatient visits (cardiology); outpatient visits (other); GP consultations; and community, mental health, and social care contacts. Only distinct contacts and attended outpatient visits were included, and extreme high users (in the top 0.1% for any of these variables) were excluded.

Data were log-transformed and normalised (min–max method) before analysis to give higher weighting to lower values and equal weighting to all variables, respectively. K-means required the number of clusters (k = 5) to be pre-specified (see Supplementary Box S1 for details).

Statistical analysis

Patient characteristics and prevalence of health service use were summarised for the four yearly cohorts and clusters separately. Usage patterns for each cluster were visualised using a heatmap by comparing the cluster mean usage with the average population usage, taking the percentage difference between these two means.

Differences in healthcare utilisation variables and key patient characteristics across clusters were analysed using Kruskal–Wallis tests for continuous variables and Pearson χ2 tests for categorical variables, with two-tailed testing and a significance level of 0.05. All analyses were conducted using R (version 3.4.0).

RESULTS

Patient characteristics

A total of 39 301 patients from 359 GP practices between 1 January 2015 and 31 December 2018 had an HF diagnosis recorded and met the inclusion criteria (see Supplementary Figure S1 for flowchart of study population), that is, approximately 10 new patients with HF per practice per year. The vast majority of patients were in each of the four yearly cohorts.

In 2018, most patients were female (56.2%, n = 19 463), aged ≥65 years (58.1%, n = 20 129), and were of white (31.1%, n = 10 793), Asian (25.7%, n = 8905), or unknown (27.3%, n = 9454) ethnicity (Table 1). Almost two-thirds (63.8%, n = 22 092) had multimorbidity, that is, had a comorbidity in addition to existing HF, and of these more than half had at least two additional comorbidities, most commonly diabetes (26.1%, n = 9053) or hypertension (36.1%, n = 12 507).

Table 1.

Patient characteristics for each cohort

Characteristics Year
2015 2016 2017 2018
n (N = 23 828) % n (N = 27 443) % n (N =31 554) % n (N = 34 651) %
Sex
  Female 13 178 55.3 15 246 55.6 17 506 55.5 19 463 56.2
Age group, years
  <45 3293 13.8 3958 14.4 4619 14.6 5227 15.1
  45 to <65 6760 28.4 7661 27.9 8600 27.3 9295 26.8
  65 to <75 5542 23.3 5957 21.7 6601 20.9 7007 20.2
  75 to <85 6002 25.2 6952 25.3 7948 25.2 8470 24.4
  >85 2231 9.4 2915 10.6 3786 12.0 4652 13.4
Deprivation level, IMD
  1 (most) 3166 13.3 3883 14.1 4656 14.8 5205 15.0
  2 6568 27.6 7673 28.0 9001 28.5 9954 28.7
  3 5869 24.6 6833 24.9 7875 25.0 8759 25.3
  4 4089 17.2 4596 16.7 5197 16.5 5653 16.3
  5 (least) 3430 14.4 3636 13.2 3866 12.3 3975 11.5
  Unknown 706 3.0 822 3.0 959 3.0 1105 3.2
Ethnicity
  White 7712 32.4 8741 31.9 9963 31.6 10 793 31.1
  Asian 6237 26.2 7147 26.0 8161 25.9 8905 25.7
  Black 1721 7.2 1990 7.3 2245 7.1 2412 7.0
  Mixed 1767 7.4 2162 7.9 2630 8.3 3087 8.9
  Unknown 6391 26.8 7403 27.0 8555 27.1 9454 27.3
Care status
  Care home 619 2.6 682 2.5 732 2.3 686 2.0
  Have carer 125 0.5 162 0.6 191 0.6 219 0.6
Status
  Died 1294 5.4 1543 5.6 1811 5.7 2351 6.8
  Opted out — 0.0 21 0.1 92 0.3 5 0.0
Smoking status
  Non 9612 40.3 11 455 41.7 13 395 42.5 14 955 43.2
  Current 2290 9.6 2648 9.6 3174 10.1 3377 9.7
  Former 7626 32.0 8390 30.6 9267 29.4 9685 28.0
  Unknown 4300 18.0 4950 18.0 5718 18.1 6634 19.1
Drinking status
  Non 1607 6.7 1934 7.0 2079 6.6 2197 6.3
  Current 382 1.6 439 1.6 485 1.5 578 1.7
  Former 43 0.2 58 0.2 70 0.2 83 0.2
  Unknown 21 796 91.5 25 012 91.1 28 920 91.7 31 793 91.8
BMI category
  Underweight 423 1.8 526 1.9 714 2.3 1000 2.9
  Ideal 5210 21.9 6000 21.9 7032 22.3 7814 22.6
  Overweight 6296 26.4 7089 25.8 7959 25.2 8503 24.5
  Obese 6255 26.3 7213 26.3 8323 26.4 8891 25.7
  Unknown 5644 23.7 6615 24.1 7526 23.9 8443 24.4
Systolic blood pressure, mm Hg
  <110 1109 4.7 1303 4.7 1535 4.9 1789 5.2
  110–119 2386 10.0 2886 10.5 3497 11.1 3966 11.4
  120–139 10 943 45.9 12 768 46.5 14 820 47.0 16 465 47.5
  140–159 5925 24.9 6540 23.8 7270 23.0 7623 22.0
  >159 510 2.1 570 2.1 627 2.0 609 1.8
  Unknown 2955 12.4 3376 12.3 3805 12.1 4199 12.1
Diastolic blood pressure, mm Hg
  <80 13 969 58.6 16 434 59.9 19 215 60.9 21 261 61.4
  80 to 89 5586 23.4 6195 22.6 6959 22.1 7536 21.7
  90 to 99 962 4.0 1053 3.8 1159 3.7 1219 3.5
  >99 134 0.6 148 0.5 166 0.5 179 0.5
  Unknown 3177 13.3 3613 13.2 4055 12.9 4456 12.9
Comorbidities
  Acute myocardial infarction 1006 4.2 1123 4.1 1309 4.1 1398 4.0
  Atrial fibrillation 2700 11.3 3362 12.3 4018 12.7 4549 13.1
  Chronic pulmonary disease 3346 14.0 3928 14.3 4623 14.7 5084 14.7
  Congenital heart disease 73 0.3 91 0.3 122 0.4 135 0.4
  Coronary heart disease 1516 6.4 1658 6.0 1856 5.9 1965 5.7
  Diabetes 6116 25.7 7102 25.9 8208 26.0 9053 26.1
  Hypertension 10 474 44.0 11 352 41.4 12 241 38.8 12 507 36.1
  Myocardial infarction 1034 4.3 1146 4.2 1331 4.2 1444 4.2
  Myocarditis 614 2.6 697 2.5 797 2.5 868 2.5
  Other arrhythmias 2181 9.2 2680 9.8 3266 10.4 3782 10.9
  Peripheral vascular disease 563 2.4 634 2.3 681 2.2 771 2.2
  Renal diseases 1048 4.4 1129 4.1 1301 4.1 1400 4.0
  Stroke 667 2.8 762 2.8 933 3.0 1052 3.0
Comorbidities, n
  0 7793 32.7 9329 34.0 11 073 35.1 12 559 36.2
  1 7155 30.0 7978 29.1 8950 28.4 9667 27.9
  2 4976 20.9 5640 20.6 6264 19.9 6659 19.2
  3 2291 9.6 2632 9.6 3082 9.8 3363 9.7
  ≥4 1613 6.8 1864 6.8 2185 6.9 2403 6.9
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BMI = body mass index. IMD = Index of Multiple Deprivation.

A total of 6999 (17.8%) people died and 110 (0.3%) opted out of the WSIC dataset.

Most patient characteristics remained constant during the study period except for an increase in proportion of the underweight (60% increase) and the oldest age group (40% increase), and a nearly 20% reduction in the prevalence of hypertension (Table 1).

Health service use

Approximately 90% of patients used health services during the study period (data not shown). In 2018, the most commonly used healthcare services were outpatients (70.1%, n = 24 283), GP consultations (59.9%, n = 20 741), unplanned A&E (40.8%, n = 14 145), community (39.7%, n = 13 762), emergency admissions (26.7%, n = 9257) and outpatient cardiology (23.8%, n = 8231) services (Table 2). Community care was the most common ‘other’ service used, of which the most frequent components were nursing (23.2%, n = 8052), podiatry (15.6%, n = 5397), and rehabilitation-related services (8.3%, n = 2861). Few (2.9%, n = 1005) used community care related to cardiology even though >1 in 5 had a GP record of referral to cardiac rehabilitation. In total, 6.3% (n = 2178) had a referral for echocardiogram, of which over half had abnormal results (51.1%, n = 1113). Both social care and mental health service use were less common (3.9% and 4.5%, respectively). When used, mental health contacts were commonly community-related (4.3%, n = 1489), suggesting a community integrated approach; social care contacts were personal care (3.1%, n = 1057), community (0.7%, n = 232), domestic (0.7%, n = 253), and disability-related (0.7%, n = 248) (Table 2).

Table 2.

Health service ever used by patients with HF in NWL between 2015 and 2018

Service Year
2015 2016 2017 2018
n (N = 23 828) % n (N = 27 443) % n (N = 31 554) % n (N= 34 651) %
Secondary care
  Emergency admission 5163 21.7 6592 24.0 8276 26.2 9257 26.7
  Elective admission 910 3.8 1495 5.4 1803 5.7 1798 5.2
  Unplanned A&E 8238 34.6 10 697 39.0 12 250 38.8 14 145 40.8
  Outpatient (any specialty) 13 492 56.6 18 560 67.6 22 115 70.1 24 283 70.1
  Outpatient (top 1) (cardiology) 4117 17.3 6518 23.8 8040 25.5 8231 23.8
  Outpatient (top 2)a 2645 11.1 4662 17.0 5600 17.7 6049 17.5
  Outpatient (top 3)a 1992 8.4 3444 12.5 4127 13.1 5053 14.6
  Outpatient (top 4)a 1645 6.9 3156 11.5 4018 12.7 4500 13.0
  Outpatient (top 5)a 1563 6.6 2554 9.3 3220 10.2 3383 9.8
Primary care
  GP consultation 16 014 67.2 17 573 64.0 19 494 61.8 20 741 59.9
  Echocardiogram 1826 7.7 2016 7.3 2156 6.8 2178 6.3
  Echocardiogram abnormal 1010 4.2 1066 3.9 1150 3.6 1113 3.2
Community
  Any 8801 36.9 9869 36.0 11 195 35.5 13 762 39.7
  Nursing 6590 27.7 5893 21.5 6311 20.0 8052 23.2
  Rehabilitation 2052 8.6 2489 9.1 2065 6.5 2861 8.3
  Urgent 918 3.9 811 3.0 999 3.2 1203 3.5
  Intermediate 1841 7.7 1754 6.4 1397 4.4 1526 4.4
  Cardiology 859 3.6 578 2.1 797 2.5 1005 2.9
  GP referral to cardiac rehabilitation 4333 18.2 5465 19.9 6585 20.9 7187 20.7
  Diabetes 1011 4.2 955 3.5 1171 3.7 1654 4.8
  Physio-/occupational therapy 1091 4.6 1160 4.2 1148 3.6 1853 5.3
  Podiatry 3760 15.8 4076 14.9 4404 14.0 5397 15.6
  Respiratory 217 0.9 163 0.6 206 0.7 302 0.9
  Neurological 1054 4.4 1427 5.2 1481 4.7 2138 6.2
  Urinary 452 1.9 597 2.2 747 2.4 1374 4.0
  Speech language therapy 108 0.5 68 0.2 60 0.2 102 0.3
  Falls 165 0.7 332 1.2 365 1.2 450 1.3
  Diet and nutrition 644 2.7 1399 5.1 1696 5.4 2197 6.3
  Home 1229 5.2 1411 5.1 1738 5.5 1654 4.8
  Phone 949 4.0 317 1.2 418 1.3 393 1.1
  Unknown 178 0.7 203 0.7 206 0.7 248 0.7
Mental health
  Any 1064 4.5 1421 5.2 1718 5.4 1557 4.5
  Outpatient 354 1.5 360 1.3 428 1.4 169 0.5
  Community 964 4.0 1305 4.8 1589 5.0 1489 4.3
  Urgent 387 1.6 695 2.5 950 3.0 631 1.8
  Specialist 62 0.3 197 0.7 253 0.8 78 0.2
  Dementia 527 2.2 585 2.1 706 2.2 732 2.1
  Learning disability 33 0.1 39 0.1 48 0.2 29 0.1
  Psychology 91 0.4 236 0.9 279 0.9 261 0.8
  Review 109 0.5 159 0.6 214 0.7 92 0.3
  Consultation 51 0.2 82 0.3 118 0.4 951 2.7
  Treatment 61 0.3 84 0.3 110 0.3 117 0.3
  Assessment 151 0.6 254 0.9 309 1.0 267 0.8
  Unknown <5 0.0 <5 0.0 — 0.0 — 0.0
Social care
  Any 1042 4.4 1236 4.5 1912 6.1 1350 3.9
  Nursing 89 0.4 109 0.4 169 0.5 128 0.4
  Personal care 709 3.0 890 3.2 1485 4.7 1057 3.1
  Food 68 0.3 82 0.3 44 0.1 10 0.0
  Domestic 165 0.7 238 0.9 350 1.1 253 0.7
  Transport 78 0.3 78 0.3 99 0.3 70 0.2
  Disability 181 0.8 230 0.8 277 0.9 248 0.7
  Occupational therapy 8 0.0 15 0.1 18 0.1 28 0.1
  Memory and cognition 53 0.2 76 0.3 104 0.3 63 0.2
  Assisted equipment technology 129 0.5 28 0.1 47 0.1 7 0.0
  Nursing home 58 0.2 76 0.3 123 0.4 80 0.2
  Mental health 43 0.2 49 0.2 95 0.3 36 0.1
  Community 250 1.0 122 0.4 198 0.6 232 0.7
  Social 64 0.3 87 0.3 165 0.5 88 0.3
  Carer 127 0.5 125 0.5 142 0.5 159 0.5
  Housing and living 119 0.5 66 0.2 98 0.3 41 0.1
  Unknown 68 0.3 110 0.4 119 0.4 66 0.2
Number of services used
  0 2602 10.9 2567 9.4 2891 9.2 3067 8.9
  1 7036 29.5 7438 27.1 8572 27.2 9267 26.7
  2 8200 34.4 10 549 38.4 12 211 38.7 13 710 39.6
  >3 5990 25.1 6889 25.1 7880 24.9 8607 24.8
Service type
  None 2602 10.9 2567 9.4 2891 9.2 3067 8.9
  Secondary care only 2508 10.5 3569 13.0 4586 14.5 5020 14.5
  Primary care only 4134 17.3 3443 12.5 3538 11.2 3600 10.4
  Other services only 460 1.9 467 1.7 486 1.5 682 2.0
  Secondary care and primary care 5298 22.2 7331 26.7 8465 26.8 8606 24.8
  Secondary care and other services 2244 9.4 3267 11.9 4097 13.0 5141 14.8
  Primary care and other services 1133 4.8 606 2.2 614 1.9 781 2.3
  All three 5449 22.9 6193 22.6 6877 21.8 7754 22.4
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a

Top five outpatient specialties by year: 2015 ranking: 1) cardiology; 2) general surgery; 3) ophthalmology; 4) trauma and orthopaedics; 5) allied health professional episode. 2016 to 2018 ranking: 1) cardiology; 2) ophthalmology; 3) general surgery; 4) allied health professional episode; 5) radiology. A&E = accident and emergency department. HF = heart failure. NWL = North West London.

In 2018, only 3067 (8.9%) patients did not use any services, while around one-quarter used >3 different types (24.8%, n = 8607). Services were most commonly used in combination with secondary care and least commonly with ‘other’ health services (Table 2 and Figure 1). Few patients used only primary care and ‘other’ services (2.3%, n = 781) or ‘other’ services alone (2.0%, n = 682).

Figure 1.

Figure 1.

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Venn diagram of service use in 2018, showing an approximation of group sizes. An intersection is missing between primary care and ‘other’ services (2.3%) – documentation of R eulerr package states ‘with three or more sets intersecting, exact Euler diagrams are often impossible. For such cases eulerr attempts to provide a good approximation.’16

Over the study period, health service use increased for all elements of secondary care analysed, particularly elective admissions (37% increase) and outpatient visits (24% increase), but decreased for primary care (11% decrease). Though many components of the community contacts remained constant, there were more than double contacts related to diet and nutrition (Table 2).

Cluster analysis

Altogether 318 patients were excluded from the k-means cluster analysis due to extremely high usage. Of the four and five-cluster solutions identified via preliminary analysis (see Supplementary Box S1), the five-cluster solution was chosen as the extra cluster had distinct usage patterns (Figure 2). Additionally, all patient characteristics differed significantly across clusters (see Supplementary Table S2 and Supplementary Figure S2). Patients who were younger, female, with less comorbidity, and not living in care homes were generally low users of health care (clusters 1 and 2). Perhaps unsurprisingly, those with higher blood pressure and more comorbidities had relatively more GP consultations (cluster 2). Patients who were older, male, and had more comorbidities were generally higher users of health care (clusters 3, 4 and 5). The lowest users of GP appointments were very high users of all other services (cluster 3, Figure 2). Those with the most cardiovascular comorbidity (cluster 4) had the highest usage of cardiology-related outpatient services and referrals to echocardiography (42.0%) (Figure 2). The oldest patients with the highest mortality (cluster 5) were the highest users of emergency inpatient, A&E, and ‘other’ services (Figure 2).

Figure 2.

Figure 2.

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Heatmap of service utilisation by cluster. Numbers represent percentage difference between cluster mean and population mean values of each health utilisation variable. A&E = accident and emergency.

DISCUSSION

Summary

Overall health service utilisation was high. Almost everyone in the present study population used some kind of health service during the study period: outpatients (7 in 10), primary care (6 in 10 saw a GP), community services, especially nursing (2 in 10), and unplanned A&E visits (4 in 10). Community care use related to cardiology was low. Few patients used only primary care and ‘other’ services, which may reflect modest needs or a lack of community and primary care provision suitable for complex needs.

Patterns of health service utilisation depended on age and comorbidity but were highly heterogeneous. Younger patients with fewer comorbidities (clusters 1 and 2) had the lowest usage, which may partly indicate underutilisation and/or lack of access. For instance, those with infrequent GP consultations (clusters 1 and 3) were also more likely to be of mixed ethnicity and living in areas of higher deprivation and demographics known to be associated with poorer primary care access.17,18 These patients also showed the highest levels of unknown values for patient variables (derived from GP data) and lower than average GP consultation rates, which could reflect both poor health management and low engagement of patients in their own health (they were also more likely to be smokers). The oldest and most likely to live in care homes (cluster 5) had the highest usage of emergency inpatient, A&E, and ‘other’ services, and had high levels of comorbidity, especially renal disease, and the highest mortality. Higher usage of care is expected in older patients with comorbidities,19–21 but some use might be excessive and avoidable.19,20 Health service use was high in the present population of adults with HF living in a community setting. However, overall, relatively low GP service use, which decreased over the study period, and high use of emergency and other unscheduled care in these vulnerable patients is of significant concern and may suggest challenges in access to primary care services. These findings warrant further investigation to ensure equity of access and appropriate integrated care provision for patients with HF.

Strengths and limitations

The authors used a linked dataset with near-complete population coverage for the region and employed both descriptive analysis and clustering algorithms to describe health service use by this highly heterogeneous population. The dataset is large and reflective of current medical practice, but the study has several limitations.

Electronic health records are not specifically intended for research, and coding is highly variable.22 Coding in some of the WSIC tables required additional cleaning and processing, which could introduce bias; however, a transparent coding methodology to mitigate this was provided. Moreover, coded data rely on recorded information, meaning that certain diseases or service components may have been underestimated, for example, cardiac rehabilitation, or that certain primary care data coding may have been affected by pay-for-performance schemes. The authors were unable to ascertain the reasons for the community care or mental health consultations as diagnosis coding was irregular. It is also likely that the fall in GP consultations during the study period was offset by more practice nurse contacts, which were not included in the dataset.

Lastly, though the dataset was based on adults living in a community setting from a large and ethnically diverse area in England, the findings may not be generalisable to the wider population of people with HF.

Comparison with existing literature

Few studies have attempted to quantify individual patterns of care in real-world settings beyond the hospital. Robertson et al described the burden of HF on the Australian healthcare system, but were only able to assess hospitalisation data,23 as was the case for the present authors’ previous work.24,25 Similarly, other studies have described a single dimensional aspect of health service use by the population of people with HF.26,27 The present findings are consistent with these, showing that secondary care use is high,23 participation in cardiac rehabilitation in the community is low,26,28 and that requirement for personal care, such as nursing and homecare services, is relatively common.27

An increase over time in most healthcare services use was observed in the present study, especially outpatient visits, but a surprising decrease of 11% in GP consultations. Furthermore, only 60% of patients had GP appointments during the study period, which contrasts with the national GP Patient Survey of 2019,29 where 85% of patients reported having had a GP appointment in the past year. Potential explanations include the increasing workload and workforce pressures on primary care, changes in primary care practice with more frequent contacts with practice nurses and allied health professionals, a significant problem of access to care, and/or differences in case mix.

Another surprising finding is the apparently limited uptake of community cardiac rehabilitation. The National Audit of Cardiac Rehabilitation 2018 report28 suggests that around half of eligible patients take up cardiac rehabilitation. The report did indicate significant regional variation. However, it seems likely that differences in coding of data are responsible for the very low uptake in this analysis; ‘rehabilitation’ events may be recorded elsewhere and currently unavailable in WSIC, and ‘community cardiology’ may also include HF nurse domiciliary care.

The authors further report low use of mental health and social care services by patients with HF, but whether this observed level is appropriate is unclear without further assessment.

Implications for research and practice

The present finding of increased secondary and urgent care service use, low GP appointment use, and apparently limited cardiac rehabilitation is of concern and suggests a lack of multidisciplinary HF care. National Institute for Health and Care Excellence guidelines recommend an MDT approach, but there is no standard definition besides who should be involved and what should be achieved.1 Each local area has unique challenges and requires tailored solutions; research is needed to establish the nature, location, timing, and intensity of the support needed by patients with HF. In an ethnically diverse area with a relatively young population like NWL, where deprivation level and ethnicity may affect a person’s access to health care, creating a strong MDT embedded in primary care may be very pertinent. For example, practice nurses may target recently diagnosed patients in primary care, that is, younger patients with fewer comorbidities, on early education and management, which may include additional telephone and/or specialist community support for those with lower socioeconomic status. This, in conjunction with hospital-based solutions, like early supported discharge plans for older patients, who are the highest users of secondary care, may provide significant and long-term benefits for the NWL area. Local solutions like these have been shown not only to reduce utilisation of health services but also to improve patient wellbeing and result in large cost savings for the NHS.30

Though the present data could not establish whether an MDT approach was implemented in the NWL area, it may well be that MDTs exist but their solutions are not translating into reduced secondary care use. Successful MDTs will require cooperation, coordination, and communication across health services. Reasons for ineffective multidisciplinary care could be posited through the following questions: is there an overarching coordinating unit for multidisciplinary care? Are the IT systems compatible for such care? Is information exchange readily available and safe? Is communication across settings both smooth and frequent? Is the approach sustainable? These questions illustrate how successful solutions will require sustained financial investments and the solid backing of all relevant stakeholders, and the sheer challenge of this may explain why many MDTs have had only neutral effects.2

Acknowledgments

The Department of Primary Care and Public Health at Imperial College London is grateful for support from the NWL NIHR Applied Research Collaboration and the Imperial NIHR Biomedical Research Centre.

Funding

The Dr Foster Unit at Imperial College London is partially funded by a grant from Dr Foster, a private healthcare information company. It is also partly funded by research grants from the National Institute for Health Research (NIHR) Health Services and Delivery Research (HS&DR) (ref: 17/99/72). Martin R Cowie’s salary is supported by the NIHR Cardiovascular Biomedical Research Unit at the Royal Brompton Hospital, London. None of the funders had any role in the conception, design, analysis, or reporting of this study

Ethical approval

Whole Systems Integrated Care (WSIC) is a dataset of North West London (NWL) residents who have consented to the anonymous data in their online health records for research purposes. Additionally, this specific study was approved by the Discover Research Advisory Group (DRAG), which is a nominated body that provides a governance mechanism for evaluating project applications requesting the WSIC de-identified dataset.

Provenance

Freely submitted; externally peer reviewed.

Competing interests

Alex Bottle, Dani Kim, and Paul Aylin had financial support through a research grant from Dr Foster for the submitted work. There were no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years and no other relationships or activities that could appear to have influenced the submitted work. Benedict Hayhoe is a GP working in the NHS.

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Articles from The British Journal of General Practice are provided here courtesy of Royal College of General Practitioners

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