Category: HPF-P Framework

Holistic Prediction Framework for Pharmaceutical Portfolios

HPF-P in Practice: Deployment Lessons and Future Directions

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19417989  80stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (81 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

The Heuristic Prediction Framework for Pharma (HPF-P) has been developed across fourteen articles in this series, from its theoretical foundations through DRI calibration, DRL operationalization, multi-scenario stress testing, and regulatory compliance integration. This final article synthesizes deployment experience from pharmaceutical portfolio contexts and identifies the principal lessons le...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19417989 80stabilfr·wdophcgmx
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[s]Reviewed Sources57%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef57%○≥80% indexed in CrossRef
[i]Indexed81%✓≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access76%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,074✓Minimum 2,000 words for a full research article. Current: 2,074
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19417989
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]72%✓≥60% of references from 2025–2026. Current: 72%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
[g]Code✓✓Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (81 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)
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Regulatory Compliance Integration: Aligning DRL with Pharmaceutical Frameworks

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19414906  77stabilfr·wdophcgmx
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[t]Trusted65%○≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef60%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,299✓Minimum 2,000 words for a full research article. Current: 2,299
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19414906
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]76%✓≥60% of references from 2025–2026. Current: 76%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code✓✓Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (75 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

Pharmaceutical organizations face increasing pressure to align their internal decision-making processes with externally imposed regulatory frameworks — ICH quality guidelines, FDA 21 CFR Part 11, EMA guidance on AI, and the revised ICH GCP E6(R3). The HPF-P Framework's Decision Readiness Level (DRL) provides a structured five-stage readiness ladder, yet its integration with formal compliance re...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19414906 77stabilfr·wdophcgmx
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[s]Reviewed Sources35%○≥80% from editorially reviewed sources
[t]Trusted65%○≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef60%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,299✓Minimum 2,000 words for a full research article. Current: 2,299
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19414906
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]76%✓≥60% of references from 2025–2026. Current: 76%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code✓✓Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (75 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)
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Real-Time DRI Monitoring: Continuous Decision Readiness Assessment

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19412430  77stabilfr·wdophcgmx
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[s]Reviewed Sources16%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]2,009✓Minimum 2,000 words for a full research article. Current: 2,009
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]75%✓≥60% of references from 2025–2026. Current: 75%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code✓✓Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (76 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

Decision Readiness Index (DRI) is the core metric of the HPF-P framework — a scalar signal summarising the information completeness required before a pharmaceutical portfolio decision can be trusted. Yet a single DRI snapshot provides only a point-in-time view. This article investigates how continuous, real-time monitoring of DRI signals transforms static readiness scores into dynamic control l...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19412430 77stabilfr·wdophcgmx
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[s]Reviewed Sources16%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
[b]CrossRef16%○≥80% indexed in CrossRef
[i]Indexed79%○≥80% have metadata indexed
[l]Academic68%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]2,009✓Minimum 2,000 words for a full research article. Current: 2,009
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19412430
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]75%✓≥60% of references from 2025–2026. Current: 75%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code✓✓Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (76 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)
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Comparative Benchmarking: HPF-P vs Traditional Portfolio Methods

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19380196  69stabilfr·wdophcgmx
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[s]Reviewed Sources22%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
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[i]Indexed56%○≥80% have metadata indexed
[l]Academic72%○≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]1,734✗Minimum 2,000 words for a full research article. Current: 1,734
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19380196
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]67%✓≥60% of references from 2025–2026. Current: 67%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
[g]Code✓✓Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (3/4 × 10%)

This article presents a systematic comparative benchmarking of the Heuristic Prediction Framework for Pharmaceuticals (HPF-P) against three established portfolio management approaches: Markowitz mean-variance optimisation, Black-Litterman allocation, and naive machine-l[REDACTED]g selectors. Drawing on validated benchmarks from the HPF-P stress-testing study and supplemented by newly collected ...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19380196 69stabilfr·wdophcgmx
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[s]Reviewed Sources22%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef22%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic72%○≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]1,734✗Minimum 2,000 words for a full research article. Current: 1,734
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19380196
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]67%✓≥60% of references from 2025–2026. Current: 67%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
[g]Code✓✓Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (3/4 × 10%)
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Multi-Scenario Stress Testing for HPF-P Pharmaceutical Portfolios

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19273234  81stabilfr·wdophcgmx
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[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI72%○≥80% have a Digital Object Identifier
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[i]Indexed83%✓≥80% have metadata indexed
[l]Academic72%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,656✓Minimum 2,000 words for a full research article. Current: 2,656
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19273234
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]67%✓≥60% of references from 2025–2026. Current: 67%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code✓✓Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (83 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

Pharmaceutical portfolio management operates under persistent uncertainty from supply chain disruptions, regulatory shifts, and demand volatility. While the HPF-P framework provides Decision Readiness Index (DRI) and Decision Readiness Level (DRL) metrics for portfolio assessment, their behavior under extreme stress conditions remains uncharacterized. This article develops a multi-scenario stre...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19273234 81stabilfr·wdophcgmx
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[s]Reviewed Sources61%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI72%○≥80% have a Digital Object Identifier
[b]CrossRef61%○≥80% indexed in CrossRef
[i]Indexed83%✓≥80% have metadata indexed
[l]Academic72%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,656✓Minimum 2,000 words for a full research article. Current: 2,656
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19273234
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]67%✓≥60% of references from 2025–2026. Current: 67%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code✓✓Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (83 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)
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HPF-P Validation Studies: Empirical Benchmarking of Decision Readiness Across Pharmaceutical Contexts

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19129094  65stabilfr·wdophcgmx
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[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI60%○≥80% have a Digital Object Identifier
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[i]Indexed87%✓≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,508✓Minimum 2,000 words for a full research article. Current: 2,508
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19129094
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The Heuristic Prediction Framework for Pharma (HPF-P) provides a structured methodology for assessing decision readiness in pharmaceutical portfolio management through the Decision Readiness Index (DRI) and Decision Readiness Level (DRL). However, any theoretical framework requires rigorous empirical validation before it can claim operational utility. This article presents a comprehensive valid...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19129094 65stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI60%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed87%✓≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,508✓Minimum 2,000 words for a full research article. Current: 2,508
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19129094
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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DRI Calibration Methodology: Empirical Approaches to Threshold Optimization in Pharmaceutical Decision Systems

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19102033  55stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI48%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed52%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access86%✓≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,610✓Minimum 2,000 words for a full research article. Current: 2,610
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19102033
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]45%✗≥60% of references from 2025–2026. Current: 45%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (58 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Threshold calibration represents the bridge between theoretical decision indices and operational pharmaceutical portfolio management. The HPF-P framework defines DRI as a composite measure of data completeness, model confidence, and environmental stability — but the boundaries between "decide," "defer," and "escalate" zones require empirical determination. We present a three-stage calibration m...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19102033 55stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI48%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed52%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access86%✓≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,610✓Minimum 2,000 words for a full research article. Current: 2,610
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19102033
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]45%✗≥60% of references from 2025–2026. Current: 45%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (58 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Integrating DRI and DRL: A Unified Decision Readiness Assessment Protocol for HPF-P

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19071139  60stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources20%○≥80% from editorially reviewed sources
[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef15%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic55%○≥80% from journals/conferences/preprints
[f]Free Access70%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,247✓Minimum 2,000 words for a full research article. Current: 2,247
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19071139
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]48%✗≥60% of references from 2025–2026. Current: 48%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (66 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The Heuristic Prediction Framework for Pharma (HPF-P) introduced two complementary constructs for evaluating decision quality in AI-augmented pharmaceutical portfolio management: the Decision Readiness Index (DRI), which quantifies information sufficiency, and the Decision Readiness Level (DRL), which assesses organizational maturity. While each metric addresses a distinct dimension of readines...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19071139 60stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources20%○≥80% from editorially reviewed sources
[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef15%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic55%○≥80% from journals/conferences/preprints
[f]Free Access70%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,247✓Minimum 2,000 words for a full research article. Current: 2,247
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19071139
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]48%✗≥60% of references from 2025–2026. Current: 48%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (66 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Decision Readiness Level (DRL): Operationalizing Maturity Assessment for AI-Augmented Pharmaceutical Portfolio Management

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19059359  56stabilfr·wdophcgmx
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[t]Trusted56%○≥80% from verified, high-quality sources
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Score = Ref Trust (59 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The Heuristic Prediction Framework for Pharma (HPF-P) defines decision readiness through two complementary constructs: the Decision Readiness Index (DRI), which quantifies information sufficiency for a given decision context, and the Decision Readiness Level (DRL), which measures organizational maturity in applying AI-augmented decision processes. While previous work formalized DRI as a continu...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19059359 56stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted56%○≥80% from verified, high-quality sources
[a]DOI44%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed88%✓≥80% have metadata indexed
[l]Academic56%○≥80% from journals/conferences/preprints
[f]Free Access63%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,272✓Minimum 2,000 words for a full research article. Current: 2,272
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19059359
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]58%✗≥60% of references from 2025–2026. Current: 58%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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HPF-P Platform Architecture: From Theoretical Framework to Production System

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Framework Research
Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18855053  40stabilfr·wdophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]3,839✓Minimum 2,000 words for a full research article. Current: 3,839
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18855053
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

HPF-P transforms the abstract DRI/DRL framework into a concrete computational system that ingests real-world pharmaceutical data, computes decision readiness diagnostics, applies conditionally permitted optimisation strategies, and produces auditable portfolio recommendations. The platform targets commercial pharmaceutical portfolios — the inventory allocation and revenue optimisation decisions...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18855053 40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]3,839✓Minimum 2,000 words for a full research article. Current: 3,839
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18855053
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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