Background
Methods
Setting
Data Sources
The ACG case-mix system
The dashboard
Results
N | % | ACG weight | Resource utilization quintile | ||
---|---|---|---|---|---|
Overall
| |||||
11,391,085 | 100 | 1 | |||
Sex
| |||||
Female | 5,894,366 | 51.8 | 1.08 | ||
Male | 5,496,719 | 48.3 | 0.92 | ||
Age
| |||||
19-34
| 3,221,862 | 28.3 | 0.56 | ||
35-44
| 2,408,590 | 21.1 | 0.7 | ||
45-54
| 2,303,950 | 20.2 | 0.95 | ||
55-64
| 1,626,276 | 14.3 | 1.26 | ||
65-74
| 979,727 | 8.6 | 1.72 | ||
75-84
| 623,531 | 5.5 | 2.3 | ||
85+
| 227,149 | 2 | 2.48 | ||
ACGs, sorted by lowest to highest illness severity weight
| |||||
5100 | No or Only Unclassified Diagnoses & Non-Users | 3,189,204 | 28 | 0.17 | 0 |
1600 | Preventive/Administrative | 185,342 | 1.6 | 0.33 | 1 |
600 | Likely To Recur, with Allergies | 28,658 | 0.3 | 0.36 | 1 |
700 | Asthma | 20,604 | 0.2 | 0.37 | 1 |
300 | Acute Minor, Age 6+ | 633,452 | 5.6 | 0.38 | 1 |
500 | Likely To Recur, without Allergies | 282,943 | 2.5 | 0.39 | 1 |
2200 | Acute Minor: Age > 5,with Allergy | 38,494 | 0.3 | 0.44 | 1 |
400 | Acute: Major | 407,466 | 3.6 | 0.49 | 1 |
2100 | Acute Minor: Age > 5,w/out Allergy | 289,867 | 2.5 | 0.49 | 2 |
3900 | Acute Minor: Male, Age 18-34 | 106,375 | 0.9 | 0.51 | 2 |
1300 | Psychosocial, without Psychosocial Unstable | 124,842 | 1.1 | 0.53 | 2 |
1200 | Chronic Specialty, Unstable | 17,645 | 0.2 | 0.55 | 2 |
1000 | Chronic Specialty | 6,545 | 0.1 | 0.58 | 2 |
1800 | Acute Minor and Acute Major | 404,069 | 3.6 | 0.59 | 2 |
2800 | Acute Major And likely To Recur | 189,580 | 1.7 | 0.6 | 2 |
2400 | Acute Minor and Eye/Dental | 9,620 | 0.1 | 0.6 | 2 |
2500 | Acute Minor, Psychosocial, Without Unstable | 94,404 | 0.8 | 0.61 | 2 |
1100 | Ophthalmological/Dental | 17,678 | 0.2 | 0.62 | 2 |
3300 | Acute Minor: Age > 12, with Allergies | 44,122 | 0.4 | 0.67 | 2 |
4000 | Acute Minor: Female, Age 18-34 | 108,935 | 1 | 0.73 | 2 |
900 | Chronic Medical, Stable | 311,434 | 2.7 | 0.76 | 2 |
3200 | Acute Minor: Age > 12,w/out Allergies | 327,428 | 2.9 | 0.76 | 3 |
3400 | Acute Minor/Likely To Recur/Eye & Dental | 6,164 | 0.1 | 0.77 | 3 |
4310 | 4-5 Other ADG Combos, Age 18–44, No Major ADGs | 162,851 | 1.4 | 0.79 | 3 |
4710 | 6-9 Other ADG Combos, Male, Age 18–34, No Major ADGs | 6,218 | 0.1 | 0.8 | 3 |
2300 | Acute Minor and Chronic Medical: Stable | 191,521 | 1.7 | 0.81 | 3 |
3500 | Acute Minor/Likely To Recur/Psychosocial | 79,984 | 0.7 | 0.84 | 3 |
4320 | 4-5 Other ADG Combos, Age 18–44, 1 Major ADG | 125,136 | 1.1 | 1.07 | 3 |
4100 | Acute Minor: Age >34 | 1,245,235 | 10.9 | 1.23 | 3 |
4810 | 6-9 Other ADG Combos, Female, Age 18–34, No Major ADGs | 19,406 | 0.2 | 1.25 | 4 |
1730 | Pregnancy: 2–3 ADGs, 1+ Major ADGs | 8,041 | 0.1 | 1.26 | 4 |
3700 | Acute Minor & Major/Likely to Recur/Psychosocial | 139,182 | 1.2 | 1.29 | 4 |
4720 | 6-9 Other ADG Combos, Male, Age 18–34, 1 Major ADGs | 11,186 | 0.1 | 1.3 | 4 |
4410 | 4-5 Other ADG Combos, Age >44, No Major ADGs | 334,873 | 2.9 | 1.31 | 4 |
3600 | Acute Minor/Maj/Likely to Recur/Chronic Med:Stable | 236,710 | 2.1 | 1.35 | 4 |
800 | Chronic Medical, Unstable | 62,924 | 0.6 | 1.41 | 4 |
1750 | Pregnancy: 4–5 ADGs, 1+ Major ADGs | 21,750 | 0.2 | 1.49 | 4 |
4820 | 6-9 Other ADG Combos, Female, Age 18–34, 1 Major ADG | 22,384 | 0.2 | 1.61 | 4 |
1710 | Pregnancy: 0–1 ADGs | 15,661 | 0.1 | 1.66 | 4 |
4330 | 4-5 Other ADG Combos, Age 18–44, 2+ Major ADGs | 29,298 | 0.3 | 1.67 | 4 |
2600 | Acute Minor: Unstable without Stable | 10,486 | 0.1 | 1.69 | 4 |
1400 | Psychosocial, with Unstable, without Stable | 26,273 | 0.2 | 1.76 | 4 |
1720 | Pregnancy: 2–3 ADGs, No Major ADGs | 68,678 | 0.6 | 1.76 | 4 |
2700 | Acute Minor: with Unstable & Stable | 6,908 | 0.1 | 1.84 | 4 |
1500 | Psychosocial, with Unstable and Stable | 10,742 | 0.1 | 1.84 | 4 |
1740 | Pregnancy: 4–5 ADGs, No Major ADGs | 61,586 | 0.5 | 1.86 | 4 |
1770 | Pregnancy: 6+ ADGs, 1+ Major ADGs | 40,171 | 0.4 | 2.05 | 5 |
1760 | Pregnancy: 6+ ADGs, No Major ADGs | 34,666 | 0.3 | 2.13 | 5 |
4420 | 4-5 Other ADG Combos, Age >44, 1 Major ADGs | 404,582 | 3.6 | 2.16 | 5 |
4910 | 6-9 Other ADG Combos, Age >34, 0–1 Major ADGs | 507,762 | 4.5 | 2.3 | 5 |
4830 | 6-9 Other ADG Combos, Female, Age 18–34, 2+ Major ADGs | 10,758 | 0.1 | 2.68 | 5 |
4730 | 6-9 Other ADG Combos, Male, Age 18–34 2+ Major ADGs | 9,737 | 0.1 | 2.76 | 5 |
5040 | 10+ Other ADG Combos, Age 18+, 0–1 Major ADGs | 33,410 | 0.3 | 3.11 | 5 |
4430 | 4-5 Other ADG Combos, Age >44, 2+ Major ADGs | 136,748 | 1.2 | 3.29 | 5 |
4920 | 6-9 Other ADG Combos, Age >34, 2 Major ADGs | 237,874 | 2.1 | 3.94 | 5 |
5050 | 10+ Other ADG Combos, Age 18+, 2 Major ADGs | 43,637 | 0.4 | 5.08 | 5 |
4930 | 6-9 Other ADG Combos, Age >34, 3 Major ADGs | 83,427 | 0.7 | 5.72 | 5 |
5060 | 10+ Other ADG Combos, Age 18+, 3 Major ADGs | 42,381 | 0.4 | 7.45 | 5 |
4940 | 6-9 Other ADG Combos, Age >34, 4+ Major ADGs | 18,533 | 0.2 | 7.9 | 5 |
5070 | 10+ Other ADG Combos, Age 18+, 4+ Major ADGs | 45,383 | 0.4 | 12.61 | 5 |
Discussion
The dashboard can help to partially measure the Triple Aims
The dashboard can help policymakers to achieve all of the Triple Aims
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Reduce costs: First, to achieve lower system costs and move the cost curve left, (See Figure 2C) policymakers and providers could use the dashboard to support cost-effective improvements in care (e.g. new technologies, diagnostic equipment, or surgical techniques), such as in ACGs with many individuals even if they are not the most complex cases. The dashboard might also identify medical provider specialties that ought to be high priority for better alignment with financial incentives that focus on patient outcomes or bundles of care, rather than number of visits, as in fee-for-service. Second, policymakers and providers can act to reduce costs within individual ACG categories. For highest-needs or highest-costs individuals, they could support interventions that provide patients with additional care support, intensive case management, or early referral to palliative care as appropriate, all of which can avoid unnecessary hospitalizations and ED visits. For medium- and low-risk individuals, they could support interventions that maintain or improve health, such as self-care, healthy nutrition, exercise, and wellness programs, which can reduce costs within an ACG category. (See Figure 2B)
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Improve population health: The dashboard can present data to providers at an individual or group practice level about their patient rosters to determine if their roster is getting healthier over time (See Figure 2A). Providers and policymakers could use the information to focus on particular health conditions, regions, or populations that could benefit from targeted interventions, and the size and scale required for such interventions. For the sickest population, they could refer them to interventions or supports that prevent the worsening of their condition (e.g. falls prevention), or help them manage complex conditions (e.g. intensive case management or nurse coaching). Just as importantly, for the medium and low-risk population, providers could encourage interventions that promote prevention and wellness, improve health, and reverse the disease progression, such as self management and exercise programs.
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Improve patient care experience: The dashboard does not measure care experience directly. However, the dashboard’s information potentially can help providers improve care experience and overall health. For instance, the dashboard could report to providers the high-risk, medically complex patients they are the most responsible physician for. The providers could then work proactively, rather than reactively, to intervene and develop multidisciplinary care plans for higher need individuals to prevent worsening of the condition(s) rather than wait for them to arrive in the hospital or their office with an issue. Thus the dashboard can support integration with the broader health system and health care team to improve the care experience for the patient.