Health Policy Spotlight Effects on Critical Time-Sensitive Diseases: Evidence from Taiwan Categorization of Hospital Emergency Capability Policy

doi:10.21203/rs.3.rs-4697511/v1

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Research Article

Health Policy Spotlight Effects on Critical Time-Sensitive Diseases: Evidence from Taiwan Categorization of Hospital Emergency Capability Policy

https://doi.org/10.21203/rs.3.rs-4697511/v1

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Objective To investigate the effects of the Categorization of hospital emergency capability (CHEC) policy on critical time-sensitive diseases (CTSDs).

Setting CHEC is a policy implemented worldwide to regionalize and guide the dispatch of critical patients to the nearest appropriate hospital. In 2009, Taiwan's CHEC policy was designed to improve the quality of emergent care for CTSDs.

Research Design and Participants A nationwide observational quasi-experimental study was conducted to examine the quality of care for CTSD before (2006-2008) and after (2009-2012) the implementation of the CHEC policy. CHEC policy focused on acute ischemic stroke (AIS), ST-segment elevation myocardial infarction (STEMI), septic shock, and major trauma. A difference-in-differences estimation was used to assess the impact of the CHEC policy exposure (AIS and STEMI) on clinical practice and outcomes, compared with the unexposed counterfactual of septic shock. We selected diagnosis and treatment guideline adherence process quality measures as primary outcome and medical utilization, upward transfer rate, short-term and long-term mortality as secondary outcomes. Taiwan National Health Insurance 2005 Longitudinal Health Insurance Database contains one million random cases, including time-sensitive disease samples.

Results In our cohort of 9,923 cases, refined through 1:1 propensity score matching, 56% were male, mostly older adults. The CHEC policy significantly reduced medical orders and major diagnostic indicators, yet diagnostic fees notably increased. This led to a decrease in mortality rates, ultimately lowering overall medical expenses. Septic shock cases showed marked reductions in both primary diagnosis indicators and medical orders. In contrast, primary treatment indicators for AIS and STEMI rose, supporting the hypothesis of a health policy spotlight effect.

Conclusions This study highlights the CHEC policy's dual effects on reducing costs and enhancing patient outcomes. We observed a health policy spotlight effect, which led to a disproportionate improvement in guideline adherence and process quality for CTSDs that have time-based surveillance indicators.

categorization of hospital emergency capability

quality

time-sensitive diseases

emergency care

difference-in-differences.

What is already known on this subject?

► Emergency care is a symptom-driven profession under significant uncertainty and time pressure.

►Critical time-sensitive diseases refer to life-threatening illnesses or injuries that require immediate emergency care, where rapid intervention is paramount to mitigate morbidity and mortality.

► The hospital emergency capability categorization policy aims to classify hospital care capacities, guide emergent patient transport to the nearest appropriate facilities prevent preventable deaths.

The hospital emergency capability categorization policy aims to classify hospital care capacities, guide emergent patient transport to the nearest appropriate facilities prevent preventable deaths.

What this study adds?

►The categorization of hospital emergency capability policy implementation demonstrates a dual capability to reduce costs and improve patient outcomes.

► Disease entities not fully encompassed in the surveillance indicators may be jeopardized with a decrease in diagnosis and treatment process quality.

► Health policy spotlight effect exists in critical time-sensitive diseases with time-based quality indicators, resulting in a disproportional improvement in disease guideline adherence and process quality.

Emergency care is a symptom-driven profession delivered by emergency department (ED) physicians under significant time pressure and uncertainty ¹. Emergency care providers tentatively diagnose specific diseases based on at least 70 common disease patterns encountered in the ED, often dedicating only 3% of their time for diagnosis ², despite this step representing the most important in terms of cost ². Furthermore, medical care providers are the most influential decision-makers and drive approximately 70–80% of medical utilization ³. Critical time-sensitive diseases (CTSD) refer to life-threatening illnesses or injuries that require immediate emergency care, where rapid intervention is paramount to mitigate morbidity and mortality ⁴.

The Agency for Healthcare Research and Quality proposed the concept of time-sensitive diseases ⁵ using scientific data to maintain up-to-date guidelines and launched the "get with the guidelines" campaign to establish it as the basis for surveillance indicators of process and outcome quality ⁶. Critical time-sensitive disease (CTSD) refers to life-threatening illnesses or injuries that require immediate emergency care, where rapid intervention is paramount to mitigate morbidity and mortality ⁴. The various guidelines for managing time-sensitive events emphasize the crucial importance of time. In the context of acute ischemic stroke (AIS), the "time is brain" ⁷ goal focuses on the timely administration of thrombolytic therapy; in ST-segment elevation myocardial infarction (STEMI), the "time is muscle" goal focuses on early reperfusion ⁸; in septic shock events, the "early goal" focuses on early resuscitation ⁹; and in major trauma cases, the "golden hour" goal focuses on the window of opportunity in which patients can undergo rescue operations ¹⁰.

American Medical Association issued the categorization of hospital emergency capability (CHEC) guidelines ¹¹ to classify hospitals according to their emergency care capabilities, thereby regionalization and providing emergency medical services with references to transport emergent patients to the nearest appropriate hospitals ¹², aiming to reduce preventable deaths. Most studies investigating the effects of this categorization, designation, and regionalization policy reported positive findings ^13–16. However, studies mainly focused on a single disease entity ^13–16 or region^{15 16}. The CHEC policy often implements rigid time-based surveillance indicators. These indicators can affect disease-specific guideline adherence in clinical practice because they may reshape the behaviors of ED medical providers ¹⁷. This phenomenon is related to the so-called "policy spotlight effect", which influences medical care providers' assessment of how others perceive them ¹⁸. More specifically, the policy spotlight effect refers to how medical care providers perceive how policymakers interpret surveillance indicators and adjust their process-related behaviors accordingly ¹⁹. Current emergency care policies often use time-based criteria as process quality indicators, which may exacerbate the policy spotlight effect ¹⁸; however, the unintended effects or safety concerns generated by this effect remain unclear. Therefore, this study targeted four CTSDs: AIS, STEMI, septic shock, and major trauma¹⁰. Our research presents the hypothesis that emergency care providers might inadvertently give more attention to diseases under active surveillance while potentially neglecting those not thoroughly incorporated in this observation. This focus might be based on their perception of observer expectations ²⁰. The primary aim of our research is to examine the effects of the CHEC policy on process quality and outcomes for CTSDs, addressing three research questions: 1. how does the CHEC policy impact the quality of diagnosis, treatment, and outcomes for these diseases? 2. Does a policy spotlight effect exist in this context? 3. What are the potential consequences of this policy spotlight effect?

Setting, study design and data source

Taiwan's National Health Insurance (NHI) is a single-payer compulsory social insurance system that primarily operates on a fee-for-service basis. This study is based on the NHI 2005 Longitudinal Health Insurance Database (LHID2005), which contains one million random cases, including medical records and hospital information, collected since 1995. The LHID2005 was validated as representative of medical utilization, as well as of diagnosis and treatment process and outcome quality for CTSDs ²¹.

This nationwide observational study investigates the impact of the CHEC policy, initiated in August 2009, which integrated 190 hospitals into a network focusing on acute conditions like stroke, myocardial infarction, major trauma, and perinatal care ²². We divided our analysis into two periods: pre-CHEC (August 1, 2005 - July 31, 2009) and post-CHEC (August 1, 2009 - July 31, 2011). This division aims to distinctly assess the CHEC policy's effects, distinctly from the ED Quality Improvement Plan introduced in 2012. Well-established guidelines exist for AIS, STEMI, and septic shock, whereas the guidelines for major trauma are continuously evolving due to the variability in injury mechanisms, locations, and severity. Moreover, AIS and STEMI events are stringently monitored under the CHEC policy with specific time-based quality indicators, whereas septic shock and major trauma events are not (Table 1). Thus, we selected major trauma events as a reference for our study because they were not monitored under the CHEC policy with rigid indicators. The study uses a quasi-experimental design to evaluate the quality of CTSDs care. We adopted pre and post-implementation of a CHEC policy, using 1:1 propensity score matching (PSM) to control for confounding variables. To estimate the association of the CHEC policy on process and outcomes for AIS and STEMI, we employed a difference-in-differences (DID) estimation approach. For the counterfactual, we used major trauma cases unexposed to the clinical guideline or CHEC policy time-based quality indicators as a comparison group.

Table 1

Critical time-sensitive diseases and categorization hospital emergency capability policy indicators in Taiwan
Quality indicator	Acute ischemic stroke	ST-segment elevation MI	Septic shock	Major trauma
Guidelines development	Well developed	Well developed	Well developed	Developing
Major diagnosis indicator	Brain imaging (CT&MRI)	EKG	Blood Culture	Image Study
Major treatment indicator	iv-TPA	PCI	Antibiotics	Rescue operation
Guideline’s major goal	Early thrombolysis (Time is brain)	Early reperfusion (Time is muscle)	Goal-directed therapy (Early goal)	Rescue operation (Golden hour)
Guideline time-based criteria	60 min	90 min	3–6 hours	1 hour
CHEC policy indicators	Stroke team NIHSS score evaluation Intravenous t-PA	PCI team Give Aspirin and Clopidogrel	ICU critical care team	Trauma team ISS evaluation
CHEC policy time-based criteria	Neurologist consultation < 30 mins Door to CT < 30 mins Door to CT read < 45 mins Onset to needle < 3 hours	Cardiologist consultation < 30 min Door to EKG < 10 mins Door to needle < 30 mins Door to balloon < 90 mins	Admission < 24 hours	Trauma team activation < 30 mins
Source: Taiwan Ministry of Health and Welfare: The Statistics and Trends in Health and Welfare 2014.
CHEC: Categorization Hospital Emergency Capability; CT: Computed tomography; EKG: Electrocardiography; ED: emergency department; ICU: Intensive Care Unit; ISS: Injury Severity Score; IV-tPA: intravenous tissue plasminogen activator; NIHSS: National Institute of Health Stroke Scale/Score; PCI: Percutaneous coronary intervention

Identification of study cohort

This study identified CTSD based on ED visits accompanied by a primary diagnosis using the appropriate disease code. The identification of AIS (codes 433 and 434), STEMI (code 410), and septic shock (codes 038, 785, and 995) was based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9‐CM). Major trauma cases were classified following the American Academy of Surgery Committee guidelines (codes 800–959) ²³. Due to the absence of trauma severity data in the LHID2005 database, primary ICD-9-CM codes served as our initial method for identifying major trauma incidents. This identification was further refined by including cases where patients received rescue surgery or were admitted to the ICU, serving as additional criteria for major trauma ²⁴. We excluded cases that occurred before the start of the study period and those that lacked hospital or patient sociodemographic information. We also excluded hospitals with a volume of CTSD cases lower than five per year ²⁵. We used the date of the first ED visit as the index date.

Variable definitions

Independent variable in this study was exposure to the CHEC policy intervention. Events related to AIS and STEMI were specifically subject to rigid time-based quality indicators and regular surveillance under the CHEC policy. While septic shock has well-developed clinical guidelines, it is not subject to the CHEC policy's rigid, time-based quality indicators. Similarly, major trauma cases, which lack well-developed clinical guidelines, are also not subject to these CHEC policy indicators and are unexposed as counterfactual. Dependent variables included guideline adherence rate of diagnosis and treatment process quality indicator as primary outcomes (Table 1), upward transfer rate, diagnostic fees, medical orders and expense, and 30-day and one-year mortality as secondary outcomes. Covariates including patients' predisposing factors included age, sex, and occupation; enable factor was insured salary, and Charlson Comorbidity Index (CCI) score one year before the index date as a need factor. External environmental factors included urbanization and regional emergency resources. Hospital-level variables followed the input-throughput-output model of Asplin et al. ²⁶, focusing on the rate of ED visits with triage severity levels Ⅰ and Ⅱ to gauge input. We assessed throughput and output efficiency via the ED's occupancy rate. The CCI score used to categorize patient comorbidities, was calculated using ICD-9-CM codes from primary diagnoses in both inpatient and outpatient claims data up to a year before the index date.

Statistical Analysis

Patients' characteristics, process quality, and outcomes were presented using descriptive statistics. Continuous data were described using mean ± standard deviation, and categorical data were presented using numbers and percentages. To enhance the robustness of the outcomes, propensity score was calculated using a multivariable logistic regression that included all baseline covariates in Table 2. The standardized mean difference (SMD) was calculated to confirm the balance of potential confounders at baseline between groups before and after matching. An SMD of less than 0.1 was considered to represent a negligible difference ²⁷.

Table 2

Propensity Score Matched Comparison of Patient Characteristics Pre- and Post-CHEC Policy
	Before propensity score matching			After propensity score matching
	Before CHEC policy (N = 28829)	After CHEC policy (N = 14534)	ASMD^a	Before CHEC policy (N = 9923)	After CHEC policy (N = 9923)	ASMD
Sex
Female	12345 (42.82%)	6250 (43.00%)	0.004	4357 (43.91%)	4268 (43.01%)	0.018
Male	16484 (57.18%)	8284 (57.00%)	0.004	5566 (56.09%)	5655 (56.99%)	0.018
Age
≤ 45	4105 (14.24%)	1967 (13.53%)	0.021	1289 (12.99%)	1306 (13.16%)	0.005
45–64	7173 (24.88%)	3795 (26.11%)	0.028	2550 (25.70%)	2590 (26.10%)	0.009
≥ 65	17551 (60.88%)	8772 (60.36%)	0.011	6084 (61.31%)	6027 (60.74%)	0.012
Charlson Comorbidity Index
≤ 1	13215 (45.84%)	6546 (45.04%)	0.016	4473 (45.08%)	4434 (44.68%)	0.008
> 1	15614 (54.16%)	7988 (54.96%)	0.016	5450 (54.92%)	5489 (55.32%)	0.008
Income
<=22800	15961 (55.36%)	6203 (42.68%)	0.256	4428 (44.62%)	4604 (46.40%)	0.036
> 22800	12868 (44.64%)	8331 (57.32%)	0.256	5495 (55.38%)	5319 (53.60%)	0.036
Occupation
Dependents of the insured	10095 (35.02%)	4952 (34.07%)	0.020	3550 (35.78%)	3326 (33.52%)	0.048
Civil servants, teachers, military	2792 (9.68%)	1529 (10.52%)	0.028	986 (9.94%)	1053 (10.61%)	0.022
Nonmanual workers and professionals	2231 (7.74%)	1240 (8.53%)	0.029	748 (7.54%)	796 (8.02%)	0.018
Manual workers	10250 (35.55%)	5154 (35.46%)	0.002	3422 (34.49%)	3708 (37.37%)	0.060
Others	3461 (12.01%)	1659 (11.41%)	0.019	1217 (12.26%)	1040 (10.48%)	0.056
Hospital categorization
Severe level	11371 (39.44%)	5924 (40.76%)	0.027	3331 (33.57%)	3502 (35.29%)	0.036
Moderate level	10921 (37.88%)	5496 (37.81%)	0.001	4030 (40.61%)	4052 (40.83%)	0.004
General level	6537 (22.68%)	3114 (21.43%)	0.030	2562 (25.82%)	2369 (23.87%)	0.045
ESI triage level Ⅰ and Ⅱ rate	5.8 ± 8.98	16.63 ± 10.06	1.136	14.01 ± 10.39	14.37 ± 10.50	0.034
Length of ED stay	5.33 ± 10.11	4.49 ± 9.30	0.086	6.25 ± 9.30	5.36 ± 10.26	0.091
ED observation ≧ 1-day rate	11.37 ± 9.97	12.32 ± 10.06	0.095	10.53 ± 9.34	11.32 ± 10.12	0.081
Place of ED resources
Sufficiency	22770 (78.98%)	11460 (78.85%)	0.003	7768 (78.28%)	7834 (78.95%)	0.016
Not sufficiency	6059 (21.02%)	3074 (21.15%)	0.003	2155 (21.72%)	2089 (21.05%)	0.016
Time-sensitive disease
Acute ischemic stroke	8660 (30.04%)	3814 (26.24%)	0.085	2895 (29.17%)	2895 (29.17%)	0.000
ST-segment elevation MI	2481 (8.61%)	1141 (7.85%)	0.028	723 (7.29%)	723 (7.29%)	0.000
Septic shock	14896 (51.67%)	8275 (56.94%)	0.106	5441 (54.83%)	5441 (54.83%)	0.000
Major trauma	2792 (9.68%)	1304 (8.97%)	0.024	864 (8.71%)	864 (8.71%)	0.000
Care delivered by:
Specialty Consultant	18692 (64.84%)	9245 (63.61%)	0.026	6738 (67.9%)	6570 (66.21%)	0.036
Emergency physician	7788 (27.01%)	4236 (29.15%)	0.048	2449 (24.68%)	2585 (26.05%)	0.031
Others	2349 (8.15%)	1053 (7.25%)	0.034	736 (7.42%)	768 (7.74%)	0.012
ASMD: absolute standardized mean difference; CHEC: Categorization Hospital Emergency Capability; ED: emergency department; ESI: Emergency Severity Index; Specialty Consultant: 1. acute ischemic stroke is treated by neurologists, 2. acute myocardial infarction is treated by cardiologists, 3. septic shock is managed by internal medicine physicians or critical care intensivists, 4. major trauma conditions are managed by surgeons or critical care intensivists.

We evaluated the impact of the CHEC intervention on each outcome, including overall, within individual diseases, and between-disease differences in change from baseline (group-by-disease interaction effects) by generalized estimating equation (GEE) models. The β coefficients of the group-by-disease interaction terms, estimated from the GEE models, indicate the difference in outcome change in each disease relative to the reference group (major trauma) between pre- and post-CHEC. A positive group-by-disease interaction β coefficient indicates that the outcome change for that disease is greater compared to the reference group. All analyses were performed using SAS version 9.4. All statistical tests were 2-sided; p-value < 0.05 was considered statistically significant.

Participants characteristics

During the study period, we analyzed emergency presentations related to four CTSDs, originally encompassing 288,443 cases. Exclusion criteria included the diagnosis of CTSD before 2005 (n = 99,768), patients with transient ischemic attack or intracranial hemorrhage (n = 878), non-STEMI (n = 1,315), and individuals with major traumas defined by ICD codes that did not necessitate a rescue operation or ICU admission (n = 142,446), and cases lacking hospital or living area information, or where the hospital's volume of CTSD was less than five visits per year (n = 673). These criteria refined the total sample size to 43,363 cases. Considering the extended period before the policy intervention, this research adopted a 1:1 PSM technique, resulting in a final matched sample of 9,923. The flow chart and baseline table (Fig. 1) display the initial count of emergency CTSD patients and the numbers post-PSM, broken down by each of the four diseases. Table 2 presents the PSM of CTSD participants before and after the PSM. After the matching process, each variable baseline characteristic demonstrates almost complete congruity. Additionally, uniformity is achieved within each disease sub-group post-matching (supplementary table 1). The distribution for each disease pre and post-PSM was as follows: AIS (n = 2,895), STEMI (n = 723), septic shock (n = 5,441), and major trauma (n = 864).

Septic shock was observed to be more pervasive, representing 54% of all cases. The patient population was male-dominated (56%), with the most represented age group being those aged 65 and above (60%). Nearly three-fourths of the incidents involving CTSD were handled in hospitals that were categorized as moderate (40%) or severe (35%) levels. Consultant specialists delivered care accounted for two-thirds of cases.

Impact of CHEC policy on overall and individual four CTSDs’ process and outcome before and after implementation

In examining individual diseases, primary diagnostic indicators for AIS, septic shock, and major trauma decreased post-intervention, while only STEMI increased (Table 3). Diagnostic fees increased for AIS, STEMI, and major trauma but decreased for septic shock. A similar trend was observed in primary treatment indicators, with AIS and STEMI increased and septic shock and major trauma decreased. In contrast, medical orders showed a universal decline. Upward transfer rates rose for AIS and major trauma, while a decrease in STEMI and septic shock. Regarding outcome indicators, short-term mortality rates displayed a universal decline, and long-term mortality rates followed suit, except for AIS, which showed an increase. The medical expenses were higher for AIS and STEMI but lower for septic shock and major trauma.

Table 3

Comparative analysis of the individual and overall impact of CHEC policy effects for critical time-sensitive diseases
	Change between pre- & post-CHEC				Before CHEC policy (N = 9923)		After CHEC policy (N = 9923)		Multivariable model Β [95% CI]		p-value
Outcome	Acute ischemic stroke^a	STEMI^b	Septic shock^c	Major trauma^d	Before CHEC policy (N = 9923)		After CHEC policy (N = 9923)		Multivariable model Β [95% CI]		p-value
Process quality
Major diagnosis indicator	-3.52	0.55	-2.28	-3.01	8682	(87.49%)	8434	(84.99%)	-0.21	(-0.29 to -0.13)	< .0001
Diagnostic fees	460.66	2746.6	-44.8	762.85	7166.94	(10018.69)	7543.31	(10833.26)	376.37	(92.42 to 660.33)	0.0094
Major treatment indicator	0.62	2.07	-1.25	-3.12	4334	(43.68%)	4272	(43.05%)	-0.03	(-0.07 to 0.02)	0.2415
Medical orders per case	-0.93	-4.21	-9.67	-16.13	102.13	(109.19)	94.84	(101.07)	-7.29	(-10.09 to -4.48)	< .0001
Upward transfer rate	0.59	-1.24	-0.1	2.43	148	(1.49%)	172	(1.73%)	0.15	(-0.06 to 0.37)	0.1648
Outcomes
30-days mortality	-0.04	-1.52	-1.84	-1.27	1631	(16.44%)	1508	(15.20%)	-0.09	(-0.17 to -0.02)	0.0137
One-year mortality	0.28	-0.83	-3.64	-0.58	3242	(32.67%)	3041	(30.65%)	-0.09	(-0.15 to -0.04)	0.0013
Total medical expense per case	3616.16	11219.01	-11059.09	-13056.54	100875.96	(192912.94)	95547.60	(176487.79)	-5328.35	(-10387.10 to -269.60)	0.0390
^a Acute ischemic stroke major diagnosis indicator: head image; major treatment indicator: IV-tPA thrombolysis
^b ST-elevation MI major diagnosis indicator: EKG; major treatment indicator: PCI
^c Septic shock major diagnosis indicator: culture; major treatment indicator: antipathogen medication
^d Major trauma major diagnosis indicator: CT or MRI or sonography study; major treatment indicator: rescue operation
CHEC: Categorization Hospital Emergency Capability; CI: confident confidence interval; EKG: Electrocardiography; IV-tPA: intravenous tissue plasminogen activator; PCI: Percutaneous coronary intervention; STEMI: ST-segment elevation myocardial infarction

In assessing the overall policy effects on four CTSD cohorts, notably, the primary diagnosis indicator significantly decreased by 0.21 percentage points (95% CI: -0.29 to -0.13, p < 0.0001); medical orders per case dropped by an average of 7.29 items (95% CI: -10.09 to -4.48, p < 0.0001). In comparison, diagnostic fees demonstrated an average increase of $376.37 (95% CI: 92.42 to 660.33, p = 0.0094). The 30-day mortality rate saw a notable reduction of 0.09 percentage points (95% CI: -0.17 to -0.02, p = 0.0137), one-year mortality significantly decreased by 0.09 percentage points (95% CI: -0.15 to -0.04, p = 0.0013) and medical expense per case significantly decreased by $5328.35 (95% CI: -10387.10 to -269.60, p = 0.0390).

Association of CHEC policy with process and outcomes quality in four CTSDs

In model 1, we analyze the variations in several indicators before and after the implementation of CHEC for individual diseases. For AIS, following the implementation of CHEC, there was a significant decrease in major diagnosis indicators by 0.23 percentage points (95% CI: -0.36 to -0.10, p = 0.0005) (Table 4). Conversely, major treatment indicator experienced a significant rise of 0.57 percentage points (95% CI: 0.07 to 1.07, p = 0.0263), and upward transfer rate also significantly increased by 0.52 percentage points (95% CI: 0.02 to 1.03, p = 0.0399). Meanwhile, there was also a trend of increasing diagnostic costs, with a rise of $460.66 (95% CI: -3.44 to 924.76, p = 0.0517). For STEMI, the diagnostic fees significantly increased by $2746.59 (95% CI: 1141.67 to 4351.51, p = 0.0008). When examining septic shock, the major diagnosis indicator saw a significant decrease of 0.25 percentage points (95% CI: -0.37 to -0.12, p < 0.0001) following the introduction of CHEC. 30-day mortality decreased by 0.11% (95% CI: -0.20 to -0.02, p = 0.0189), and 1-year mortality decreased by 0.15% (95% CI: -0.22 to -0.07, p = 0.0001). Additionally, medical orders significantly dropped by 9.67 items (95% CI: -13.99 to -5.35, p < 0.0001), and average medical expenses significantly fell by $11059.10 (95% CI: -18603.60 to -3514.55, p = 0.0041). Finally, in the case of major trauma, post-CHEC implementation, the average medical orders significantly decreased by 16.13 items (95% CI: -25.32 to -6.94, p = 0.0006).

Table 4

Association of CHEC policy with process and outcomes quality in four critical time-sensitive diseases
	Before CHEC policy	After CHEC policy	Change between pre & post CHEC	Model 1^α (95% CI)^a	P value	Model 2^β (95% CI)^b	P value
Acute ischemic stroke^a (N = 2895)
Major diagnosis indicator	2391 (82.59%)	2289 (79.07%)	-3.52	-0.23 (-0.36 to -0.10)	0.0005	-0.06 (-0.32 to 0.20)	0.6635
Diagnostic fees	6050.63 (10934.56)	6511.29 (6895.16)	460.66	460.66 (-3.44 to 924.76)	0.0517	-302.19 (-1419.15 to 814.77)	0.5959
Major treatment indicator	24 (0.83%)	42 (1.45%)	0.62	0.57 (0.07 to 1.07)	0.0263	0.77 (0.21 to 1.33)	0.0068
Medical orders per case	70.44 (71.03)	69.51 (74.08)	-0.93	-0.93 (-4.64 to 2.78)	0.6228	15.20 (5.28 to 25.11)	0.0027
Upward transfer rate	25 (0.86%)	42 (1.45%)	0.59	0.52 (0.02 to 1.03)	0.0399	0.21 (-0.39 to 0.81)	0.4929
Short-term mortality (30 days)	140 (4.84%)	139 (4.80%)	-0.04	-0.01 (-0.25 to 0.23)	0.951	0.11 (-0.27 to 0.49)	0.5687
Long-term mortality (365 days)	441 (15.23%)	449 (15.51%)	0.28	0.02 (-0.12 to 0.16)	0.7668	0.06 (-0.22 to .033)	0.6874
Total medical expense per case	58995.28 (161260.94)	62611.44 (113039.39)	3616.16	3616.15 (-3524.26 to 10756.56)	0.3209	16672.69 (-3581.75 to 36927.12)	0.1067
ST-segment elevation MI^b (N = 723)
Major diagnosis indicator	671 (92.81%)	675 (93.36%)	0.55	0.09 (-0.32 to 0.49)	0.6767	0.26 (-0.21 to 0.72)	0.2767
Diagnostic fees	6269.07 (11425.79)	9015.67 (19940.85)	2746.6	2746.59 (1141.67 to 4351.51)	0.0008	1983.75 (84.28 to 3883.21)	0.0407
Major treatment indicator	240 (33.20%)	255 (35.27%)	2.07	0.09 (-0.11 to 0.30)	0.3824	0.30 (-0.03 to 0.62)	0.0729
Medical orders per case	92.31 (90.33)	88.10 (88.05)	-4.21	-4.21 (-13.14 to 4.72)	0.3556	11.92 (-0.90 to 24.73)	0.0684
Upward transfer rate	39 (5.39%)	30 (4.15%)	-1.24	-0.28 (-0.76 to 0.21)	0.2654	-0.59 (-1.18 to -0.001)	0.0496
Short-term mortality (30 days)	144 (19.92%)	133 (18.40%)	-1.52	-0.10 (-0.36 to 0.16)	0.4531	-0.02 (-0.37 to 0.41)	0.9247
Long-term mortality (365 days)	217 (30.01%)	211 (29.18%)	-0.83	-0.04 (-0.26 to 0.18)	0.7227	-0.01 (-0.33 to .032)	0.9745
Total medical expense per case	108481.43 (144206.04)	119700.44 (166898.18)	11219.01	11219.00 (-4953.90 to 27391.90)	0.174	24275.54 (-640.71 to 49191.78)	0.0562
Septic shock^c (N = 5441)
Major diagnosis indicator	4941 (90.81%)	4817 (88.53%)	-2.28	-0.25 (-0.37 to -0.12)	< .0001	-0.08 (-0.33 to 0.18)	0.5573
Diagnostic fees	7360.02 (9079.06)	7315.22 (10611.73)	-44.8	-44.80 (-412.26 to 322.66)	0.8111	-807.65 (-1888.03 to 272.74)	0.1429
Major treatment indicator	3894 (71.57%)	3826 (70.32%)	-1.25	-0.06 (-0.14 to 0.02)	0.1434	0.14 (-0.12 to 0.41)	0.2824
Medical orders per case	120.70 (124.08)	111.03 (112.49)	-9.67	-9.67 (-13.99 to -5.35)	< .0001	6.45 (-3.70 to 16.61)	0.2129
Upward transfer rate	21 (0.39%)	16 (0.29%)	-0.1	-0.27 (-0.93 to 0.38)	0.4125	-0.59 (-1.32 to 0.15)	0.1165
Short-term mortality (30 days)	1234 (22.68%)	1134 (20.84%)	-1.84	-0.11 (-0.20 to -0.02)	0.0189	-0.01 (-0.29 to 0.31)	0.9539
Long-term mortality (365 days)	2398 (44.07%)	2200 (40.43%)	-3.64	-0.15 (-0.22 to -0.07)	0.0001	-0.11 (-0.36 to 0.13)	0.3588
Total medical expense per case	115832.23 (206098.13)	104773.14 (198516.37)	-11059.09	-11059.10 (-18603.60 to -3514.55)	0.0041	1997.45 (-18403.00 to 22397.86)	0.8478
Major trauma^d (N = 864)
Major diagnosis indicator	679 (78.59%)	653 (75.58%)	-3.01	-0.17 (-0.39 to 0.05)	0.1359	Major trauma as reference group
Diagnostic fees	10442.74 (10411.94)	11205.59 (11322.87)	762.85	762.85 (-253.13 to 1778.82)	0.1411
Major treatment indicator	176 (20.37%)	149 (17.25%)	-3.12	-0.21 (-0.46 to 0.05)	0.1086
Medical orders per case	99.56 (103.19)	83.43 (93.90)	-16.13	-16.13 (-25.32 to -6.94)	0.0006
Upward transfer rate	63 (7.29%)	84 (9.72%)	2.43	0.31 (-0.02 to 0.65)	0.0650
Short-term mortality (30 days)	113 (13.08%)	102 (11.81%)	-1.27	-0.12 (-0.41 to 0.17)	0.4286
Long-term mortality (365 days)	186 (21.53%)	181 (20.95%)	-0.58	-0.03 (-0.27 to 0.20)	0.7710
Total medical expense per case	140654.57 (215834.78)	127598.03 (194545.71)	-13056.54	-13056.50 (-32010.60 to 5897.53)	0.1770
^αModel 1: Compare the differences between post-policy and pre-policy.
^βModel 2: The model adjusted estimates for an interaction between a binary measure of CHEC policy (post-implementation vs. pre-implementation) and critical time-sensitive diseases compared with major trauma (e.g.. acute ischemic stroke vs. major trauma; ST-segment elevation MI vs. major trauma; septic shock vs. major trauma)
CHEC: Categorization Hospital Emergency Capability.
^a Acute ischemic stroke major diagnosis indicator: head image; major treatment indicator: IV-tPA thrombolysis
^b ST-elevation MI major diagnosis indicator: EKG; major treatment indicator: PCI
^c Septic shock major diagnosis indicator: culture; major treatment indicator: antipathogen medication
^d Major trauma major diagnosis indicator: CT or MRI or sonography study; major treatment indicator: rescue operation
CHEC: Categorization Hospital Emergency Capability; CI: confident confidence interval; EKG: Electrocardiography; IV-tPA: intravenous tissue plasminogen activator; STEMI: ST-segment elevation myocardial infarction; PCI: Percutaneous coronary intervention

In Model 2 results from the GEE model highlight the CHEC policy's varied effects across different disease outcomes. Compared to the major trauma, AIS exhibited a significant increase in the major treatment indicator (Interaction β = 0.77; 95% CI = 0.21 to 1.33; p = 0.0068) and medical orders (Interaction β = 15.20; 95% CI = 5.28 to 25.11; p = 0.0027) between pre- & post-CHEC. Meanwhile, STEMI demonstrated diagnostic fees significantly increased (Interaction β = 1983.75; 95% CI = 84.28 to 3883.21; p = 0.0407) and significant decrease in upward transfer rate (Interaction β=-0.59; 95% CI=-1.18 to -0.001; p = 0.0496) compared to the major trauma, additionally, there was a trend of increasing major treatment indicators (Interaction β = 0.30; 95% CI: -0.03 to 0.62, p = 0.0729), medical orders (Interaction β = 11.92; 95% CI=-0.90 to 24.73; p = 0.0684), and medical expense (Interaction β = 24275.54; 95% CI=-640.71 to 4991991.78; p = 0.0562). In septic shock, compared to major trauma, there were no significant differences observed in either process or outcome quality.

Evaluating the impact of a health policy exposed to an entire nationwide population presents challenges due to the absence of a control group. This restriction only permits pre- and post-implementation comparisons and complicates understanding the potential interplay of various disease shifts under the influence of policy is a significant challenge. Nevertheless, our study aims to thoroughly analyze such a policy, investigating its differential impacts across various time-sensitive diseases. By carefully examining each disease's guidelines, we discern the unique effects of the policy on each one. This process allows us to identify the groups affected by the policy and those serving as relatively unaffected counterfactuals, thereby giving us insights into the policy's impacts and difference-in-differences comparison.

The overall impact of the CHEC policy

Our study utilized real-world data to illustrate that the enforcement of the CHEC policy has led to a substantial reduction in overall medical orders and major diagnostic indicators. Nevertheless, there has been a conspicuous increase in diagnostic fees; this could be potentially attributed to stringent time indicators, as expedited diagnoses are crucial. Consequently, there could be an augmentation in the charges associated with other differential diagnoses. Moreover, the implementation of the CHEC policy has also resulted in a decrease in mortality rates, ultimately contributing to a reduction in overall medical expenses. Thus, the effectiveness and efficiency of the CHEC policy underscore its dual ability to decrease costs while improving patient outcomes. However, a crucial question remains unanswered: Why has the CHEC policy been effective in significantly reducing medical costs and mortality rates, even when the overall major diagnosis indicator has declined and no substantial change has been observed in treatment quality indicators? The following sections will delve further into diseases' individual and interactive effects to provide an analysis.

Individual disease response to CHEC policy

Implementing the CHEC policy aims to improve the quality of the diagnosis and treatment process, enable rapid diagnosis and timely treatment, efficiently reduce unnecessary examinations, and streamline management, leading to lower diagnostic cost and medical orders. As hospitals improve their capabilities in emergency medical care, the need for patient transfers is anticipated to reduce. Through the provision of timely and appropriate treatment, the ultimate objectives of the policy are to lower mortality rates and curtail overall healthcare utilization.

When examining individual disease policy effects, in the case of AIS, we observed a decrease in diagnostic indicators, which might be attributable to an increase in patient upward transfer rates and possibly suggests that hospital physicians may be more inclined to transfer AIS patients to higher-level hospitals to increase their chances of receiving thrombolytic treatment ²⁸. Therefore, it presents a significant increase in the primary treatment indicators. There has been a substantial increase in diagnostic fees. c seemed to be adversely affected by the policy intervention, with increased medical orders, which led to increased diagnostic costs, upper transfer rates, long-term mortality rates, and overall medical costs. In the context of STEMI, there is an increase in diagnostic fees and a tendency to increase diagnostic and treatment indicators while transfer rates have decreased. This suggests a more intensive treatment approach for STEMI, correlating with decreased mortality rates. Conversely, there was a decrease in major diagnostic and treatment indicators and medical orders for septic shock and major trauma exhibited a decline in primary diagnosis indicators and primary treatment indicators also saw an increase in transfer rates. The differential impact on diagnostic costs, treatment indicators, transfer rates, long-term mortality rates, and medical costs among different diseases highlights the differential influence of policy effects. This could indirectly suggest the impact of the policy spotlight effect on AIS and STEMI conditions, while septic shock and major trauma may deteriorate due to the absence of policy monitoring.

Association of diseases with policy spotlight effect

Given the setting of this study, all emergency medical services are exposed to the CHEC policy, potentially subjecting emergency medical personnel to effects ranging from the Hawthorne effect and the policy spotlight effect. To differentiate the behaviors of healthcare providers under the influence of the Hawthorne effect or the policy spotlight effect, we selected AIS and STEMI diseases with well-established guidelines and time-based quality indicators under the CHEC policy. Septic shock, a disease with well-established guidelines but without specific time-based quality indicators, and major trauma, a disease without well-established guidelines or specific time-based quality indicators, were selected as external controls. Based on these natural quasi-experimental conditions, we hypothesize that the response observed may suggest varying levels of awareness among emergency care providers ²⁹. The Hawthorne effect demonstrates that the productivity of individuals in an experiment may increase simply because they are being observed, reflecting the influence of human attention and intervention on behavior ³⁰. The policy spotlight effect may be intensified by factors such as time constraints, ambiguous symptom patterns, and time-based quality surveillance indicators, prompting emergency care providers to unconsciously adopt selective behaviors concentrating on specific diseases according to policy targets ³⁰. Consequently, diseases not prioritized by the policy, such as septic shock and major trauma in this study, may experience a significant decrease in process quality and outcome.

When using major trauma as a reference group, both AIS and STEMI, diseases monitored by time-based indicators, exhibited an increase in the medical orders corresponding to treatment indicators. Moreover, there was an apparent increase in diagnostic and overall healthcare expenses in the STEMI cohort and a decreased transfer rate. In contrast, no significant changes were observed for the septic shock group, which was not under time indicator monitoring when compared to the major trauma reference group. These contrasting developments illustrate the divergent trends between time-based and non-time-based monitored time-sensitive diseases. We attribute these differences to the potential influence of the policy spotlight effect. The two diseases, AIS and STEMI, which are under the spotlight effect of policy, have shown an increase in the overall medical orders, diagnostic fees and medical expenses compared to the reference group, subsequently increasing the treatment indicators. As hypothesized, the septic shock group did not demonstrate significant changes in comparison to the major trauma reference group, underscoring the influence of time-based monitoring on these particular outcomes.

Discrepancy and unintended consequences

While both AIS and STEMI appeared to be influenced by the policy spotlight effect, they showed entirely opposite results in diagnostic indicators and transfer rates. This could potentially be attributed to the inherent differences between AIS and STEMI. For instance, only about 1%~2% of all AIS patients underwent primary treatment with intravenous thrombolytic agents, which is far lower than STEMI major treatment rate. Unexpectedly, in less policy spotlight affected groups such as septic shock and major trauma, despite significant decreases in primary diagnostic indicators, total medical orders, and diagnostic fees, there were unexpected reductions in 30-day and one-year mortality rates and overall medical expenses per event. These findings may align with the American 'less is more' initiative, suggesting that minimizing excessive diagnoses and unnecessary procedures could potentially enhance patient outcomes and yield cost reductions.

Policy implications

Currently, many emergency care policies implement time-based criteria ^31–33, such as the UK’s 4-hour standard ³³ and the four-hour rule ³¹. Australia's experience showed that an emergency care policy using time-based criteria can improve emergency congestion without increasing the rate of ED re-visits. However, in New Zealand, a policy in effect during 2006–2012 dictated that emergent patients must be hospitalized, transferred, or discharged within six hours of visiting the ED. After emergency care policy intervention, the length of ED stay decreased while the treatment outcomes of acute myocardial infarction, severe septic shock, and acute appendicitis did not improve significantly ³². Similarly, after the Canadian Emergency Observation Reduction Program implementation, the length of ED stay decreased while the treatment quality indicators for acute myocardial infarction, asthma, and upper limb fractures can only be treated in time for the above-mentioned time-sensitive diseases during the non-congested emergency period ³⁴.

Emergency care quality is closely related to the practices of medical care providers ¹⁷. Policymakers and medical care providers must reconsider the conventional emphasis on time-based process indicators. This approach may have unintended consequences for diseases that are outside the "spotlight" of rigid time-sensitive evaluation. Instead, a broader and more nuanced evaluation of emergency care quality is needed to incorporate the complexity and ambiguity of various time-sensitive diseases that emergency care providers often manage ³⁵. Therefore, we propose replacing time-based indicators with performance-based indicators, such as those exemplified in the NHS's Best Practice Tariff policy ³⁶. This shift towards 'best practice' can create a more flexible approach that goes beyond merely relying on time-based or diagnosis-based practices ³⁷. Such a transition is crucial, as an excessive reliance on diagnostic tests may lead to ED crowding, a decline in emergency care quality, and increased safety issues ³⁸. The complexities of emergency care, including diverse patient origins and multiple routes to specialized units like stroke units, demand more sophisticated quality measures. These may encompass factors such as the presence of a consultant and the need for critical care admissions. In the case of specific procedures like diagnostic angiography and PCI (where indicated) within 72 hours of admission with NSTEMI, rigid time-based quality indicators may be especially inadequate. Performance-based measures, such as those used in the "Best Practice Tariff," reward best practices in care delivery and foster a higher standard of healthcare.

Strengths

To the best of our knowledge, this was the first nationwide retrospective cohort study using data on different CTSDs the effects of a policy exposed to an entire population. In the scenario that often presents challenges due to the lack of a control group, we identified differential impacts on various diseases through detailed analysis, establishing affected and counterfactual groups. This process allowed us to explore pre- and post-intervention effects, comprehending the policy's overall impact. This structure provides essential insights into the impact of hospital categorization on the processes and outcomes of emergency care. Further, it offers crucial empirical evidence that could be instrumental in refining policies and optimizing emergency care systems. This study explored emergency care providers' behaviors under time constraints and how they interacted with strict time-based quality surveillance indicators and “get with the guidelines” adherence.

Limitations

Because the study data were retrieved from a secondary dataset of insurance claims not a randomized controlled trial, this study has the following limitations: (1) our analysis lacked detailed information on time-related quality indicators, such as door-to-evaluation and door-to-treatment times measurement; (2) Our study's sample definitions were based on emergency primary diagnosis, making the emergency ICD code diagnosis potentially imprecise highlighting the need for a more comprehensive approach ³⁹ (3) The effectiveness of emergency medical care often hinges on the cooperation between emergency physicians and the consulting physicians. This study is unable to dissect this relationship explicitly. Evaluating how emergency physicians and consulting physicians collaborate and coordinate is a key focus for future research.; (4) we need more qualitative research to elucidate the psychological mechanisms through which the policy spotlight effect influences emergency care providers' behaviour.

Our study used real-world evidence to demonstrate that CHEC policy implementation demonstrates a dual capability to reduce costs and improve patient outcomes. Health policy spotlight effect resulting in a disproportional improvement in disease guideline adherence rates and process quality of CTSD with time-based surveillance indicators. In contrast, disease entities not fully encompassed in the surveillance indicators may be jeopardized with a decrease in diagnosis and treatment process quality, thereby highlighting an unintended consequence of the policy. We propose a transition from time-based process indicators to performance-based indicators that may potentially improve efficiency and quality of care.

AIS

acute ischemic stroke

ASMD

absolute standardized mean difference

CCI

Charlson Comorbidity Index

CHEC

categorization of hospital emergency capability

confident confidence interval

computed tomography

CTSD

critical time-sensitive diseases

DID

difference-in-differences

emergency department

EKG

electrocardiography

ESI

Emergency Severity Index

ICD-9-CM

International Classification of Diseases, 9th Revision, Clinical Modification

ICU

intensive care unit

ISS

Injury Severity Score

IV-tPA

intravenous tissue plasminogen activator

JMIR

Journal of Medical Internet Research

LHID2005

NHI 2005 Longitudinal Health Insurance Database

MRI

Magnetic Resonance Imaging

NHI

National Health Insurance

NIHSS

National Institute of Health Stroke Scale

PCI

percutaneous coronary intervention

PSM

propensity score matching

SMD

standardized mean difference

STEMI

ST-segment elevation myocardial infarction

Ethics approval and consent to participate

This study approved by Institutional Review Board of National Yang-Ming University-YM107035E on May, 5 2018. In accordance with regulations of the National Health Research Institutes, patient identification information was anonymized, such that informed consent was not required.

Consent for publication

Not applicable.

Availability of data and materials

The data that support the findings of this study are available from Taiwan National Health Insurance Research Database but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the academic request and with permission of Taiwan National Health Insurance Administration.

Competing interests

The authors declare that they have no competing interests.

Funding

The authors declare that they have no funding.

Authors' contributors

CY Lin took a lead role in conceptualizing the study and writing the original draft, and was responsible for formal data analysis. CY Lin also verified the underlying data in the manuscript. CC Liu contributed to study design, data curation, and formal data analysis, and was in charge of data collection. CC Liu YT Huang ensured accurate data analysis and interpretation, and verified the manuscript's underlying data. YC Lee supervised the study, validated the results, and significantly contributed to reviewing and editing the manuscript. All authors participated in developing the study concept and design, analysing and interpreting data, and preparing the manuscript. We have all approved the final manuscript and agree to be accountable for all aspects of the work, promising to appropriately investigate and resolve any question related to the work's accuracy or integrity.

Acknowledgments

We acknowledge the Taiwan National Health Insurance Research Database, which was provided by the National Health Insurance Administration and is managed by National Health Research Institutes.

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Health Policy Spotlight Effects on Critical Time-Sensitive Diseases: Evidence from Taiwan Categorization of Hospital Emergency Capability Policy

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Abstract

Figures

Highlights

What is already known on this subject?

What this study adds?

INTRODUCTION

METHODS

Setting, study design and data source

Identification of study cohort

Variable definitions

Statistical Analysis

RESULTS

Participants characteristics

Association of CHEC policy with process and outcomes quality in four CTSDs

DISCUSSION

The overall impact of the CHEC policy

Individual disease response to CHEC policy

Association of diseases with policy spotlight effect

Discrepancy and unintended consequences

Policy implications

Strengths

Limitations

CONCLUSION

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

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