Providers and purchasers Readiness for Case-Based Payment and its Systemic Constraints in Ethiopia: A Mixed-Methods Study

Providers and purchasers Readiness for Case-Based Payment and its Systemic Constraints in Ethiopia: A Mixed-Methods Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Providers and purchasers Readiness for Case-Based Payment and its Systemic Constraints in Ethiopia: A Mixed-Methods Study Afework Tadele, Hayat Mohammed, Animut Ayalew Redatie, Samuel Abera Zewdie, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8454222/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Ethiopia’s Health Sector Transformation Plan II prioritizes transitioning from fee-for-service to Case-Based Payment (CBP) to enhance efficiency and equity. This study assesses the readiness of healthcare providers and purchasers for CBP implementation. Methods: A sequential exploratory mixed-methods study was conducted (Sep-Oct 2025) across three regions and Addis Ababa in Ethiopia. Data included 30 key informant interviews, 8 focus group discussions, and structured surveys with 344 facilities and 57 purchaser institutions. Qualitative data were analyzed thematically; quantitative data using descriptive statistics. Results: Provider readiness is hampered by a critical digital divide: while 98.8% use DHIS2 for aggregate reporting, only 40.4% have functional Electronic Medical Records (EMRs), and existing systems lack bundled-pricing capability. Problems​‍​‌‍​‍‌ to data integrity have been identified with coding sometimes being a nurse's task and the use of rule-out diagnoses. Major differences in development level are observed at regions level, with Addis Ababa showing advanced readiness while Somali (15.3%) and Southwest Ethiopia (46.5%) are lagging behind. Purchaser institutions appear to have a strong strategic design, however, they face critical operational gaps: 95% of them are dependent on spreadsheets for claims processing, they do not have automated fraud detection and they encounter a median claim rejection rate of 4%, which is mainly due to incomplete documentation (100%). Conclusion: The implementation of CBP in Ethiopia can be successful only if deep-rooted systemic constraints are addressed. The report recommends focusing on integrated digital infrastructure renovation, capacity building for data integrity on a nationwide scale, and aligning operational protocols to the UHC goals while making them more ​‍​‌‍​‍‌efficient. Case-Based Payment Health Financing Readiness Assessment Digital Health Ethiopia Universal Health Coverage Introduction Ethiopia’s healthcare financing system is undergoing a strategic transformation guided by the Health Care Financing Strategy 2022–2031, which seeks to establish a more equitable and sustainable system by increasing domestic resource mobilization [ 1 ]. Central to this transformation is the pursuit of Universal Health Coverage (UHC), which requires innovative provider payment mechanisms that ensure financial sustainability without compromising the quality of care [ 2 ]. Among these mechanisms, Case-Based Payment (CBP) has emerged as a promising alternative to fee-for-service models, with global evidence demonstrating its potential to enhance efficiency and contain costs [ 3 , 4 ]. CBP operates by reimbursing hospitals a fixed rate for clinically similar cases, creating incentives for efficiency and resource optimization [ 5 ]. In various low- and middle-income settings, CBP has been associated with reduced hospital stays and fewer inappropriate admissions [ 3 , 5 – 8 ]. However, international experience also highlights mixed outcomes and implementation risks. While systems such as DRGs can improve quality metrics like readmission rates, they may also lead to unintended consequences such as increased admissions, upcoding, or cost-shifting [ 2 , 9 , 10 ]. Financial protection for patients can improve with timely reimbursements, though administrative delays or complex hybrid models may hinder access [ 11 , 12 ]. Furthermore, successful implementation depends on precise case classification, balanced payment rates, and safeguards against potential inequities and perverse incentives—challenges that must be carefully managed to align CBP with Ethiopia’s goals of sustainable, quality healthcare [ 2 , 11 , 13 ]. Given this complex landscape, a systematic assessment of institutional readiness is critical before nationwide rollout. Transitioning to CBP within Ethiopia’s multi-level health system comprising the Ethiopian Health Insurance Service (EHIS), Community-Based Health Insurance (CBHI) schemes, and diverse public and private providers requires robust capacity in clinical coding, health information management, claims processing, and financial administration [ 1 , 14 ]. Without adequate preparation, hasty implementation risks undermining care quality, exacerbating access inequities, or encouraging gaming of the reimbursement system [ 14 ]. This study therefore conducts a comprehensive, mixed-methods readiness assessment of both providers and purchasers within Ethiopia’s healthcare financing ecosystem. By identifying existing capabilities and systemic bottlenecks, it aims to generate actionable evidence to inform a phased, context-sensitive implementation strategy ensuring that the shift to CBP strengthens, rather than strains, Ethiopia’s progress toward UHC [ 15 ]. Methods 2.1 Study Design and Setting This study employed a sequential exploratory mixed-methods design conducted in Ethiopia between September and October 2025. The research was carried out across three regional states (Oromia, Somali, and Southwestern) and the Addis Ababa city administration to ensure geographical and systemic diversity. The assessment targeted the full spectrum of the healthcare financing ecosystem, including healthcare providers (general and specialized hospitals, and health centers) and purchaser institutions (Regional Health Bureaus (RHBs), Ethiopian Health Insurance Service (EHIS) clusters, Community-Based Health Insurance (CBHI) units, and the federal-level EHIA/CBHI office). The sequential design began with an initial qualitative phase to explore contextual nuances and identify key themes. These insights were used to inform and refine the subsequent quantitative phase, designed to measure the prevalence and distribution of the identified factors. The process concluded with a final qualitative phase to refine findings and validate strategic action plans, ensuring a deep, contextually grounded understanding while generating measurable data on system readiness. 2.2 Data Collection Data were collected in sequential phases, beginning with qualitative exploration followed by a structured quantitative assessment. 2.2.1 Qualitative Exploration This initial phase aimed to map the operational landscape and inform the development of quantitative survey tools. Key Informant Interviews (KIIs) : Over 30 exploratory KIIs were conducted with a purposively selected range of senior stakeholders from both provider and purchaser entities. The provider group included senior administrative and clinical leaders (e.g., Clinical Directors, Quality Officers, and Finance Heads) from various tiers of the health system. Concurrently, KIIs were held with senior officials from purchaser organizations, including EHIS and CBHI officials at Federal, Regional, and Zonal levels. Interviews utilized open-ended questions to explore perceived readiness challenges, detailed CBP requirements, and systemic capacity gaps. Focus Group Discussions (FGDs) : Eight FGDs were held with frontline personnel responsible for implementing CBP. These included hospital staff in selected regions and operational units within purchaser agencies. Participants included OPD/IPD Directors, Emergency Department Heads, and heads of Pharmacy, Laboratory, and Quality units. Discussions focused on daily workflows, potential documentation and data management challenges, and specific training needs. The qualitative data tools were developed and added as a supplementary file below (Supplementary file 1_Qualitative Tools). 2.2.2 Quantitative Assessment Structured tools developed from the qualitative findings were deployed to collect measurable data. Healthcare Provider Readiness Survey : A structured questionnaire was administered to 344 hospitals and a stratified random sample of health centers. Facility management teams (e.g., CEO, HMIS, Finance, and Clinical Department Heads) were surveyed on domains including Health Information System (HIS) capabilities, claims processing, and CBP readiness. Data were collected electronically via tablets using Open Data Kit (ODK). Purchaser Preparedness Assessment : A total of 57 purchaser institutions were assessed using a structured institutional tool. The tool evaluated operational capacity, data systems, and CBP management functions. Data were gathered through semi-structured interviews and document review conducted by senior researchers. The quantitative data tools were developed and added as a supplementary file below (Supplementary file 2_Quantitative Tools). 2.3 Data Management and Analysis Qualitative Analysis : All KIIs and FGDs were audio-recorded, transcribed verbatim, and managed using ATLAS.ti software. Data were coded and analyzed using a thematic analysis approach within a framework methodology to identify key patterns and themes. Quantitative Analysis : Quantitative data captured electronically via ODK underwent rigorous validation checks. Analysis was performed using Stata version 17 and consisted of descriptive statistics (frequencies, percentages, means, medians). A formal gap analysis was conducted comparing the current state against predefined CBP requirements. Integration : Findings from the qualitative and quantitative strands were integrated through triangulation to confirm and explain results. Joint displays were used to visualize how qualitative themes and quantitative data converged or expanded understanding, providing robust mixed-method insights. 2.4 Quality Assurance Rigorous quality assurance measures were implemented throughout the study. All data collection tools were pre-tested and refined. Enumerators and qualitative data collectors underwent a comprehensive, standardized training program. Field supervision, peer debriefing during analysis, and maintaining audit trails for qualitative data enhanced the reliability and validity of the findings. Results Participant Demographics and Characteristics A total of 344 participants responded to the study, achieving an impressive response rate of 89.6%. The participants were drawn from various regional states, with Oromia representing the largest group at 42.6%, followed by Somali (24.8%) and Southwest Ethiopia (20.7%). In terms of facility type, the majority were affiliated with general hospitals (42.7%) and specialized hospitals (34.3%), indicating a focus on larger healthcare institutions. Regarding roles within their institutions, health experts and consultants comprised the largest segment at 74.4%, with finance experts and HIS technicians being less common. Professionally, the respondents were predominantly nurses (51.3%), followed by physicians (9.9%) and pharmacists (8.7%), reflecting a strong representation of nursing staff. The gender distribution showed a slight male majority, with 54.9% male and 45.1% female participants. The mean age of respondents was 31.1 years (SD = 6.6), with an average total work experience of 8.2 years (SD = 6.2) and 5.3 years (SD = 4.8) of experience in their current institution. This detailed characterization highlights the diverse and qualified group of healthcare professionals involved in the assessment of readiness for the case-based payment system implementation in Ethiopia (Table 1 ). Table 1 Demographic and Professional Characteristics of Study Participants Variables Categories Frequency (percentages) Regional states Addis Abeba 41 (12.0%) Oromia 146 (42.6%) Somali 85 (24.8%) Southwest Ethiopia 71 (20.7%) Facility type General Hospital 147 (42.7%) Health Center 44 (12.8%) Primary Hospital 35 (10.2%) Specialized hospital 118 (34.3%) Role in this institution Finance expert 28 (8.1%) HIS technician 15 (4.4%) Head 7 (2.0%) Health expert/consultant 256 (74.4%) Other (specify 38 (11.0%) Profession Physician (all type) 34 (9.9%) Nurse (all type) 176 (51.3%) Health officer 12 (3.5%) Environmental health 1 (0.3%) HIT 17 (5.0%) Midwifery 18 (5.2%) laboratory 19 (5.5%) Pharmacist 30 (8.7%) Accountant 25 (7.3%) Others a 11 (3.3%) Sex of respondent female 155 (45.1%) male 189 (54.9%) Educational status, median (IQR) BSc 238 (69.2%) Diploma 44 (12.8%) MSc/MPH 54 (15.7%) Specialty certificate 8 (2.3%) Total years of experience (in years) mean (SD) 8.2 (6.2) experience in the current institution (in years) mean (SD) 5.3 (4.8) Age of respondent mean (SD) 31.1 (6.6) a Biomedical Engineer, Computer science, Health informatics, human resource management Management, & public health 3.1 Provider Readiness: Operational Fragility amidst Strategic Intent The assessment of the healthcare facilities revealed a landscape characterized by strong foundational capacities undermined by critical systemic weaknesses, particularly in digital infrastructure, data quality, and human resources. Digital Infrastructure: The Paradox of High Adoption vs. Functional Failure , nearly all facilities (98.8%) reported using the DHIS2 platform for aggregate health reporting. In stark contrast, only 40.4% had an Electronic Medical Record (EMR) system in place, and functionality was a major concern (Table 2 ). Qualitative data exposed this as a critical operational flaw. Existing EMRs were described as structurally incapable of supporting CBP’s core function: "Our EMR is not price bundled... But the case based payment need bundled price" (Finance Head, Addis Ababa). Furthermore, systems were operationally fragile, with external vendor dependency causing frequent downtimes that local IT staff could not resolve. The near-universal DHIS2 adoption paints a misleading picture of digital readiness, as CBP requires robust, patient-level transactional data systems, not just aggregate reporting tools. Table 2 Overview of HIS Capabilities and coding infrastructure of health facilities in the study areas Variables Categories Frequency (percentages) HIS Capabilities (n = 344) DHIS2 No 4 (1.2%) Yes 340 (98.8%) Laboratory system No 236 (68.6%) Yes 108 (31.4%) Pharmacy No 233 (67.7%) Yes 111 (32.3%) EMR No 205 (59.6%) Yes 139 (40.4%) Diagnoses coded in the system (in percent) (n = 213) mean (SD) 83.1 (23.7) Data Integrity: High Volume Masks Compromised Quality While facilities reported a high percentage of diagnoses coded (mean 83.1%, SD 23.7), qualitative evidence revealed severe threats to the validity of this data. The coding process was often delegated to non-clinicians: "Since most of the seniors and GPs don't involve on this, the nurses attempt to code and report" (MCH Head, Bonga). Diagnoses were frequently based on "rule-out" assessments due to limited diagnostic capacity, and the national single-disease reporting system failed to capture clinical complexity. This misalignment means the data intended to feed CBP costing and reimbursement is clinically and financially unreliable from the outset. Human Resources: A Skilled but Burdened and Unstable Workforce The workforce was quantitatively experienced (mean 8.2 years total experience) and dominated by nurses (51.3% of respondents) (Table 1 ). Qualitatively, this translated into a burdened workforce where nurses were saddled with data tasks outside their clinical role, leading to burnout and inaccuracies. Compounding this was a "leaky bucket" of institutional knowledge due to high turnover among key clinical staff like General Practitioners (GPs), eroding capacity for accurate clinical coding and CBP management. Financial Constraints: A Root Cause of Systemic Gaps A striking 84.3% of facilities reported inadequate budgets, while 43.3% experienced delayed fund disbursement (Table 3 ). Qualitative insights directly linked this financial scarcity to operational failures, explaining the lack of diagnostic tools, fragile digital systems, and inability to retain staff. The widespread budget inadequacy is a root cause constraining all other readiness domains. Table 3 summary of Financial Constraints in Healthcare Institutions in the study area Variables Categories Frequency (percentages) Delayed fund disbursement No 195 (56.7%) Yes 149 (43.3%) Inadequate budget No 54 (15.7%) Yes 290 (84.3%) Poor accounting skills No 289 (84.0%) Yes 55 (16.0%) Fraud/leakage No 322 (93.6%) Yes 22 (6.4%) Other challenges No 307 (89.2%) Yes 37 (10.8%) Total value of exempted services provided in the last quarter (in ETB) mean (SD) 72,600.0 (202,885.4) Regional Disparities: A Two-Tiered System Quantitative data revealed stark inequities in CBP readiness across regions (Table 4 ). While Addis Ababa showed advanced readiness across most domains (e.g., 100% for measurement outcomes), Somali and Southwest Ethiopia lagged significantly (e.g., 15.3% and 46.5% advanced readiness for technological dashboards, respectively). Qualitative evidence contextualized these disparities as differences in fundamental operational maturity, exemplified by descriptions of "half paper and half EMR" systems in less-ready regions. Table 4 Readiness Levels for Case-Based Payment Implementation across Regional States in Ethiopia CBP Readiness domains Level of readiness Regional states Addis Ababa Oromia Somali Southwest Ethiopia p-value Measurement outcome level Advanced 41 (100.0%) 121 (82.9%) 82 (96.5%) 71 (100.0%) < 0.001 Intermediate 0 (0.0%) 9 (6.2%) 1 (1.2%) 0 (0.0%) Preliminary 0 (0.0%) 16 (11.0%) 2 (2.4%) 0 (0.0%) Organization tracks quality measures Advanced 41 (100.0%) 110 (75.3%) 80 (94.1%) 70 (98.6%) < 0.001 Intermediate 0 (0.0%) 20 (13.7%) 4 (4.7%) 1 (1.4%) Preliminary 0 (0.0%) 16 (11.0%) 1 (1.2%) 0 (0.0%) Population health management metrics Advanced 40 (97.6%) 133 (91.1%) 80 (94.1%) 63 (88.7%) 0.13 Intermediate 0 (0.0%) 1 (0.7%) 2 (2.4%) 4 (5.6%) Preliminary 1 (2.4%) 12 (8.2%) 3 (3.5%) 4 (5.6%) Leadership value transformation Advanced 41 (100.0%) 116 (79.5%) 59 (69.4%) 48 (67.6%) < 0.001 Intermediate 0 (0.0%) 8 (5.5%) 8 (9.4%) 20 (28.2%) Preliminary 0 (0.0%) 22 (15.1%) 18 (21.2%) 3 (4.2%) Technological Capability dashboards Advanced 36 (87.8%) 98 (67.1%) 75 (88.2%) 33 (46.5%) < 0.001 Intermediate 3 (7.3%) 16 (11.0%) 3 (3.5%) 7 (9.9%) Preliminary 2 (4.9%) 32 (21.9%) 7 (8.2%) 31 (43.7%) Technological Capability data for analysis Advanced 40 (97.6%) 73 (50.0%) 77 (90.6%) 65 (91.5%) < 0.001 Intermediate 1 (2.4%) 35 (24.0%) 3 (3.5%) 6 (8.5%) Preliminary 0 (0.0%) 38 (26.0%) 5 (5.9%) 0 (0.0%) Partnerships and Collaborative for transformation Advanced 26 (63.4%) 89 (61.0%) 13 (15.3%) 22 (31.0%) < 0.001 Intermediate 3 (7.3%) 11 (7.5%) 11 (12.9%) 15 (21.1%) Preliminary 12 (29.3%) 46 (31.5%) 61 (71.8%) 34 (47.9%) Partnerships and Collaborative for performance-based contracts Advanced 32 (78.0%) 82 (56.2%) 4 (4.7%) 31 (43.7%) < 0.001 Intermediate 4 (9.8%) 0 (0.0%) 0 (0.0%) 4 (5.6%) Preliminary 5 (12.2%) 64 (43.8%) 81 (95.3%) 36 (50.7%) Partnerships and Collaborative for health equity strategy Advanced 41 (100.0%) 121 (82.9%) 64 (75.3%) 65 (91.5%) 0.003 Intermediate 0 (0.0%) 5 (3.4%) 1 (1.2%) 1 (1.4%) Preliminary 0 (0.0%) 20 (13.7%) 20 (23.5%) 5 (7.0%) EMR configured for data access Advanced 31 (75.6%) 16 (11.0%) 49 (57.6%) 11 (15.5%) < 0.001 Intermediate 9 (22.0%) 42 (28.8%) 0 (0.0%) 21 (29.6%) Preliminary 1 (2.4%) 88 (60.3%) 36 (42.4%) 39 (54.9%) EMR configured for data access Advanced 39 (95.1%) 93 (63.7%) 9 (10.6%) 65 (91.5%) < 0.001 Intermediate 1 (2.4%) 33 (22.6%) 25 (29.4%) 5 (7.0%) Preliminary 1 (2.4%) 20 (13.7%) 51 (60.0%) 1 (1.4%) EMR configured for risk-based contracts Advanced 28 (68.3%) 66 (45.2%) 3 (3.5%) 28 (39.4%) < 0.001 Intermediate 1 (2.4%) 24 (16.4%) 2 (2.4%) 4 (5.6%) Preliminary 12 (29.3%) 56 (38.4%) 80 (94.1%) 39 (54.9%) N 41 146 85 71 3.2 Purchaser Readiness: Strategic Design Confronts Operational Reality The assessment of 57 purchaser institutions (EHIS, CBHI, RHBs) uncovered a critical disconnect between well-developed strategic plans and archaic operational systems. Strategic Design vs. Operational Reality Purchaser organizations demonstrated strong strategic and legal foundations. Qualitatively, officials cited a clear mandate and detailed preparatory work: "We collected expenditure data and done normative cost... So currently we have finished the design phase" (EHIS Official, Addis Ababa). Quantitatively, this was reflected in highly experienced staff (median 10 years experience) and near-universal documentation of Standard Operating Procedures (SOPs) (98%) (Tables 5 & 6 ). However, 68% of these SOPs were never updated, signaling a gap between documented procedures and dynamic operational needs. Table 5 Organizational and Respondents Characteristics in Ethiopia Variables Categories Frequency (Percentages) Region Addis Abeba 10 (18%) Oromia 23 (40%) Somali 16 (28%) Southwest Ethiopia 8 (14%) Name of organization EHIS Branch 17 (30%) EHIS Head Quarter 8 (14%) EHIS Jijiga Branch 9 (16%) Jimma Zone HD 8 (14%) Somali region health Bureau 7 (12%) Southsest EHIS branch 1 (2%) Southwest regional health bureau 7 (12%) Purchaser type CBHI units 19 (33%) EHIS clusters 19 (33%) Other(specify) 3 (5%) RHB finance teams 3 (5%) federal EHIA/CBHI 13 (23%) Role in this institution Finance expert 14 (25%) Head 4 (7%) Health expert/consultant 24 (42%) Administrative staff 15 (26%) Profession Public Health 18 (31.6%) Nurse (all type) 13 (22.8%) Physician (all type) 2 (3.5%) Accounting and finance 15 (26.3%) Management 4 (7.0%) Economics 2 (3.5%) Other (specify) 3 (5.3%) Educational status BSc 35 (61%) Diploma 1 (2%) MSc/MPH 20 (35%) Specialty certificate 1 (2%) Sex of respondent Female 17 (30%) Male 40 (70%) Age of respondent (in years) median (IQR) 35.0 (32.0, 38.0) Total experience (in years) median (IQR) 10.0 (8.0, 15.0) Experience in this institution (in years) median (IQR) 5.0 (3.0, 8.0) N 57 Digital Infrastructure as the Critical Bottleneck The operational reality was defined by a profound digital deficit. A striking 95% of purchasers relied on spreadsheets for claims processing, and none had automated fraud detection capabilities (Table 6 ). This manual, inefficient reality was acutely recognized by purchasers themselves, who qualitatively identified digital systems as the "non-negotiable gatekeeper." Their cautious implementation strategy—limiting the initial pilot to hospitals with mature EMRs—was a direct response to their own inability to manage complex claims data at scale. Table 6 Data Analysis Capabilities among CBP Purchasers in Ethiopia Variables Categories Frequency (Percentages) Systems used for claims processing Dedicated software No 54 (95%) Yes 3 (5%) Spreadsheets No 3 (5%) Yes 54 (95%) Manual ledgers No 36 (63%) Yes 21 (37%) System to generate real-time reports Claims volume No 37 (65%) Yes 20 (35%) Rejection rates No 39 (68%) Yes 18 (32%) Provider performance No 49 (86%) Yes 8 (14%) Expenditure trends No 48 (84%) Yes 9 (16%) System integrated with national health databases (HMIS, CBHI) No 39 (68%) Yes 18 (32%) Automated fraud detection capabilities: No 57 (100%) N 57 Deferred Financial Safeguards and Process Inefficiencies Purchasers were keenly aware of financial risks like upcoding and fraud but had deferred key mitigations. Quantitatively, risk adjustments for case severity (12%) or inflation (4%) were rarely applied (Table 7 ). The claims adjudication process was already problematic: the primary reason for rejection was incomplete documentation (100% of purchasers), leading to a median rejection rate of 4% and an average processing time of 30 days. Qualitatively, officials openly acknowledged these unaddressed risks: "We anticipate underpayment for the facilities... There may be up-coding... Fraud also may happen" (EHIS Official, Addis Ababa). This combination of manual processes, high error rates, and lacking safeguards creates a high-risk environment for CBP implementation. Table 7 Purchasers’ practices of reimbursement mechanisms for health care payments in Ethiopia Variables Categories Frequency (Percentages) Reimbursement methods Capitation No 23 (40%) Yes 34 (60%) case-based No 57 (100%) Fee-for-Service Yes 1 (2%) No 56 (98%) line item No 52 (91%) Yes 5 (9%) Main reasons for claim rejection incomplete documentation Yes 25 (100%) coding errors No 19 (76%) Yes 6 (24%) Ineligible services No 4 (16%) Yes 21 (84%) duplicate claims No 21 (84%) Yes 4 (16%) fraud No 18 (72%) Yes 7 (28%) Reimbursement rates adjusted Geography No 53 (93%) Yes 4 (7%) Facility level No 5 (9%) Yes 52 (91%) Case severity No 50 (88%) Yes 7 (12%) Inflation No 55 (96%) Yes 2 (4%) Timeliness of reimbursements 31–60 days 29 (51%) > 60 days 19 (33%) <=30 days 9 (16%) Average claim processing time median (IQR) 30 (25, 60) Claim rejection rate last quart median (IQR) 4 (0, 10) N 57 Systemic Fragmentation Qualitative data further revealed an ecosystem challenge: blurred purchaser-provider lines and multiple, fragmented purchasing entities complicate accountability and data flow, posing a significant threat to the coherence and sustainability of the reform. Discussion This mixed-methods readiness assessment reveals a critical juncture in Ethiopia’s health financing reform. The findings illuminate not merely gaps in capacity, but fundamental systemic misalignments that threaten to undermine the transition to CBP. The paradoxes identified between digital reporting and transaction-ready systems, between data quantity and quality, and between strategic intent and operational capability collectively suggest that successful implementation requires a foundational strengthening of the health system itself. A central finding of this study is the dangerous chasm between Ethiopia’s high-performance aggregate reporting system (DHIS2) and its underdeveloped, non-interoperable transactional digital infrastructure. While DHIS2 adoption at 98.8% indicates strong capacity for public health monitoring and performance tracking, CBP operates on an entirely different digital plane. It requires real-time, patient-level data exchange, precise clinical coding integrated with billing, and systems configured for bundled case pricing functions that the existing, fragmented EMR landscape (40.4% adoption) cannot reliably support [ 16 , 17 ]. This "digital cliff" poses a fundamental risk to CBP's core mechanics. Without interoperable EMRs capable of generating accurate, auditable case-cost data, the reimbursement process will rest on manual data entry and estimation, inviting errors, delays, and disputes. This finding echoes global lessons where payment reforms faltered not due to policy design, but due to incompatible information systems that failed to operationalize the new financing logic [ 17 ]. The assessment underscores the peril of relying on superficial quantitative metrics as proxies for readiness. The high rate of diagnoses coded (83.1%) creates an illusion of data preparedness. However, qualitative evidence exposes this metric as deeply flawed, revealing coding performed by overburdened nurses based on "rule-out" diagnoses and constrained by a single-disease reporting framework. For CBP, the financial and clinical validity of each coded diagnosis is paramount, as it directly determines reimbursement and quality measurement [ 17 ]. Implementing a sophisticated payment model on this compromised data foundation risks institutionalizing inaccurate payments, rewarding incorrect coding over quality care, and generating misleading performance metrics. This aligns with documented risks in other settings where "upcoding" and data manipulation emerged as unintended consequences of poorly implemented case-based systems [ 18 , 19 ]. The study identifies a potentially crippling asymmetry between purchaser and provider readiness. On one side, purchaser institutions (EHIS) exhibit advanced strategic readiness a clear legal mandate, costed models, and a phased implementation plan. On the other side, they suffer from deficient operational readiness, relying on spreadsheets (95%) and lacking automated fraud detection. They are poised to interface with provider facilities that demonstrate strong leadership will but fragile operational foundations, including budget inadequacy (84.3%) and unstable human resources. This asymmetry creates a high-risk implementation environment. Strategically prepared purchasers may design sound contracts, but their manual adjudication systems will struggle to process claims efficiently. Providers, eager to engage but lacking the digital tools and data integrity to submit clean claims, will face high rejection rates (currently 4% median, primarily for documentation). This dynamic can quickly erode trust, cause cash flow crises for providers, and stall the reform, as seen in hybrid payment model challenges elsewhere [ 20 ]. The collective findings argue compellingly for a strategic pivot. The priority must shift from merely piloting a new payment mechanic to making integrated investments in the foundational pillars of the health system that are prerequisites for any advanced financing model. Invest in Interoperable Digital Infrastructure The focus must move beyond expanding EMR access to mandating and funding the configuration of CBP-specific, interoperable modules . This includes bundled price functionality, integrated clinical and financial dashboards, and standardized data exchange protocols between provider EMRs and a modernized purchaser claims platform. Investment in local IT capacity is equally critical to reduce vendor dependency and ensure system sustainability. Stabilize and Empower the Health Workforce : Addressing human resource readiness requires moving beyond one-off training. It necessitates a dual strategy: formalizing the role of clinical coders to relieve nurses of inappropriate data burdens, and implementing retention incentives for key clinical staff (e.g., GPs) in underserved regions to stem the "leaky bucket" of institutional knowledge. Ethics and safeguarding training must be integrated to mitigate concerns over perverse incentives. Institute Robust Data Governance A "data integrity initiative" is urgently needed. This involves piloting a multi-diagnosis reporting system to reflect clinical complexity, establishing routine data quality audits, and creating clear protocols for coding responsibility and validation. Purchasers must simultaneously develop the deferred financial safeguards, such as risk-adjustment models and anti-fraud frameworks, using data from the pilot phase. Conclusion Ethiopia's commitment to CBP is a bold step toward a more sustainable and equitable health system. However, this assessment reveals that the path forward is not a straightforward technical rollout. The identified "digital cliff," "illusion of readiness," and "asymmetric preparedness" are symptoms of systemic gaps that must be addressed proactively. The proposed cautious, phased pilot is prudent, but its success and subsequent scale-up depend entirely on parallel, concerted investments in digital infrastructure, human resources, and data governance. By reframing CBP implementation as a catalyst for holistic health system strengthening, Ethiopia can ensure that the reform not only succeeds in its immediate goals but also advances the broader objectives of Universal Health Coverage. Abbreviations CBHI: Community-Based Health Insurance; CBP: Case-Based Payment; DHIS2: District Health Information Software 2; EHIA: Ethiopian Health Insurance Agency; EHIS: Ethiopian Health Insurance Service; EMR: Electronic Medical Record; FGD: Focus Group Discussion; GP: General Practitioner; HIT: Health Information Technician; IQR: Interquartile Range; KII: Key Informant Interview; MCH: Maternal and Child Health; MoH: Ministry of Health; MPH: Master of Public Health; MSc: Master of Science; ODK: Open Data Kit; RHB: Regional Health Bureau; SD: Standard Deviation; SOP: Standard Operating Procedure; UHC: Universal Health Coverage. Declarations Ethical Considerations Ethical approval for this study was obtained from the Institutional Review Board of Jimma University (Approval No: JUIH/IRB/0518/25). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments. Operational permissions from the Federal Ministry of Health (MoH), EHIA, and all relevant Regional Health Bureaus (RHBs). Verbal informed consent was obtained from all participants prior to data collection. To ensure confidentiality, all data were stored securely, personal identifiers were removed, and findings are reported in aggregate form. The principle of beneficence was upheld by using non-threatening questions and framing the study constructively as a system-level improvement effort. Authors' contributions – AT: Conception and design, literature review, data analysis, interpretation and drafting of the manuscript; TKC involved securing funding for the project; HM, AAR, SAZ, EB, BD, FB, SN, AJ, KH, ST, HG, and GD involved in supervision, and quality assessment and reviewed the manuscript. All authors have read and approved the manuscript. Availability of data and material: The data analyzed for this study will be available based upon request. Funding: This work is supported by funding from Korea Foundation for International Healthcare (KOFIH), as part of the project “Providers and Purchasers Readiness for Case‑Based Payment in Ethiopia. Acknowledgements: We gratefully acknowledge the contributions of the research team from Jimma University, the Ethiopian Health Insurance Service (EHIS), Community‑Based Health Insurance (CBHI) units, and Regional Health Bureaus (RHBs). The Ministry of Health of Ethiopia provided operational guidance and permissions. Competing interests: The authors declare no conflicts of interest in the preparation of this policy brief. The funding organization KOFIH had no role in the design, analysis, or interpretation of findings, and none of the individuals or institutions involved stand to benefit directly or be negatively affected by the policy options presented Consent for publication: Not applicable. References Federal Ministry of Health Ethiopia: Health Care Financing Strategy 2022 – 2031 . In . ; 2022. Zhang X, Qian M, Yan J, Wang R, Lyu D, Ying X, Tang S: The Impact of a New Case-Based Payment System on Quality of Care: A Difference-in-Differences Analysis in China . Risk Management and Healthcare Policy 2024:3113-3124. Alshreef A: Achieving Universal and Sustainable Healthcare Coverage . Universal Health Coverage 2019:9. Messerle R, Schreyögg J: Country-level effects of diagnosis-related groups: evidence from Germany’s comprehensive reform of hospital payments . The European Journal of Health Economics 2024, 25 (6):1013-1030. Alexander T: Prospective Case-Based Payment for Hospitals: A Guide with Illustrations from Latin America . In . Edited by LAC HSR HSRI: Abt Associates Inc. ; 2001. Dkhimi F, Honda A, Hanson K, Mbau R, Onwujekwe O, Phuong HT, Mathauer I, Akhnif EH, Jaouadi I, Kiendrébéogo JA: Examining multiple funding flows to public healthcare providers in low-and middle-income countries—results from case studies in Burkina Faso, Kenya, Morocco, Nigeria, Tunisia and Vietnam . Health Policy and Planning 2023, 38 (10):1139-1153. World Bank Group: Transition to DiagnosisRelated Group (DRG) Payments for Health: Lessons from Case Studies . In . Edited by Caryn B, Sarah Ba, Kristiina K; 2019. Cashin C, O’Dougherty S, Samyshkin Y, Katsaga A, Ibraimova A, Kutanov Y, Lyachshuk K, Zuys O: Case-based hospital payment systems: a step-by-step guide for design and implementation in low-and middle-income countries . USA ID Zdrav Reform Project 2005. World Health Organization: Case-based Payment Systems for Hospital Funding in Asia An Investigation of Current Status and Future Directions: An Investigation of Current Status and Future Directions . 2015. Wu J, He X, Feng XL: Can case-based payment contain healthcare costs? - A curious case from China . Soc Sci Med 2022, 312 :115384. Cielo B, 2nd, Santillan M, de Claro V: Effect of a case-capped, fee-for-service payment mechanism on accessibility and affordability of health care . Health Aff Sch 2024, 2 (2):qxae004. Dalmacion GV, Juban NR, Zordilla Z: Optimizing PhilHealth’s case-based payment scheme to achieve greater financial protection . 2016. Berenson RA, Sunshine J, Deb A, Doherty JA, Kurtzman ET, Richardson E, Kalman NS, Macri J: The Effect of Provider Payment Systems on Quality, Cost and Efficiency, and Access: A Systematic Literature Review . 2012. Wyszewianski L, Thomas JW, Friedman BA: Case-based payment and the control of quality and efficiency in hospitals . Inquiry 1987, 24 (1):17-25. Ethiopian Health Insurance [https://www.moh.gov.et/en/Ethiopian_Health_Insurance?language_content_entity=en] Okunuga A: Improving Healthcare Financial Performance through Data-Driven Forecasting, Cost Modeling, and Reimbursement Optimization Tools . Jayakumar P, Mills Z, Triana B, Moxham J, Olmstead T, Wallace S, Bozic K, Koenig K: A model for evaluating total costs of care and cost savings of specialty condition-based care for hip and knee osteoarthritis in an integrated practice unit . Value in Health 2023, 26 (9):1363-1371. Wilchesky M, Tamblyn RM, Huang A: Validation of diagnostic codes within medical services claims . Journal of clinical epidemiology 2004, 57 (2):131-141. O'malley KJ, Cook KF, Price MD, Wildes KR, Hurdle JF, Ashton CM: Measuring diagnoses: ICD code accuracy . Health services research 2005, 40 (5p2):1620-1639. Moro Visconti R, Morea D: Healthcare digitalization and pay-for-performance incentives in smart hospital project financing . International journal of environmental research and public health 2020, 17 (7):2318. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1QualitativeTools.pdf Supplementaryfile2QuantitativeTools.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 Mar, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers invited by journal 30 Jan, 2026 Editor assigned by journal 27 Jan, 2026 Editor invited by journal 06 Jan, 2026 Submission checks completed at journal 05 Jan, 2026 First submitted to journal 05 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8454222","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583406505,"identity":"b4ecd8c6-6ec1-4fab-b677-679c2440c688","order_by":0,"name":"Afework 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Uneversity","correspondingAuthor":false,"prefix":"","firstName":"Temesgen","middleName":"Kabeta","lastName":"Chala","suffix":""}],"badges":[],"createdAt":"2025-12-26 09:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8454222/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8454222/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101881081,"identity":"6ecfa718-f1b5-4c1d-8f0c-9a921feb1084","added_by":"auto","created_at":"2026-02-04 15:09:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2504372,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8454222/v1/6db97818-fa43-44cc-84b7-98e70868b7b8.pdf"},{"id":101777370,"identity":"988488ea-af2c-4161-8338-3ca38265c9df","added_by":"auto","created_at":"2026-02-03 14:17:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":440342,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1QualitativeTools.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8454222/v1/2b2e70b9bd9255ed7550cd89.pdf"},{"id":101777371,"identity":"797e9d65-32c7-415c-8ccb-aca60a91171b","added_by":"auto","created_at":"2026-02-03 14:17:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":933501,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2QuantitativeTools.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8454222/v1/c8cd37ee47b91c37e249d28c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Providers and purchasers Readiness for Case-Based Payment and its Systemic Constraints in Ethiopia: A Mixed-Methods Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEthiopia\u0026rsquo;s healthcare financing system is undergoing a strategic transformation guided by the Health Care Financing Strategy 2022\u0026ndash;2031, which seeks to establish a more equitable and sustainable system by increasing domestic resource mobilization [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Central to this transformation is the pursuit of Universal Health Coverage (UHC), which requires innovative provider payment mechanisms that ensure financial sustainability without compromising the quality of care [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among these mechanisms, Case-Based Payment (CBP) has emerged as a promising alternative to fee-for-service models, with global evidence demonstrating its potential to enhance efficiency and contain costs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCBP operates by reimbursing hospitals a fixed rate for clinically similar cases, creating incentives for efficiency and resource optimization [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In various low- and middle-income settings, CBP has been associated with reduced hospital stays and fewer inappropriate admissions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, international experience also highlights mixed outcomes and implementation risks. While systems such as DRGs can improve quality metrics like readmission rates, they may also lead to unintended consequences such as increased admissions, upcoding, or cost-shifting [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Financial protection for patients can improve with timely reimbursements, though administrative delays or complex hybrid models may hinder access [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, successful implementation depends on precise case classification, balanced payment rates, and safeguards against potential inequities and perverse incentives\u0026mdash;challenges that must be carefully managed to align CBP with Ethiopia\u0026rsquo;s goals of sustainable, quality healthcare [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven this complex landscape, a systematic assessment of institutional readiness is critical before nationwide rollout. Transitioning to CBP within Ethiopia\u0026rsquo;s multi-level health system comprising the Ethiopian Health Insurance Service (EHIS), Community-Based Health Insurance (CBHI) schemes, and diverse public and private providers requires robust capacity in clinical coding, health information management, claims processing, and financial administration [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Without adequate preparation, hasty implementation risks undermining care quality, exacerbating access inequities, or encouraging gaming of the reimbursement system [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study therefore conducts a comprehensive, mixed-methods readiness assessment of both providers and purchasers within Ethiopia\u0026rsquo;s healthcare financing ecosystem. By identifying existing capabilities and systemic bottlenecks, it aims to generate actionable evidence to inform a phased, context-sensitive implementation strategy ensuring that the shift to CBP strengthens, rather than strains, Ethiopia\u0026rsquo;s progress toward UHC [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003e2.1 Study Design and Setting\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study employed a sequential exploratory mixed-methods design conducted in Ethiopia between September and October 2025. The research was carried out across three regional states (Oromia, Somali, and Southwestern) and the Addis Ababa city administration to ensure geographical and systemic diversity. The assessment targeted the full spectrum of the healthcare financing ecosystem, including healthcare providers (general and specialized hospitals, and health centers) and purchaser institutions (Regional Health Bureaus (RHBs), Ethiopian Health Insurance Service (EHIS) clusters, Community-Based Health Insurance (CBHI) units, and the federal-level EHIA/CBHI office).\u003c/p\u003e \u003cp\u003eThe sequential design began with an initial qualitative phase to explore contextual nuances and identify key themes. These insights were used to inform and refine the subsequent quantitative phase, designed to measure the prevalence and distribution of the identified factors. The process concluded with a final qualitative phase to refine findings and validate strategic action plans, ensuring a deep, contextually grounded understanding while generating measurable data on system readiness.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2 Data Collection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData were collected in sequential phases, beginning with qualitative exploration followed by a structured quantitative assessment.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2.1 Qualitative Exploration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis initial phase aimed to map the operational landscape and inform the development of quantitative survey tools.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eKey Informant Interviews (KIIs)\u003c/b\u003e: Over 30 exploratory KIIs were conducted with a purposively selected range of senior stakeholders from both provider and purchaser entities. The provider group included senior administrative and clinical leaders (e.g., Clinical Directors, Quality Officers, and Finance Heads) from various tiers of the health system. Concurrently, KIIs were held with senior officials from purchaser organizations, including EHIS and CBHI officials at Federal, Regional, and Zonal levels. Interviews utilized open-ended questions to explore perceived readiness challenges, detailed CBP requirements, and systemic capacity gaps.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFocus Group Discussions (FGDs)\u003c/b\u003e: Eight FGDs were held with frontline personnel responsible for implementing CBP. These included hospital staff in selected regions and operational units within purchaser agencies. Participants included OPD/IPD Directors, Emergency Department Heads, and heads of Pharmacy, Laboratory, and Quality units. Discussions focused on daily workflows, potential documentation and data management challenges, and specific training needs. The qualitative data tools were developed and added as a supplementary file below (Supplementary file 1_Qualitative Tools).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2.2 Quantitative Assessment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStructured tools developed from the qualitative findings were deployed to collect measurable data.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHealthcare Provider Readiness Survey\u003c/b\u003e: A structured questionnaire was administered to 344 hospitals and a stratified random sample of health centers. Facility management teams (e.g., CEO, HMIS, Finance, and Clinical Department Heads) were surveyed on domains including Health Information System (HIS) capabilities, claims processing, and CBP readiness. Data were collected electronically via tablets using Open Data Kit (ODK).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePurchaser Preparedness Assessment\u003c/b\u003e: A total of 57 purchaser institutions were assessed using a structured institutional tool. The tool evaluated operational capacity, data systems, and CBP management functions. Data were gathered through semi-structured interviews and document review conducted by senior researchers. The quantitative data tools were developed and added as a supplementary file below (Supplementary file 2_Quantitative Tools).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Management and Analysis\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eQualitative Analysis\u003c/b\u003e: All KIIs and FGDs were audio-recorded, transcribed verbatim, and managed using ATLAS.ti software. Data were coded and analyzed using a thematic analysis approach within a framework methodology to identify key patterns and themes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eQuantitative Analysis\u003c/b\u003e: Quantitative data captured electronically via ODK underwent rigorous validation checks. Analysis was performed using Stata version 17 and consisted of descriptive statistics (frequencies, percentages, means, medians). A formal gap analysis was conducted comparing the current state against predefined CBP requirements.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntegration\u003c/b\u003e: Findings from the qualitative and quantitative strands were integrated through triangulation to confirm and explain results. Joint displays were used to visualize how qualitative themes and quantitative data converged or expanded understanding, providing robust mixed-method insights.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.4 Quality Assurance\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRigorous quality assurance measures were implemented throughout the study. All data collection tools were pre-tested and refined. Enumerators and qualitative data collectors underwent a comprehensive, standardized training program. Field supervision, peer debriefing during analysis, and maintaining audit trails for qualitative data enhanced the reliability and validity of the findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Demographics and Characteristics\u003c/h2\u003e \u003cp\u003e A total of 344 participants responded to the study, achieving an impressive response rate of 89.6%. The participants were drawn from various regional states, with Oromia representing the largest group at 42.6%, followed by Somali (24.8%) and Southwest Ethiopia (20.7%).\u003c/p\u003e \u003cp\u003eIn terms of facility type, the majority were affiliated with general hospitals (42.7%) and specialized hospitals (34.3%), indicating a focus on larger healthcare institutions. Regarding roles within their institutions, health experts and consultants comprised the largest segment at 74.4%, with finance experts and HIS technicians being less common. Professionally, the respondents were predominantly nurses (51.3%), followed by physicians (9.9%) and pharmacists (8.7%), reflecting a strong representation of nursing staff. The gender distribution showed a slight male majority, with 54.9% male and 45.1% female participants.\u003c/p\u003e \u003cp\u003eThe mean age of respondents was 31.1 years (SD\u0026thinsp;=\u0026thinsp;6.6), with an average total work experience of 8.2 years (SD\u0026thinsp;=\u0026thinsp;6.2) and 5.3 years (SD\u0026thinsp;=\u0026thinsp;4.8) of experience in their current institution. This detailed characterization highlights the diverse and qualified group of healthcare professionals involved in the assessment of readiness for the case-based payment system implementation in Ethiopia (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Professional Characteristics of Study Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (percentages)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eRegional states\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAddis Abeba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOromia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthwest Ethiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFacility type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147 (42.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth Center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecialized hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118 (34.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRole in this institution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinance expert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHIS technician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth expert/consultant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e256 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther (specify\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysician (all type)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse (all type)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e176 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth officer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMidwifery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePharmacist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccountant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex of respondent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189 (54.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducational status, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBSc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e238 (69.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSc/MPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecialty certificate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal years of experience\u003c/p\u003e \u003cp\u003e(in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.2 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eexperience in the current institution (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.3 (4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of respondent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.1 (6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003e Biomedical Engineer, Computer science, Health informatics, human resource management\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eManagement, \u0026amp; public health\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.1 Provider Readiness: Operational Fragility amidst Strategic Intent\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe assessment of the healthcare facilities revealed a landscape characterized by strong foundational capacities undermined by critical systemic weaknesses, particularly in digital infrastructure, data quality, and human resources.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDigital Infrastructure: The Paradox of High Adoption vs. Functional Failure\u003c/b\u003e, nearly all facilities (98.8%) reported using the DHIS2 platform for aggregate health reporting. In stark contrast, only 40.4% had an Electronic Medical Record (EMR) system in place, and functionality was a major concern (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQualitative data exposed this as a critical operational flaw. Existing EMRs were described as structurally incapable of supporting CBP\u0026rsquo;s core function: \u003cem\u003e\"Our EMR is not price bundled... But the case based payment need bundled price\"\u003c/em\u003e (Finance Head, Addis Ababa). Furthermore, systems were operationally fragile, with external vendor dependency causing frequent downtimes that local IT staff could not resolve. The near-universal DHIS2 adoption paints a misleading picture of digital readiness, as CBP requires robust, patient-level transactional data systems, not just aggregate reporting tools.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of HIS Capabilities and coding infrastructure of health facilities in the study areas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency (percentages)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eHIS Capabilities\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDHIS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e340 (98.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLaboratory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e236 (68.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePharmacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233 (67.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (59.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (40.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiagnoses coded in the system (in percent) (n\u0026thinsp;=\u0026thinsp;213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.1 (23.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Integrity: High Volume Masks Compromised Quality\u003c/h3\u003e\n\u003cp\u003eWhile facilities reported a high percentage of diagnoses coded (mean 83.1%, SD 23.7), qualitative evidence revealed severe threats to the validity of this data. The coding process was often delegated to non-clinicians: \u003cem\u003e\"Since most of the seniors and GPs don't involve on this, the nurses attempt to code and report\"\u003c/em\u003e (MCH Head, Bonga). Diagnoses were frequently based on \"rule-out\" assessments due to limited diagnostic capacity, and the national single-disease reporting system failed to capture clinical complexity. This misalignment means the data intended to feed CBP costing and reimbursement is clinically and financially unreliable from the outset.\u003c/p\u003e\n\u003ch3\u003eHuman Resources: A Skilled but Burdened and Unstable Workforce\u003c/h3\u003e\n\u003cp\u003eThe workforce was quantitatively experienced (mean 8.2 years total experience) and dominated by nurses (51.3% of respondents) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Qualitatively, this translated into a burdened workforce where nurses were saddled with data tasks outside their clinical role, leading to burnout and inaccuracies. Compounding this was a \"leaky bucket\" of institutional knowledge due to high turnover among key clinical staff like General Practitioners (GPs), eroding capacity for accurate clinical coding and CBP management.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFinancial Constraints: A Root Cause of Systemic Gaps\u003c/h2\u003e \u003cp\u003eA striking 84.3% of facilities reported inadequate budgets, while 43.3% experienced delayed fund disbursement (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Qualitative insights directly linked this financial scarcity to operational failures, explaining the lack of diagnostic tools, fragile digital systems, and inability to retain staff. The widespread budget inadequacy is a root cause constraining all other readiness domains.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esummary of Financial Constraints in Healthcare Institutions in the study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (percentages)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDelayed fund disbursement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (56.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInadequate budget\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290 (84.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePoor accounting skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e289 (84.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFraud/leakage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e322 (93.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOther challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307 (89.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal value of exempted services provided in the last quarter (in ETB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72,600.0 (202,885.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRegional Disparities: A Two-Tiered System\u003c/h3\u003e\n\u003cp\u003eQuantitative data revealed stark inequities in CBP readiness across regions (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). While Addis Ababa showed advanced readiness across most domains (e.g., 100% for measurement outcomes), Somali and Southwest Ethiopia lagged significantly (e.g., 15.3% and 46.5% advanced readiness for technological dashboards, respectively). Qualitative evidence contextualized these disparities as differences in fundamental operational maturity, exemplified by descriptions of \"half paper and half EMR\" systems in less-ready regions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReadiness Levels for Case-Based Payment Implementation across Regional States in Ethiopia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCBP Readiness domains\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLevel of readiness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eRegional states\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAddis Ababa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOromia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSomali\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouthwest Ethiopia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMeasurement outcome level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82 (96.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOrganization tracks quality measures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (75.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (94.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70 (98.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePopulation health management metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (97.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133 (91.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (94.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63 (88.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLeadership value transformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (79.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48 (67.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTechnological Capability dashboards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (87.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75 (88.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTechnological Capability data for analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (97.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (90.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65 (91.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePartnerships and Collaborative for transformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (63.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (61.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (71.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 (47.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePartnerships and Collaborative for performance-based contracts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (78.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (56.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (95.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePartnerships and Collaborative for health equity strategy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (75.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65 (91.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEMR configured for data access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (75.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (60.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39 (54.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEMR configured for data access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (95.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (63.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65 (91.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEMR configured for risk-based contracts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (68.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreliminary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (38.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (94.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39 (54.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Purchaser Readiness: Strategic Design Confronts Operational Reality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe assessment of 57 purchaser institutions (EHIS, CBHI, RHBs) uncovered a critical disconnect between well-developed strategic plans and archaic operational systems.\u003c/p\u003e\n\u003ch3\u003eStrategic Design vs. Operational Reality\u003c/h3\u003e\n\u003cp\u003ePurchaser organizations demonstrated strong strategic and legal foundations. Qualitatively, officials cited a clear mandate and detailed preparatory work: \u003cem\u003e\"We collected expenditure data and done normative cost... So currently we have finished the design phase\"\u003c/em\u003e (EHIS Official, Addis Ababa). Quantitatively, this was reflected in highly experienced staff (median 10 years experience) and near-universal documentation of Standard Operating Procedures (SOPs) (98%) (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, 68% of these SOPs were never updated, signaling a gap between documented procedures and dynamic operational needs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOrganizational and Respondents Characteristics in Ethiopia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (Percentages)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAddis Abeba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOromia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthwest Ethiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eName of organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEHIS Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEHIS Head Quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEHIS Jijiga Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJimma Zone HD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomali region health Bureau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthsest EHIS branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthwest regional health bureau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePurchaser type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCBHI units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEHIS clusters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther(specify)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRHB finance teams\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efederal EHIA/CBHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eRole in this institution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinance expert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth expert/consultant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdministrative staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse (all type)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (22.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysician (all type)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccounting and finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther (specify)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBSc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (61%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSc/MPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecialty certificate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex of respondent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (70%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of respondent (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.0 (32.0, 38.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal experience (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0 (8.0, 15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperience in this institution (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDigital Infrastructure as the Critical Bottleneck\u003c/h2\u003e \u003cp\u003eThe operational reality was defined by a profound digital deficit. A striking 95% of purchasers relied on spreadsheets for claims processing, and none had automated fraud detection capabilities (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This manual, inefficient reality was acutely recognized by purchasers themselves, who qualitatively identified digital systems as the \u003cem\u003e\"non-negotiable gatekeeper.\"\u003c/em\u003e Their cautious implementation strategy\u0026mdash;limiting the initial pilot to hospitals with mature EMRs\u0026mdash;was a direct response to their own inability to manage complex claims data at scale.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Analysis Capabilities among CBP Purchasers in Ethiopia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency (Percentages)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSystems used for claims processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDedicated software\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpreadsheets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eManual ledgers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eSystem to generate real-time reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClaims volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (65%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRejection rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (68%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProvider performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExpenditure trends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSystem integrated with national health databases\u003c/p\u003e \u003cp\u003e(HMIS, CBHI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (68%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAutomated fraud detection capabilities:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDeferred Financial Safeguards and Process Inefficiencies\u003c/h2\u003e \u003cp\u003ePurchasers were keenly aware of financial risks like upcoding and fraud but had deferred key mitigations. Quantitatively, risk adjustments for case severity (12%) or inflation (4%) were rarely applied (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The claims adjudication process was already problematic: the primary reason for rejection was incomplete documentation (100% of purchasers), leading to a median rejection rate of 4% and an average processing time of 30 days. Qualitatively, officials openly acknowledged these unaddressed risks: \u003cem\u003e\"We anticipate underpayment for the facilities... There may be up-coding... Fraud also may happen\"\u003c/em\u003e (EHIS Official, Addis Ababa). This combination of manual processes, high error rates, and lacking safeguards creates a high-risk environment for CBP implementation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePurchasers\u0026rsquo; practices of reimbursement mechanisms for health care payments in Ethiopia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency (Percentages)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eReimbursement methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCapitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e34 (60%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecase-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFee-for-Service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (98%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eline item\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (91%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eMain reasons for claim rejection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eincomplete documentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecoding errors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIneligible services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eduplicate claims\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003efraud\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (72%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eReimbursement rates adjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGeography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (93%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFacility level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (91%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCase severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (88%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (96%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eTimeliness of reimbursements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u0026ndash;60 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;=30 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage claim processing time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (25, 60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eClaim rejection rate last quart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0, 10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSystemic Fragmentation\u003c/h2\u003e \u003cp\u003eQualitative data further revealed an ecosystem challenge: blurred purchaser-provider lines and multiple, fragmented purchasing entities complicate accountability and data flow, posing a significant threat to the coherence and sustainability of the reform.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis mixed-methods readiness assessment reveals a critical juncture in Ethiopia\u0026rsquo;s health financing reform. The findings illuminate not merely gaps in capacity, but fundamental systemic misalignments that threaten to undermine the transition to CBP. The paradoxes identified between digital reporting and transaction-ready systems, between data quantity and quality, and between strategic intent and operational capability collectively suggest that successful implementation requires a foundational strengthening of the health system itself.\u003c/p\u003e \u003cp\u003eA central finding of this study is the dangerous chasm between Ethiopia\u0026rsquo;s high-performance aggregate reporting system (DHIS2) and its underdeveloped, non-interoperable transactional digital infrastructure. While DHIS2 adoption at 98.8% indicates strong capacity for public health monitoring and performance tracking, CBP operates on an entirely different digital plane. It requires real-time, patient-level data exchange, precise clinical coding integrated with billing, and systems configured for bundled case pricing functions that the existing, fragmented EMR landscape (40.4% adoption) cannot reliably support [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This \"digital cliff\" poses a fundamental risk to CBP's core mechanics. Without interoperable EMRs capable of generating accurate, auditable case-cost data, the reimbursement process will rest on manual data entry and estimation, inviting errors, delays, and disputes. This finding echoes global lessons where payment reforms faltered not due to policy design, but due to incompatible information systems that failed to operationalize the new financing logic [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe assessment underscores the peril of relying on superficial quantitative metrics as proxies for readiness. The high rate of diagnoses coded (83.1%) creates an illusion of data preparedness. However, qualitative evidence exposes this metric as deeply flawed, revealing coding performed by overburdened nurses based on \"rule-out\" diagnoses and constrained by a single-disease reporting framework. For CBP, the financial and clinical validity of each coded diagnosis is paramount, as it directly determines reimbursement and quality measurement [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Implementing a sophisticated payment model on this compromised data foundation risks institutionalizing inaccurate payments, rewarding incorrect coding over quality care, and generating misleading performance metrics. This aligns with documented risks in other settings where \"upcoding\" and data manipulation emerged as unintended consequences of poorly implemented case-based systems [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study identifies a potentially crippling asymmetry between purchaser and provider readiness. On one side, purchaser institutions (EHIS) exhibit advanced strategic readiness a clear legal mandate, costed models, and a phased implementation plan. On the other side, they suffer from deficient operational readiness, relying on spreadsheets (95%) and lacking automated fraud detection. They are poised to interface with provider facilities that demonstrate strong leadership will but fragile operational foundations, including budget inadequacy (84.3%) and unstable human resources. This asymmetry creates a high-risk implementation environment. Strategically prepared purchasers may design sound contracts, but their manual adjudication systems will struggle to process claims efficiently. Providers, eager to engage but lacking the digital tools and data integrity to submit clean claims, will face high rejection rates (currently 4% median, primarily for documentation). This dynamic can quickly erode trust, cause cash flow crises for providers, and stall the reform, as seen in hybrid payment model challenges elsewhere [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe collective findings argue compellingly for a strategic pivot. The priority must shift from merely piloting a new payment mechanic to making integrated investments in the foundational pillars of the health system that are prerequisites for \u003cem\u003eany\u003c/em\u003e advanced financing model.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInvest in Interoperable Digital Infrastructure\u003c/strong\u003e \u003cp\u003eThe focus must move beyond expanding EMR access to \u003cem\u003emandating and funding the configuration of CBP-specific, interoperable modules\u003c/em\u003e. This includes bundled price functionality, integrated clinical and financial dashboards, and standardized data exchange protocols between provider EMRs and a modernized purchaser claims platform. Investment in local IT capacity is equally critical to reduce vendor dependency and ensure system sustainability.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStabilize and Empower the Health Workforce\u003c/b\u003e: Addressing human resource readiness requires moving beyond one-off training. It necessitates a dual strategy: formalizing the role of clinical coders to relieve nurses of inappropriate data burdens, and implementing retention incentives for key clinical staff (e.g., GPs) in underserved regions to stem the \"leaky bucket\" of institutional knowledge. Ethics and safeguarding training must be integrated to mitigate concerns over perverse incentives.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInstitute Robust Data Governance\u003c/strong\u003e \u003cp\u003eA \"data integrity initiative\" is urgently needed. This involves piloting a multi-diagnosis reporting system to reflect clinical complexity, establishing routine data quality audits, and creating clear protocols for coding responsibility and validation. Purchasers must simultaneously develop the deferred financial safeguards, such as risk-adjustment models and anti-fraud frameworks, using data from the pilot phase.\u003c/p\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eEthiopia's commitment to CBP is a bold step toward a more sustainable and equitable health system. However, this assessment reveals that the path forward is not a straightforward technical rollout. The identified \"digital cliff,\" \"illusion of readiness,\" and \"asymmetric preparedness\" are symptoms of systemic gaps that must be addressed proactively. The proposed cautious, phased pilot is prudent, but its success and subsequent scale-up depend entirely on parallel, concerted investments in digital infrastructure, human resources, and data governance. By reframing CBP implementation as a catalyst for holistic health system strengthening, Ethiopia can ensure that the reform not only succeeds in its immediate goals but also advances the broader objectives of Universal Health Coverage.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCBHI: Community-Based Health Insurance; CBP: Case-Based Payment; DHIS2: District Health Information Software 2; EHIA: Ethiopian Health Insurance Agency; EHIS: Ethiopian Health Insurance Service; EMR: Electronic Medical Record; FGD: Focus Group Discussion; GP: General Practitioner; HIT: Health Information Technician; IQR: Interquartile Range; KII: Key Informant Interview; MCH: Maternal and Child Health; MoH: Ministry of Health; MPH: Master of Public Health; MSc: Master of Science; ODK: Open Data Kit; RHB: Regional Health Bureau; SD: Standard Deviation; SOP: Standard Operating Procedure; UHC: Universal Health Coverage.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Institutional Review Board of Jimma University (Approval No: JUIH/IRB/0518/25). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments. Operational permissions from the Federal Ministry of Health (MoH), EHIA, and all relevant Regional Health Bureaus (RHBs). Verbal informed consent was obtained from all participants prior to data collection. To ensure confidentiality, all data were stored securely, personal identifiers were removed, and findings are reported in aggregate form. The principle of beneficence was upheld by using non-threatening questions and framing the study constructively as a system-level improvement effort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions –\u0026nbsp;\u003c/strong\u003eAT: Conception and design, literature review, data analysis, interpretation and drafting of the manuscript; TKC \u0026nbsp;involved securing funding for the project; HM, AAR, SAZ, EB, BD, FB, SN, AJ, KH, ST, HG, and GD involved in supervision, and quality assessment and reviewed the manuscript. All authors have read and approved the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e The data analyzed for this study will be available based upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work is supported by funding from Korea Foundation for International Healthcare (KOFIH), as part of the project “Providers and Purchasers Readiness for Case‑Based Payment in Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We gratefully acknowledge the contributions of the research team from Jimma University, the Ethiopian Health Insurance Service (EHIS), Community‑Based Health Insurance (CBHI) units, and Regional Health Bureaus (RHBs). The Ministry of Health of Ethiopia provided operational guidance and permissions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no conflicts of interest in the preparation of this policy brief. The funding organization KOFIH had no role in the design, analysis, or interpretation of findings, and none of the individuals or institutions involved stand to benefit directly or be negatively affected by the policy options presented\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFederal Ministry of Health Ethiopia: \u003cstrong\u003eHealth Care Financing Strategy 2022 \u0026ndash; 2031\u003c/strong\u003e. 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\u003cstrong\u003eValidation of diagnostic codes within medical services claims\u003c/strong\u003e. \u003cem\u003eJournal of clinical epidemiology \u003c/em\u003e2004, \u003cstrong\u003e57\u003c/strong\u003e(2):131-141.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;malley KJ, Cook KF, Price MD, Wildes KR, Hurdle JF, Ashton CM: \u003cstrong\u003eMeasuring diagnoses: ICD code accuracy\u003c/strong\u003e. \u003cem\u003eHealth services research \u003c/em\u003e2005, \u003cstrong\u003e40\u003c/strong\u003e(5p2):1620-1639.\u003c/li\u003e\n\u003cli\u003eMoro Visconti R, Morea D: \u003cstrong\u003eHealthcare digitalization and pay-for-performance incentives in smart hospital project financing\u003c/strong\u003e. \u003cem\u003eInternational journal of environmental research and public health \u003c/em\u003e2020, \u003cstrong\u003e17\u003c/strong\u003e(7):2318.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Case-Based Payment, Health Financing, Readiness Assessment, Digital Health, Ethiopia, Universal Health Coverage","lastPublishedDoi":"10.21203/rs.3.rs-8454222/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8454222/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Ethiopia’s Health Sector Transformation Plan II prioritizes transitioning from fee-for-service to Case-Based Payment (CBP) to enhance efficiency and equity. This study assesses the readiness of healthcare providers and purchasers for CBP implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A sequential exploratory mixed-methods study was conducted (Sep-Oct 2025) across three regions and Addis Ababa in Ethiopia. Data included 30 key informant interviews, 8 focus group discussions, and structured surveys with 344 facilities and 57 purchaser institutions. Qualitative data were analyzed thematically; quantitative data using descriptive statistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Provider readiness is hampered by a critical digital divide: while 98.8% use DHIS2 for aggregate reporting, only 40.4% have functional Electronic Medical Records (EMRs), and existing systems lack bundled-pricing capability. Problems​‍​‌‍​‍‌ to data integrity have been identified with coding sometimes being a nurse's task and the use of rule-out diagnoses. Major differences in development level are observed at regions level, with Addis Ababa showing advanced readiness while Somali (15.3%) and Southwest Ethiopia (46.5%) are lagging behind. Purchaser institutions appear to have a strong strategic design, however, they face critical operational gaps: 95% of them are dependent on spreadsheets for claims processing, they do not have automated fraud detection and they encounter a median claim rejection rate of 4%, which is mainly due to incomplete documentation (100%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe implementation of CBP in Ethiopia can be successful only if deep-rooted systemic constraints are addressed. The report recommends focusing on integrated digital infrastructure renovation, capacity building for data integrity on a nationwide scale, and aligning operational protocols to the UHC goals while making them more ​‍​‌‍​‍‌efficient.\u003c/p\u003e","manuscriptTitle":"Providers and purchasers Readiness for Case-Based Payment and its Systemic Constraints in Ethiopia: A Mixed-Methods Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 14:16:58","doi":"10.21203/rs.3.rs-8454222/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-01T18:50:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277437757292720628947402684144041612718","date":"2026-02-11T16:05:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267971510202537835590858319830774938140","date":"2026-02-09T14:01:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T11:44:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324811599504210367104117583268437930437","date":"2026-02-06T10:13:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T17:55:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T08:26:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-06T06:34:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-05T20:00:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-01-05T19:54:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"afbb8bf6-bb87-47f4-9b19-30446dd0dd1d","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T14:16:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 14:16:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8454222","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8454222","identity":"rs-8454222","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}